For many businesses, another corporate asset – data – has become a target in the search for a more profitable organization. But, the dynamics of data management are more chaotic than ever. Data is now collected and saved from every conceivable source – internet applications, front-office and back-office systems, trading networks, social media – and this complexity requires companies to have a sophisticated, deliberate process for managing this vital information. After all, data holds the key to sales, marketing, customer support, production and other initiatives. Without an accurate view of customers, products, materials, locations and assets, how can a company compete in today’s marketplace?

Because of these factors, the need for data management has never been higher. This paper will explore a new methodology for integrating data management principles into the organization. Through this proven lifecycle for data management, companies have the ability to create more accurate, integrated and controlled data to support every part of the business.

The Business Case for Data Management

In a 2009 Gartner survey, the research firm found that the average organization loses $8.2 million annually from poor-quality data. Further, of the 140 companies surveyed, 22% estimated their annual losses resulting from bad data at $20 million – and 4% put that figure at a staggering $100+ million.

For companies who are facing data management problems, the primary question seems to be, “Where do we start?” As with any corporate initiative, the best starting point is to set measurable business goals. After all, no one has ever managed, moved or standardized data for the sheer thrill of the task. There must be a business reason to conduct any data management initiative. At the highest level, there are three primary reasons why organizations perform any business function: to stay out of trouble, to make money or to spend less money. To translate that into more business-friendly terminology, these three reasons are:

• Governance, Risk and Compliance – Better data creates a more accurate view of the organization to help understand when the company is at risk from failing to meet regulatory requirements, complying with industry standards and other external and internal pressures.
• Business and revenue optimization – Better data makes the company more profitable by improving business processes that support every phase of the business.
• Cost control –Better data supports increased efficiency throughout the organization, driving down the costs of both direct (related to production) and indirect (personnel and other administrative) costs.

The key for data management professionals is to tie the data management efforts to each of these business objectives. The next few sections will discuss how data management technologies and processes map to these broader business objectives.

This informative demonstration will show how proven DataFlux technology, expertise and methodology can help you overcome your data migration challenges. By addressing every phase of the data migration process – from a single platform – you can create the foundation for a more effective data migration program.

Industry thought leader Jill Dyché joins DataFlux president and CEO Tony Fisher for an informative and engaging look at the challenges and benefits facing companies that tackle MDM today. Attendees will learn what to do and what not to do when implementing or expanding an MDM initiative.

Data quality and data management are critical for compliance to this new legislation. As organizations embrace transparency, they will be able to:

• Use payments to establish trends
• Household physician data for the purposes of creating a “physician master”
• Reduce costs and mitigate risk

Forward-looking organizations are solving these technology and business problems by adopting enterprise data management (EDM). Download this TDWI Checklist Report to learn more about this best practice for unifying diverse data management disciplines.

This paper from Ovum, a leading research firm in technology, telecommunications and other business sectors, argues against cutting into these projects too deeply. An economic downturn highlights the importance of accurate and up-to-date information to optimally drive business applications and decision-making. Hence, a rigorous data quality program is now a business imperative rather than a luxury. Implemented correctly, it not only gives organizations the agility to better ride out the current recession but also raises their competitiveness when the economy recovers.

There is no arguing that information technology (IT) is typically a cost center, in which many different types of operating costs are incurred, accumulated and offset against organizational revenues and profits. As a core component of IT, the staff, hardware, software and support dedicated to data management is often directly accountable for many of those costs. But although data management contributes to corporate expenses, there remain opportunities to apply best practices in data quality management as techniques that can reduce expenses across the board.

There are different aspects to the meaning of “cost reduction.” To some, it focuses solely on eliminating or reducing operational expenses. But reduced costs are often linked to increased efficiencies in day-to-day operations as well as improved performance for revenue-generating activities. In other words, within the scope of a performance-oriented organization, data quality management can be used to seek out operational efficiencies for cost reduction, leading to increased margins and, consequently, increased profits.

This paper will review aspects of cost reduction by examining some typical financial accounting expense categories. The paper then selects some specific examples and assesses their reliance on high-quality data. In turn, the paper looks at how data quality services can be applied in those examples to reduce expenses. Last, we reiterate the potential for applying data quality management as a way to manage and reduce organizational expenses.

Understanding Expenses

The objective of a program identifying operational efficiency may signify a “slash and burn” approach to reducing expenses. However, eliminating staff or services necessary to keep the business running will increase not only the workload for staff remaining after a layoff, but it also reduces the effectiveness in fulfilling those tasks and ultimately detracts from the employee experience. This leads to increased turnover and an overall reduction in organizational knowledge.

Reducing expenses is more about being smart in understanding where costs have exceeded reasonable levels and determining ways to identify and eliminate excessive costs. And this is where quality data comes in – if the source of waste can be attributed to situations in which successful business operations are negatively impacted by poor data quality, then (logic would suggest) data quality improvement will help in identifying ways to streamline processes and reduce costs. More importantly, the effects of applying these techniques during weaker economic times will train people to work smarter in the future, helping the organization improve competitiveness and agility during economic recovery and expansion.

The challenge for the data professional lies not in the knowledge of good data management techniques, but in understanding the financial lingua franca that describes how money is spent in order to run a business, usually encapsulated within the finance department’s chart of accounts. This chart lists the “channels” through which money flows into and out of the organization.

The drive for data unity creates many opportunities for data consolidation. Customer data integration projects, master product catalogs, security master projects and enterprise master patient indexes are all examples of technology-driven projects intended to resolve multiple data sets that contain similar information into a single view – all with the hope that a unified data asset will lead to improved business processes. The success of this data integration process hinges on the ability to determine when different data instances in the same (or across multiple) data sets refer to the same real-world entity. Searching through data sets for matching records that represent the same party or product is the key to the data consolidation process.

The two most interesting challenges for customer data integration are basically two sides of the same coin; the challenge is not just about determining when two records refer to the same real-world object, but it’s about knowing when they do not refer to the same real-world object. Yet without being able to make that clear connection or distinction, it would be difficult, if not impossible, to identify potential duplicate records within and across data sets.

The method used to find these connections is typically referred to as identity resolution. From a technology perspective, identity resolution is a collection of algorithms used to parse, standardize, normalize and compare data values. This can establish that two records refer to the same entity or to determine that they don’t. By feeding the set of records into the identity resolution process, we can determine that all of these records contain a reference to a unique entity. Beyond that, we can use data culled from all of the records to materialize a high-quality representation of each entity type. This process is used to resolve different entity representations and determine that they all refer to the same real-world entity.

The techniques used in this process are critical for any business applications that rely on customer or product data integration as part of a master data management (MDM) or data quality initiative. In this paper we explore the root cause of the “dual challenge” of identity resolution, examine how parsing and standardization contribute to the process, then review different ways that similarity scoring and approximate matching algorithms can help determine and resolve identical entities despite variant representations.

Between the years 1996 and 2005, the annual spending on pharmaceutical industry direct-to-consumer marketing, advertising and promotion to health care professionals rose from $11 billion to practically $30 billion. By 2005, the majority of that activity consisted of professional promotion and over $18 billion worth of free samples. For the pharmaceutical industry, this significant marketing investment is a double-edged sword – while it promotes awareness of drugs and devices to the health care provider community, payments by drug, device and medical supply companies to medical practitioners might be construed as influencing practitioner decisions. The risk lies in the potential for creating a conflict of interest on behalf of that same community of health care practitioners, leading to concerns about the objectivity of physicians and, ultimately, patient safety.

There has been a growing sentiment that establishing rules for physician payment transparency would reduce the impact of pharmaceutical marketing and promote awareness of appropriate prescription and drug safety. While many states already have laws requiring pharmaceutical and medical device manufacturers to publicly report gifts and payments made to physicians and other health care practitioners and providers, provisions for physician payment transparency were included in the Patient Protection and Affordable Care Act of 2009 (H.R. 3590, section 6002), which was signed into law on March 23, 2010.

The inclusion of these provisions in the health care reform legislation has consequences for the pharmaceutical and medical device industry with respect to the ability to capture, manage and organize data in order to generate reports to support compliance with the transparency reporting directives. This paper explores just a few of the data management requirements necessary to support compliance with the reporting requirements for physician payment transparency and disclosure. Using the text of the federal legislation as a starting point, the paper first seeks to understand how some of the key data concepts factor into compliance reporting.

Next, the paper discusses the complexity of the reporting requirements, challenges to establishing the proper business processes for documenting transfers of value, and the need for analytic reporting. Finally, after reviewing the dependence on high quality data, the paper suggests that ancillary benefits can be achieved as a byproduct of instituting best practices for data quality and data governance.

Warum Data Governance helfen kann

Data Governance-Programme gibt es in unterschiedlichen „Geschmacksrichtungen“. Einige konzentrieren sich auf die Unterstützung von Compliance, Sicherheit und den Datenzugang. Andere fokussieren sich darauf Datenqualität zu unterstützen. Weitere konzentrieren sich darauf Datenintegration zu ermöglichen, den Wert der Informationen zu steigern und Anstrengungen zu Datentransformation zu unterstützen. Data Governance-Programme können eng auf ein einziges Datenrepository, einen bestimmten Datensatz oder ein Geschäftsproblem beschränkt sein. Oder sie werfen mit dem Ziel eine Standardisierung, eine Überprüfbarkeit und eine zuverlässige Entscheidungsfindung im gesamten Unternehmen umzusetzen, ein weites Netz aus.

Unabhängig von der „Geschmacksrichtung“ widmen sich alle Programme den folgenden Aufgaben:

• Definition und Abstimmung von Policies, Standards und Regeln
• Etablieren von Entscheidungsbefugnissen
• Vergeben datenbezogener Verantwortungsbereiche
• Bereitstellen von Mechanismen zur Problemeskalation/-lösung
• Identifizieren von Interessensgruppen und deren Bedürfnissen
• Kommunikation mit allen datenbezogenen Interessensgruppen

Dies sind funktionsübergreifende Aufgaben, die von Verantwortlichen aus allen Unternehmensbereichen übernommen werden. Als Ergebnis entwickeln Data Governance-Programme und daran Beteiligte einzigartige Fähigkeiten:

• Sie werden sich der Lücken und Überlappungen im Management bewusst
• Sie erfahren mehr über Softwareprodukte und deren optimale Nutzung, um vielfältige Ziele zu erreichen
• Sie beobachten proaktive, reaktive und fortlaufende Prozesse
• Sie sehen, an welchen Stellen Geld ausgegeben wird, und oftmals erkennen sie auch, wo Kosten eingespart werden könnten

Es folgen sieben Nutzenversprechen für Data Governance. Wenn nur einer für Ihr Unternehmen zutrifft, werden Sie feststellen, dass Data Governance die eigenen Kosten nur durch die Vermeidung anderer Kosten amortisiert. Treffen mehrere Nutzenversprechen auf Ihre Situation zu, werden Sie vielleicht feststellen, dass Ihr Data Governance-Budget die cleverste Geldanlage ist, die Sie zurzeit tätigen.

Die gute Nachricht dazu: diese Hürde kann genommen werden, wenn man Daten als strategisches Gut behandelt und die richtigen Strategien und Technologien anwendet, die es Unternehmen ermöglichen, qualifizierte Geschäftsentscheidungen basierend auf vertrauenswürdigen Daten zu treffen.

Legt man den Schwerpunkt auf das unternehmensweite Datenmanagement kann dies beispiellose Einblicke in das Kaufverhalten der Kunden und betriebliche Abläufe eröffnen,während gleichzeitig neue Umsatzpotenziale aufgespürt, Risiken gemindert und Kosten eingedämmt werden können. Bessere Daten führen zu besseren Entscheidungen,was schlussendlich zu besseren Geschäften führt.

• Investitionen in Datenqualität zahlen sich aus. Ohne Datenqualität bleibt die Wertschöpfung aus der häufig kostspieligen Einführung von SAP-Systemen eingeschränkt.
• Datenqualität ist ein aktuelles Thema: In den meisten Fällen halten Anwender Daten für nur bedingt vertrauenswürdig. Ohne Vertrauen in Daten steht letztlich jedes IT-System in Frage.
• Trotz des deutlich artikulierten Verbesserungsbedarfes greifen nur knapp über 50 Prozent der Teilnehmer das Thema Datenqualität konkret auf.
• Zur Steigerung der Datenqualität werden primär in-house Fixes oder Scripts verwendet, nur in 36 Prozent aller Fälle werden Datenqualitätswerkzeuge verwendet.
• Finanz-, Material- und Produktdaten sind neben den Adressdaten die wichtigsten Daten im Unternehmen. Datenqualität ist keine adressdatenspezifische Domäne mehr.
• Die Einrichtung von Business Intelligence Competence Centern oder anderer organisatorischer Lösungen zur Stärkung der Zusammenarbeit von IT und Fachbereichen wird seit einigen Jahren in vielen Unternehmen forciert. Auch das Thema Datenqualität wird in 56 Prozent der Unternehmen abteilungsübergreifend angegangen.
• Es besteht ein deutlicher Bedarf zur Steigerung der Aufmerksamkeit für Data-Governance- Programme.

The Business

A Fortune 500 pharmaceutical company and a leading provider of medicines to fight cancer, cardiovascular disease and infectious diseases, with over a century of experience has grown to become a global health care leader, developing and providing innovative pharmaceuticals and other health care products.

The company faced a unique set of challenges. Not only does it operate in a heavily-regulated industry, but the company’s operations extend internationally – bringing it under multiple and widely varying sets of regulations. The company saw an opportunity to embrace these challenges and make data quality a central part of its operations, allowing it to work smarter to become more profitable.

The Challenge

The company is already accustomed to dealing with vast amounts of data, since it offers an impressive number of products, each produced from complex manufacturing processes that carry extremely detailed auditing requirements. Also, because the auditing requirements are tracked differently from country to country, the same item can be identified multiple times as different products.

The company’s interaction with its customers also has to conform to strict guidelines. As a company that prides itself on going above and beyond mandated requirements, it requires complete, accurate, and unified customer master records for day-to-day operations.

To help the company turn its data into a more valuable corporate asset, the organization sought to enhance their existing data quality and data management initiatives to better support master data and data governance programs.

The DataFlux Solution

The company chose DataFlux technology to extend data quality routines across the enterprise as web services. The unique graphical user interface gives business users control over data quality routines, allowing subject matter experts to apply their knowledge directly to business rules that are integrated into the data infrastructure. This strengthens auditing processes and provides more direct control over the integrity of key data elements.

The company also found that, beyond the technology, the DataFlux methodology of analyzing, improving and controlling data complimented its existing business processes. The in-depth DataFlux methodology encompasses people, processes and technology – allowing companies to make better business decisions and gain a competitive edge. The five technology building blocks of the DataFlux methodology are data profiling, data quality, data integration, data enrichment and data monitoring – steps which enable organization to understand, improve and maintain high-quality data.

The Results

The company found that DataFlux technology integrated easily with its existing infrastructure. Using the workflow creation tools of dfPower Studio, the company was soon able to launch a comprehensive project to create master records for customer information. DataFlux technology provided the ability to cleanse, standardize and match customer information across existing sources, helping the company to gain more value from this critical data.

Moreover, the company found that the vision offered by a more effective data quality program had the power to make them more competitive. High-quality data offered this organization the opportunity to manage costs more effectively and create an informational platform for greater innovation and excellence. The company saw DataFlux technology as an enabler of change throughout the organization, allowing data stewards to serve a key role in making data quality central to the company’s operations.

The Business

BCE is Canada’s largest communications company, providing a comprehensive and innovative suite of communication services to residential and business customers in Canada.
 
Under the Bell brand, the company’s services include local, long distance and wireless phone services, high-speed and wireless Internet access, IP-broadband services, direct-to-home satellite and VDSL television services.

The Challenge

As a premiere communications provider, Bell offers multiple services to its clients. Having the ability to know detailed information about a customer is essential to be able to offer the appropriate goods and services. Bell sought to combine its customer address information with details of Canadian dwellings.

Like many companies, Bell manages customer data in multiple applications. To improve customer service and support initiatives, Bell wanted to gain a unified view of the services already in use at a specific address. It also needed to verify that each address was, in fact, unique and distinct.

To achieve this unified view, the company used a third party vendor to combine internal customer profiles with multiple external sources to create a master list of Canadian dwellings. However, after examining more than 24 million records from this process, Bell found errors, duplications and variations in the data. Inaccurate and unreliable data hindered the company’s ability to offer new services to potential customers.

Bell wanted a flexible and efficient in-house solution that managed its match rules, so they invested in a DataFlux data management server license. In addition, Bell turned to Modern Analytics, a DataFlux partner and an industry-leading solution provider in data processing automation, business intelligence and customer analytics. After assessing the scope of the problem, Modern Analytics recommended a tailored solution – with DataFlux technology at the core – to provide a foundation for this customer data initiative.

The Solution

DataFlux technology enables business users to reconcile, cleanse and enrich internal address data, with localization and address enrichment capabilities that offer the ability to create complete and accurate address information for more than 240 countries around the world. In addition, DataFlux can enrich address data with geographic, demographic or other details.

“Our experience working with Modern Analytics and DataFlux greatly exceeded our expectations,” says Yves Lapierre, associate director of the customer data mart, Bell. “The team went beyond simply solving the problem; they gave us the information we needed to improve our business. We were expecting a company that could just help us properly deploy the technology, but Modern Analytics helped us understand what was required as well as preparing us for the future.”

The Results

With DataFlux technology providing a data management framework, Modern Analytics helped Bell create a more accurate master list of Canadian dwellings that can now be used for extensive product marketing campaigns. Using the advanced fuzzy-matching capabilities of DataFlux, Bell identified and eliminated more than five million duplicate records from its databases.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Data migration projects are critical to strategic initiatives yet the success rate across the industry is extremely low. A recent industry survey by Bloor Research discovered that only 16% of data migration projects are typically delivered on time and on budget. Severe delays or failure during the data migration project can have a major impact on strategic objectives. New business systems sit idle as they wait to be populated with data. Competitive advantage is lost and costs begin to soar.

One principle cause of this endemic failure within the data migration industry is the inability to adequately manage the quality of the data assets that require migration. The same Bloor Research report also found that only 10% of organizations surveyed admitted to using data quality tools during their data migration.

This white paper provides practical advice that will help the reader understand the pivotal role data quality technology must play in a data migration. Five distinct implementations of data quality technology are described in detail. Each one provides clear evidence and benefits for the need to adopt the right data quality approach in data migration projects.

Scope, Cost and Timeline Forecasting

The Risk

At the inception of any data migration there is a great deal of uncertainty.

• How many systems do we need to migrate?
• How many business objects will be in scope?
• What type of skilled resources is required?
• How many data quality issues will we face?
• How much time will the business grant to load the data?
• Will the target system be available as planned?

One of the first risks to impact most data migration projects is the failure to gather sufficient information to enable accurate forecasting for the scope, cost and duration of the project.

Without accurate forecasting the following conditions may arise:

• Funding expiring before the project completes because cost estimates are insufficient
• Delivery date not being met because the timelines are unrealistic and the scope ‘creeps’ during the project
• Lack of sufficient personnel at key phases of project because the scoping and resource estimates are poorly understood

• Increased confidence in decision making
• Added value from corporate data
• Improved data security and risk management
• More efficient business processes

Data quality management often concentrates on prevention – instituting inspection and monitoring for potential anomalies as a way of eliminating the source of introduction of erred data. However, there are certain situations in which the organization does not exercise administrative control over data that is used by business applications – such as data sourced from third-party data providers, data that is entered by external parties, or data that is generated by flawed automated processes.

In these cases, it is difficult, if not impossible, to prevent errors from entering the environment. In order to maintain high quality data, the data management practitioner may need to rely on data cleansing techniques. Parsing and standardization are a combination of techniques used to match data values against known patterns to help map values to standard formats, identify errors and potentially correct them, and ultimately normalize data values so that they can be more effectively used within business processes.

In this paper we look at common data error paradigms – descriptions, examples, and ways that data set quality is impacted by those common root causes for introducing errors. We then consider aspects of metadata management that help to limit the scope of introduced errors, and then how that metadata is used by parsing and standardization utilities to normalize data. Last, we’ll explore how parsing and standardization techniques can be integrated into the application framework to help identify potential errors as data enters the environment as a “data quality firewall.”

The Business

Česká pojišťovna is an all-purpose insurance company providing both individual life and non-life insurance, along with insurance for small, medium and large clients in industrial and business segments. Česká pojišťovna (ČP), the leader in the Czech insurance market, belongs to the Generali PPF Holding, which serves 30.9 percent of the market in terms of the volume. ČP is the largest insurance company in the country with more than nine million active policies.

The Challenge

Thanks to its long history, ČP has an extensive collection of client data, gathered over many years and systems (going back to punch cards). One of ČP’s oldest operational systems contains life insurance data for almost ten million policies and more than ten million claims. Some of this data suffered from inconsistencies, duplications and other data quality problems, such as:

• First names and surnames with missing diacritical marks
• National ID numbers with an invalid suffix – usually 4 zeros
• National ID numbers that didn’t match the client’s gender
• Name, surname and title(s) that were erroneously typed in a single field, instead of their respective fields
• Obsolete post codes, parts of addresses sometimes missing
• Heavily abbreviated names, surnames and addresses
• Miscellaneous remarks not stored in designated fields

The poor data quality had an adverse effect on other systems within ČP. It was difficult to effectively cleanse and de-duplicate data after it had been transferred into a central client database. Also, information on policies didn’t always match information on corresponding claims. Incorrect information on age or gender could have led to erroneous insurance premium calculation. Moreover, the data inconsistency made it virtually impossible to move data from the existing operating system into a new, modern system.

The Solution

ČP selected the DataFlux solution as part of a larger SAS technology offering to improve the quality of its enterprise data. The project consisted of data cleansing and de-duplication of ČP’s main client database. In order to prevent the data distortion in the future, ČP will establish unique and custom new business rules.

The Results

Thanks to DataFlux, ČP cleansed data in its oldest operational system over a four-month period. The data quality initiative achieved better than a 90-percent success rate (defined as the ratio of cleansed or verified records to the total number of records). The database held more than 20 million records in two datasets: policies and claims. Due to the fact that the data was historical and static, the main focus was not on the performance of the data quality tool, but on the quality of the new information.

ČP is pleased with the outcome of the project. “The collaboration with SAS was excellent. The project ran smoothly and the results are impressive,” said Štepán Cábelka, head of data quality, Česká pojišťovna. “Data – the most valuable asset of our company – has been improved. The project was an important milestone on the road to consistent, error-free and reliable information.”

You will learn:

• Business and technology drivers for operational data integration
• How requirements for tools, techniques, and teams vary between analytic and operational data integration
• Specialized technology requirements for operational data integration, including service oriented architecture, software as a service, data exchange standards, exception processing, and cross-functional collaboration
• Staffing, funding, and organizational approaches that accommodate both analytic and operational data integration

This on-demand webcast features industry thought leaders Robert Abate of EMC Consulting and Daniel Teachey from DataFlux. They discuss how a unified approach to data governance helps companies make better, faster decisions based on accurate and trusted data.

The Business

The CPP Group Plc (CPP) is an international marketing services business, offering end-to-end bespoke customer management solutions to multi-sector business partners. With a 25-year history and a customer base that spans Europe, North America and Asia, CPP offers a portfolio of life assistance insurance products which offer protection for everyday items such as mobile phones, plastic bank cards and identities. CPP distributes its products directly through a robust channel of partners which include major banks and brands.

The Challenge

CPP holds data relating to over 10 million customers in multiple markets as well as over 280 partner organisations and a vast number of individual policies. The company relies on two key systems for managing its policies. As a result, they were unable to provide a consistent, single customer view across the company. In addition, CPP’s customer marketing data management was handled by an outsourced partner.

Key Objectives of CPP’s Data Management Initiative

1. Create a single, consolidated master data hub which would supply a single version of the truth throughout the organisation to improve customer service and business intelligence.
2. Reduce the costs associated with CPP’s outsourced data management service.
3. Develop a clear set of policies for enforcing data governance across the organisation.

The Solution

CPP views its data as a strategic asset and selected a phased approach to MDM powered by DataFlux technology. The first step was to implement a data quality program to examine and align disparate data. To further improve efficiencies and create a more effective organisation, the company deployed DataFlux qMDM to underpin its unified data environment and power an MDM initiative that would drive value from corporate data by building a more consistent, unified view of enterprise information.

The Results

Through integration with CPP’s customer relationship management (CRM) and operational systems, as well as its analytical data marts, DataFlux qMDM is providing a unified, trusted record for use by the business. CPP now has the ability to draw on significantly higher quality and more trustworthy data.

“We decided to make the move to DataFlux qMDM to improve both the operational and analytical value of our data,” said Charles Blyth, head of business intelligence at CPP. “In just ten months we have completed a master data repository migration, which has allowed us to use trustworthy data to power our marketing and service programs. By bringing our data management in-house we have seen significant cost savings and greatly improved the business value of our data.”

DataFlux qMDM allows CPP to improve its interactions with retail partners and increase the effectiveness of its marketing and customer retention initiatives. Additional program results include:

• Single customer view: CPP deployed the DataFlux qMDM platform as a central master data hub that provides a single, consolidated customer view across its entire organisation. This view draws on the best available data from both of CPP’s policy systems and is enhanced by batch data quality routines to enable accurate customer information. The DataFlux technology has helped CPP transition from monthly customer updates to daily updates, providing the ability to make more accurate decisions faster.
• Single policy view: A single policy view has been achieved with daily updates supporting management information required to process policies. The MDM hub also forms the basis of CPP’s analytical data marts, which are essential to the company’s business analysis and customer retention objectives.
• Data governance: DataFlux technology has been applied to extract, analyse and cleanse core information relating to payment methods, business partners and products. Through reusable business rules, CPP can identify potential data quality errors and automatically fix problems at the source. To accompany this enhanced functionality, CPP has implemented new business processes to document and review any changes to its data quality business rules.
• Improved efficiency and cost reduction: CPP has generated annual cost savings of over £500,000 by bringing its data management in-house. Over 50% of existing data management processes have been migrated into automated linear processes running within the DataFlux platform. In addition, CPP has also identified additional annual revenue savings by using DataFlux technology to identify revenue leakage.

The Business

A leading home appliance retailer has full-line and specialty stores across the United States and Canada along with a large offering of tools, lawn and garden, home electronics and automotive repair and maintenance products. The company manages over 120 million customer contacts, originated through phone and web interactions. These contacts include requests for services as diverse as delivery, installation, home improvement and ordering replacement parts.

The Challenge

As a company with an extensive reach, tremendous inventory and an ongoing relationship with millions of customers, it produces and holds large amounts of highly specialized, highly diverse data.

The company maintains a database that includes product data for every part for sale through its stores, catalog and website, including frequently updated information such as pricing and availability. This incremental database, from which data is never purged, contains records for over seven million parts and grows daily. A separate database, which in itself is in excess of 50 million rows of data, maintains all of the product model data and related parts.

The parts and model data were originally intended for internal use only, and therefore were created with no set formatting or standards. The data was entered from multiple sources within and outside the company, with no standardization at the point of entry.

After more than 20 years of unstructured, unmanaged data entry into these databases, the data was riddled with inconsistencies, inaccuracies, misspellings and unrecognizable abbreviations, and only a minimum of data was truly identifiable or available.

The company sought to cleanse this data and use it to fuel an online customer resource that allows users to search for a model number or part number. The resource provides a listing of subcomponents and replacement parts for a particular model, as well as alternative parts if the original part has been discontinued. To successfully make the data customer-facing, the retailer needed to transform the data into high-quality and accurate information.

The DataFlux Solution

DataFlux Data Management Studio provides sophisticated data profiling and data matching technologies driven from an intuitive interface. Advanced DataFlux fuzzy-matching technology can successfully match incomplete, misspelled and inconsistent information to create a standard, unified and accurate record.

DataFlux allows users to easily create customized data matching rules, and then extend those rules across the enterprise – in batch or real time. The company used DataFlux technology to scan the millions of records in its databases, successfully identify related records and transform bad data into useful information.

The Results

With DataFlux, an initiative that many within the company had thought highly unfeasible, if not downright impossible, became a reality.

The company used DataFlux technology to effectively cleanse its databases of 20 years worth of bad data. The company standardized all part descriptions, accurately combined model and parts descriptions, and eliminated all abbreviations. Misspellings were also corrected and description formats standardized.

Furthermore, by creating an actual ongoing, monitored data governance program, the company gained the ability to define attributes at the part level, parsing attributes such as color, shape and size from the model descriptions to provide more granularity when searching the site. The data was enhanced with indicators that flagged data when an accessory or additional part or product was required. A local store availability indicator was also added, providing more value to the system.

In just 12 months, with DataFlux at the foundation of its data initiative, the company transformed two decades worth of disparate data into more than seven million records that were both accurate and useful for its customers.

The Business

A Medicare and Medicaid contractor processes payments and related information for services received from the entire spectrum of health care facilities, including hospitals, skilled nursing facilities, physicians, laboratories and suppliers. This company has been administering Medicare contracts in multiple U.S. states since the program began in the 1960s.

The Challenge

The company deals with massive amounts of data pertaining to Medicare beneficiaries and Medicaid recipients, as well as hundreds of thousands of individual files from doctors, hospitals and clinics. Fraudulent activity within the Medicare and Medicaid systems costs Americans an estimated $60 billion every year. As a processor of Medicare and Medicaid claims, the company sought to detect and prevent fraud – saving itself, its clients and taxpayers fraudulent claims expenses.

Detecting insurance fraud requires a complete picture of individuals and transactions occurring in isolated data silos. Transactions in one data silo can seem perfectly legitimate, but there may be information in another source that, when the two are combined, reveals the fraudulent nature of the transactions. It’s only when the data can be seen in its larger context – from a more comprehensive perspective – that patterns of fraud emerge. The company needed a system that would allow it to gain this perspective and automatically make connections across segmented groups of data. To do this, they turned to DataFlux.

The DataFlux Solution

DataFlux provides a single, unified platform for data management. The DataFlux Data Management Platform allows users to build corrective routines for problematic data, perform data matching and address verification, and extend those rules across the enterprise in batch or real time – all from a single interface. This approach gives business users improved control over enterprise data quality, allows them to deliver reliable, trusted data across the enterprise and lets them make informed business decisions based on accurate data.

The company used DataFlux technology to form a complete picture of the scale and nature of its data quality issues, profile its data to detect larger patterns and effectively act on those discoveries.

The Results

To form complete pictures of individuals and suspicious transactions, the company utilized DataFlux technology to match diverse data from disparate databases and immediately flag any dubious transaction. For example, if a transaction associated with a particular Social Security number occurred at a clinic in Florida at the same time a transaction associated with the same Social Security number occurred at a hospital in New Jersey, the software can flag the transaction as a possible case of identity theft.

Notification of suspicious transactions allows the company to immediately shut off that avenue of fraud, contact the involved parties and turn the evidence over to law enforcement. The company estimates that with DataFlux, it has already been able to prevent over $270 million dollars worth of fraud.

The Business

A leading national bank provides comprehensive financial services to consumers, small and medium-sized businesses and large corporations. The company has more than 2.5 million clients and over $100 billion in assets.

The Challenge

The bank was launching a major initiative with the goal of having a single, unified view of its customers. The bank sought to have a single key identifier for each of its customers, which could be used to draw on the existing information within the bank’s data warehouses. The result would be a unified master record containing accurate information about each client – accessible from any point of contact.

An outside vendor was engaged to help with this initiative. This company’s software was deployed, and the bank began attempting to fulfill its goals. However, it became clear that the output data was simply not useable. The bank began to see that there were too many data quality issues within its source data to create a unified record. After three years, the bank had to abandon its efforts and seek another solution – one that would allow them to profile their data and produce high-quality data as the master records were created. To achieve these objectives, the company turned to DataFlux.

The DataFlux Solution

The company chose DataFlux technology to meet its needs. DataFlux allows business users to easily analyze, improve and control data through an intuitive graphical user interface. With DataFlux technology, users can easily construct data quality rules and then enforce them in batch or real time across the enterprise with the DataFlux Integration Server.

The bank took advantage of DataFlux data profiling capabilities to understand DataFlux data profiling capabilities, allowing them to understand the exact scope and nature of their data quality issues. With this understanding, the company could then use DataFlux’s award-winning technology to cleanse, standardize and de-duplicate customer records, as well as enhance customer data through address verification and standardization.

DataFlux was brought in for a demonstration and within three days, the technology had profiled, de-duplicated and cleansed all three trial data sets that the bank had set forth.

The Results

The power of DataFlux technology, combined with the ease-of-use of the intuitive interface, allowed the company to rapidly advance towards its goals. Profiling the customer data in the warehouses was accomplished in three months by two interns. In a single summer the bank gained more knowledge on the integrity of its data than it had in the three previous years.

The data management team has become an enthusiastic advocate of the benefits of data profiling throughout the bank’s operations. As the organization continues toward its goal of have a single, unified view of its customer, the company is convinced that without high-quality data, powered by the DataFlux platform, the goal would be unreachable.

The Business

An independently-owned healthcare device provider develops, manufactures and markets healthcare products that are sold by health care suppliers, retailers and distributors throughout the world. A global company with operations in nearly 100 countries, the company is a renowned provider of specialty healthcare devices – and its global reach means that data management is also a global concern.

The Challenge

The company ships many of its products directly to customers and maintains a sizeable customer relationship management (CRM) system to handle these orders. Employees manually enter hundreds of new entries into this system daily and, over the years, an inability to match new and existing records created a significant amount of duplicate data. These records had to be manually cleansed for duplicates before each mailing campaign, representing a significant investment of time and effort.

Furthermore, because the system relied on manual entry, the overall quality of its address data was poor. The company lost tens of thousands of dollars per year in returned shipping costs due to incorrect or incomplete address information. The opportunity cost of the wrong marketing message reaching the wrong recipient provided an even greater barrier for the company’s sales efforts.

Seeking to eliminate these problems, the company evaluated several solutions, searching for a powerful and scalable data standardization and address verification solution that would integrate with its existing systems. Because both line-of-business and IT staff would work on the project, the company also needed an intuitive tool to design, develop and maintain rules to address duplicate or non-standard data.

The DataFlux Solution

The company chose DataFlux technology to analyze the data within its CRM system, weeding out duplicates and verifying address information. After this initial effort, the company would transfer into a monitoring mode, providing ongoing control over new data entering the system.

DataFlux gives business users control over complex data quality initiatives, allowing them to translate their knowledge directly into business rules to manage enterprise data, all from a single, easy-to-use interface. Business and IT staff could work on the program jointly; business users analyzed the data and created the data management requirements through graphically designed business rules, while IT staff would optimize and install these rules within the IT infrastructure.

The Results

DataFlux technology allowed the company to address data quality issues within the CRM system, ultimately eliminating over 10,000 duplicate mailing records. Furthermore, this manufacturer now uses DataFlux to verify that new customer details are unique as they enter its databases, preventing duplicates from reaching core business systems. With new entries entering the system daily, this preventative measure translated into a significant reduction in the time required to fix data quality problems before customer communications.

One of the largest benefits to the company has been eliminating the need to de-duplicate customer records for targeted marketing campaigns and to support other customer-facing support initiatives. With as many as 200 campaigns a year, adding address verification to its processes helped the company optimize personnel resources and reduce marketing costs.

The Business

A leading integrator of IT environments, infrastructure and software offers solutions on virtually every technology platform. To support global operations, this organization depends upon its data residing in multiple databases and in multiple environments. This method of data silos supports individual geographies, but it also creates multiple views of customers, products and other data assets.

The Challenge

The company knew that it had a persistent problem with poor-quality data throughout the enterprise, with numerous duplicate, incomplete or inaccurate customer records in its systems. The company had a particular issue around the customer address data in its systems. As a global company, significant portions of this company’s customer data had been entered into a Siebel CRM application by individuals unfamiliar with local postal standards – or into systems ill-equipped to deal with those standards. As a result, staff faced a persistent problem of incomplete or invalid contact information.

The company’s IT department routinely addressed data quality issues raised by individuals throughout the organization. The individual complaints showed several trends that indicated that a more programmatic approach to data quality could solve the customer data issues that were inhibiting solid customer relationships. The company decided to implement an enterprisewide data quality initiative to cleanse the data and bring it up to company standards.

Seeking a robust solution that would allow data quality routines to manage data within its Siebel environment, the company thoroughly evaluated multiple data quality vendors and selected DataFlux.

The DataFlux Solution

DataFlux offers an industry-leading technology platform that encompasses every facet of the data management process. Through its intuitive interface, DataFlux gives business users powerful data improvement capabilities and complete control over data quality, including extensive pre-built standardization libraries.

To address the company’s address standardization requirements, DataFlux provided capabilities to standardize addresses in more than 240 countries. DataFlux address verification solutions are certified for CASS (USA), SERP (Canada), AMAS (Australia) and other postal conventions. Additionally, DataFlux provides full-featured, localized versions of the rules, grammars and phonetics that serve as the foundation for parsing, standardizing and verifying a wide range of international data.

Importantly, DataFlux supported a data quality program that was not a single project but an ongoing data management initiative. Rules created to initially cleanse data became part of the Siebel infrastructure, allowing the system to check for duplicates using non-obvious (or fuzzy) matches between records. The system also checked for postal standards in real time, helping to catch non-standard data before it could cause a problem with customer service and support.

The Results

The company saw an immediate improvement in its customer data. Using DataFlux, staff identified and eliminated more than one million duplicate customer records. The improved data quality resulted in increased usefulness of the data across the enterprise – the company standardized address data in accordance with local standards worldwide, severely reducing issues associated with incorrect customer addresses.

Furthermore, the company extended these data quality routines as services throughout the enterprise, allowing users worldwide to benefit from the improved data quality. Data quality became part of the fabric of the IT environment, pushing data standardization and normalization as far upstream as possible. These routines helped the company achieve initial success and will help the company build towards an enterprisewide master data management (MDM) program.

The Business

A leading independent natural gas and oil exploration and production company with onshore operations in the United States and Canada is one of North America’s largest processors of natural gas liquids. The company has IT systems that support both regional and centralized corporate structures.

The Challenge

As an energy provider, the company watches two essential data points on a daily basis – the number of active wells and the amount of production per well. Having timely, accurate and reliable data on these business aspects was essential to the company’s day-to-day operations and its strategic planning for the future.

To provide easy, centralized access to this information, the company constructed an operational data store. This store assembled data from multiple systems to provide consistent reporting from a single system. With the operational data store, analysts could access regularly updated data and produce reports on daily, weekly and monthly schedules.

While the data store was a success in improving access to the data, the company found it also provided faster access to bad data that infiltrated its databases. Analysts soon found themselves having to drill through reports that included overwhelming amounts of inaccurate data, such as wells listed as “producing” that had actually been closed for a decade – or in some cases had never existed at all.

To deliver high-quality data to the operational data store, the company initially explored rebuilding the entire data structure, but the cost and time required made this unfeasible. Instead, the company decided to focus on improving data quality at the source, which would allow for a dramatic increase in data integrity within the target data store.

Deciding on an approach to purge the bad data from its systems, improve the quality of its data and apply its existing auditing rules directly to the data, the company turned to DataFlux.

The DataFlux Solution

DataFlux offered this energy company an industry-leading data management technology that allowed it to cleanse, correct and enhance well data kept in the data store – and create an accurate view of that data.

DataFlux offers a single, integrated platform for end-to-end data quality and data integration projects as well as managing critical aspects of enterprisewide data management initiatives. The oil and gas company used DataFlux to create a single set of rules that governed the quality of information in the source systems.

The Results

DataFlux allowed the company to integrate data quality into its existing enterprise data warehousing infrastructure. The technology gave business and IT users the ability to coordinate, creating a more rapid ROI for an emerging data governance effort. The ability to show immediate success enabled the data governance group to validate their efforts – and get funding for an expanded initiative.

Beyond the technology, DataFlux also provided the company with the ability to take its existing auditing procedures and translate them into rules to govern its data on an ongoing basis. In this way, the company could directly fix the business issues created by inaccurate data. Even if issues could not be immediately corrected, analysts at least knew that the data was bad – and could fix these records manually if necessary.

DataFlux provided immediate help as the company sought to correct the existing data issues. When the system first went live, analysts saw more than 500 errors a day in their reports. Within a few months, these same reports were showing less than a dozen errors a day. The company moved into a proactive mode for data management, finding and eliminating data management problems upstream – before they could cause business problems.

Correcting these errors has had a real business impact, boosting efficiency in well production by allowing analysts to view better information on what wells are producing. Better data allowed the company to more accurately focus resources and make better business decisions. For an industry where everything relies on production and output, any improvement in this cycle had a positive, lasting effect on the company.

The Business

A leader in the gaming and destination resort industry operates casino gaming, resort and entertainment facilities across the United States and South America. As the company grew through expansion and acquisition, it saw a need for an enterprisewide data governance and customer data integration (CDI) program.

The Challenge

Like any resort and entertainment company, this company faced several data quality issues, all of which combined to prevent it from having a consistent, accurate view of its customers. Previously, each of its properties maintained customer data in separate systems, often within multiple data repositories. Customers would often have unique – and often duplicate – records in the resort’s gaming system, hotel reservation repository and point-of-sale systems.

The company also faced a series of unique regulatory challenges. Since gambling is regulated at both the state and federal levels – and because the company operates in a number of different states and countries – it has multiple sets of regulations to follow. The cost of non-compliance in the industry is also particularly high; infractions can result in heavy fines or even the closure of properties.

Ultimately, the company sought to integrate and household its customer data and create an integrated, 360-degree view of its customers to provide more insight into its customer base. With a single, accurate view of its customers across its systems, the company could understand what was drawing its customers to its resorts, what it could do to more effectively meet customer demands, and how it could increase customer loyalty.

To address these issues, the company sought to integrate its customer data into data warehouses and brought in a top data analytics and data integration consulting firm to assist. When it saw the scope and the nature of the problem, the consulting firm recommended DataFlux.

The DataFlux Solution

The company selected DataFlux technology to cleanse, de-duplicate and integrate its customer data. The intuitive DataFlux user interface allowed both business and IT users to easily analyze, improve and control customer data.

With DataFlux technology, the company established de-duplication and householding efforts and began migrating data into the new warehouses. The technology also made it easy for the company to establish data quality as part of its overall business practices and create a data governance program to manage cleansing and de-duplicating the data.

The Results

With DataFlux in place, the company has made significant strides in its customer data integration efforts. Going through its holdings system by system, the organization has begun to build an integrated, 360-degree view of its customers.

DataFlux has also helped the company to cleanse data of duplicates before it enters its systems. The company used DataFlux technology to examine one customer list that the company was required by regulatory mandate to integrate with its systems. DataFlux helped it prevent tens of thousands of duplicate records being introduced into its systems from this single list.

The Business

The Miami Herald Media Company publishes The Miami Herald and El Nuevo Herald newspapers as well as MiamiHerald.com, elNuevoHerald.com, Miami.com and MomsMiami.com. Its wide reach includes direct marketing initiatives such as sub-ZIP code zoning, database marketing, printing and distribution, direct mail, email/sms marketing and events such as Americas Conference and Herald Hunt.

The Challenge

The Miami Herald Media Company’s direct mail operation sends out over half a million pieces of mail each week. Operating with a business model that was completely dependent upon accurate and reliable data, the organization knew the need for accurate and reliable data and had been using a data quality solution for years. Unfortunately, this data quality solution did not consistently produce the highest level of data coding.

With this knowledge, the Miami Herald Media Company sought to replace its existing data quality solution with a better, more accurate and more cost effective solution. To achieve this, the company turned to DataFlux.

The DataFlux Solution

The company selected the industry-leading DataFlux data quality and data integration platform to replace the incumbent data quality solution. DataFlux products offer a cutting-edge data quality and data integration platform built on best practices and real-world implementations, helping organizations understand and improve the quality of enterprise data.

DataFlux provides full-featured address verification for over 240 countries and territories worldwide. Beyond essential address verification, DataFlux offers value-added content with regional address, phone number and demographic data enhancements, as well as rooftop-level geocoding.

The Results

DataFlux gave the Miami Herald Media Company the flexibility it needed to process data from multiple sources, de-duplicate, cleanse and improve its data records in order to transform the data into trusted information that was usable for the company and marketable to its clients. The company was able to enhance its geocoding by consistently delivering the highest level of accuracy, rooftop-level geocoding.

The Miami Herald Media Company also eliminated another issue it had encountered, oversized cluster groups from the matching process. The DataFlux technology not only improved the accuracy of its data, but also improved the reporting of anomalous, or unmatchable, data. This change allowed the company to improve its overall data quality.

"DataFlux gave us the flexibility, connectivity and accuracy to improve our data quality and meet the needs of our clients," says Alfred Hampton, database/systems administrator with the Miami Herald Media Company.

The Company

PPL is a London-based music industry organisation that licences recorded music and music videos produced by record labels in the UK for use in public performances, broadcast and new media. PPL allows radio and television stations, other broadcasters and internet radio stations to legally use sound recordings and music videos in their transmissions by distributing airplay payments to the respective record company and performers. PPL’s public performance licences also allow thousands of clubs, shops, pubs, restaurants, bars and other music users to play sound recordings and music videos in public. In addition, PPL has multiple contracts with international licensing organisations to collect overseas income. In 2009, PPL collected more than £129 million and provided crucial revenues for those creative individuals and companies.

The Challenge

PPL forms a data hub for over 5,750 record companies and 45,000 performers in the UK. The PPL repertoire database contains over four million recordings, which grows weekly by an average of 6,500 new, electronically submitted recordings. In addition, through its 48 contracts with similar organisations around the world, it interacts with a further 4,000 record companies and 21,000 performers in many different languages and formats.

The amount of royalties PPL remits each year is increasing in response to a growing member community, an increase in material to licence and an expanding number of delivery mechanisms via social media outlets. To correctly assign and complete payments, PPL must start with accurate and standardised data on this network of members.

The Solution

Historically, PPL built its own data management tools in-house. However, to keep pace with the continued expansion of the market, PPL turned to the technology expertise of DataFlux and the strategic consulting of Deloitte to build a master data management (MDM) architecture that would provide the long-term infrastructure to meet the growing needs of its constituents.

Initially, PPL implemented an enterprisewide data quality program to profile, cleanse and align the data it managed. DataFlux technology generated easy-to-understand dashboards that helped staff measure the quality of their data against PPL’s business rules.

These rules enabled PPL to standardise key text fields (such as title and main artist), tailoring DataFlux’s dictionary to music industry-specific needs. This streamlined the ability to cluster and resolve duplicate data – a key requirement for PPL, since the same recordings are often legitimately submitted from many sources. Also, a series of "trust rules" enabled the creation of "golden recordings," blending the best information from each source to create a master data store.

After integrating the data management system into the new PPL database, the company had a data quality "gateway" to protect against poor-quality data. The overall architecture is ground-breaking for this industry, managing both original and master versions of all data received as well as identifying complex relationships between recordings, rights holders, performers and related stakeholders.

The Results

The initiative took nine months from vendor selection to project completion. The teams from Deloitte and DataFlux worked closely with the PPL IT team to develop an MDM architecture designed to increase the frequency and accuracy of payments made by PPL.

"It was essential for us to develop a platform that integrates well with our existing systems and makes it easy to manage, configure and customise into the future," said Peter Leathem, executive director, PPL. "The platform we now have in place provides an advanced way to store our mission-critical information and manage our business."

• Improved data accuracy: PPL used the industry-leading DataFlux technology to validate, standardise, cluster and integrate data on more than 6.5 million recordings, including a major migration and complete catalogue resubmissions of eight major record companies’ entire UK databases. Each transaction can now be accurately traced through its lifecycle by PPL.
• Data governance: The MDM platform allows PPL to apply key business rules as data enters the system, providing transparency and enabling the timely processing of payments for submitted recordings.
• Centralized repository for business rules: A single, re-usable set of rules now powers PPL’s data management system. For example, the same standardisation rules used to build the recordings database will also be applied to usage data received from licensees to improve automatic match rates.
• Infrastructure built for the future: As the number of new media music outlets presents more diverse ways of distributing music, PPL now has an infrastructure that can incorporate these new sources.

Former Intel CEO Andy Grove once coined the phrase, “Technology happens.” As true as Grove’s pat aphorism has become, it’s not always good news. Twenty years ago, no one ever got fired for buying IBM. In the heyday of customer relationship management (CRM), companies bought first and asked questions later. Nowadays, business intelligence, data quality and master data management (MDM) initiatives have opened companies’ eyes to the fact that—absent sustained processes and data-centric expertise—the tools themselves rarely solve the problem.

A technology’s success or failure is not proportional to the existence of an executive sponsor, solid requirements, or even a deliberately-crafted business case. Instead it depends on the existence of rigorous processes and dedicated skills to implement and maintain it.

When it comes to the aforementioned solutions, data stewardship is seen as the glue that binds heterogeneous information—ensuring common, meaningful data across applications and systems. It seems obvious that data stewardship is important to the business. However, is it really a critical success factor?

The Problem with Data Stewardship

When clients talk to us about introducing the role of data steward in their organizations, the need for data stewardship often belies broader cultural and ownership issues. Here’s a synopsis of a conversation with the Director of Marketing Analytics at a consumer goods firm that illustrates this point.

Baseline: “So can you describe the problems that are driving the need for data stewardship?”

Director: “Well, it’s pretty clear we’re at the point now where we need someone to own the data.”

Baseline: “And what data is that?”

Director: “All the marketing data.”

Baseline: “What are the boundaries with the data?”

Director: “Boundaries? All customers, all products and all financial data. Oh, and the stores, too, so location data. And five years of history.”

Baseline: “Hmmm. You mention ownership. If you had a single data owner, how would that help?”

Director: “He’d own the data so he could tell us what to do with it and the processes to put in place. He’d also define it all for us, and tell us where to keep it. We have no one to do that now.”

Baseline: “And how do you see this new resource spending his time?”

Director: “Spending his time?”

Baseline: “Yes. Tactically.”

Director: “We’d need you guys to tell us that.”

Baseline: “Okay. But would there be an initial project or data set that the data steward could focus on? So that we can design the role and the accompanying processes to prove value?”

Director: “Yes. The project would be to socialize the understanding of data stewardship.”

In chaotic environments with highly distributed systems and projects, data stewardship promises a central point of contact for increasingly complex and growing data volumes. In companies where roles are vague, data stewardship assigns decision rights around data – enforcing accountability. In very political environments, data stewardship holds the promise of more turf ownership and more visibility.

In these cases, data stewards are often assigned hastily without much vetting or focus, and are just as quickly rendered inert by organizational maneuvering and land-grabbing. Whether they exist in the business or in IT, data stewards become roving linebackers, going from meeting to meeting with no real authority to resolve data quality problems or enhance metadata management capabilities. Many data stewards are rendered mere figureheads in their organizations, with few constituents understanding their responsibilities. The term “data steward” is eventually met with shrugs and rolled eyes and is all too often marginalized as just another indistinct IT function.

Indeed, the promise of data stewardship is the inherent problem with data stewardship: it’s not specific enough. In fact, the well-worn industry precepts for data stewardship have been largely to blame for the increasing disillusionment and confusion about the role. You've probably heard some of them:

• Data stewardship is a business function, not an IT function
• Data stewardship requires enterprise data governance
• Data stewards define and maintain data
• Data stewards are subject area experts
• Everybody is a data steward

But we have clients where none of the above applies and yet their data stewardship efforts have been wildly successful. How did they do it?

The Business

PITT OHIO EXPRESS is a Pittsburgh, Pa.-based transportation company that handles less-than-truckload shipping throughout the Mid-Atlantic and Great Lakes states. With 22 terminals in its service area, PITT OHIO makes over 9,500 daily deliveries to more than 14,000 unique locations.

The Challenge

On any given day, PITT OHIO processes 9,000 – 11,000 new invoices. Each invoice is entered manually, in a process that has historically given greater precedence to entry speed than to data quality. While understandable from an operational perspective, this rapid-fire data entry approach eventually created a significant duplicate data problem for the company. Each variation in customer name or address information would create an entirely new customer record, meaning that a single entity could be represented by dozens of records in the database system.

The DataFlux Solution

PITT OHIO selected DataFlux to complete its customer data integration initiative. The intuitive DataFlux interface allowed PITT OHIO’s business users (those employees whose job performance was tied to the integrity of information) to take responsibility for managing customer data.

DataFlux technology allowed PITT OHIO to discover and address problematic data, and to verify and merge customer records. Business users built data improvement workflows quickly and logically with the innovative job flow builder.

With DataFlux's fuzzy logic matching capabilities, PITT OHIO was able to reconcile customer records, identify duplicates, and reduce these multiple instances into a single master record.

The Results

PITT OHIO began the project with a goal of a nine-month implementation schedule. With DataFlux’s flexibility and intuitive interface, deployment and learning time were significantly shorter than scheduled. This project, scheduled for nine months, was completed in just five.

PITT OHIO began the deployment with an expectation of an ongoing 95% data consolidation rate. DataFlux allowed the company to surpass this goal, giving PITT OHIO a consistent 99% consolidation rate.

Before implementing DataFlux, PITT OHIO’s customer data management had been consuming significant amounts of time from the IT department and of multiple business users. After using DataFlux technology to increase the quality of customer data, PITT OHIO was able to manage this information with only a single business user.

PITT OHIO was able to turn its improved database into a competitive advantage. By refining its address information, the company was able to offer its customers more efficient shipping times and more advanced logistics than its competitors. Now, with the new, consolidated view of its customer data, PITT OHIO has been able to provide more targeted service, identify its most valuable customers and deliver competitive value in return.

Within any company, data and documents associated with customers, orders and products flows through the organisation as it processes transacted business. In the case of manufacturing, the main process that manages transacted business is the order-to-cash process. Here, order data can enter the company via multiple channels, e.g. call centre, sales representatives, via the internet or via inbound electronic messages. In most cases this means that orders are captured in a number of different front office systems, all of which can then trigger execution of the order-to-cash process. Customer data, product data and order data associated with these transactions could therefore potentially reside in multiple systems. Once orders have been placed, they need to be collected and collated so that ordering of materials and accurate production scheduling can take place. From here the products are then manufactured, inspected, despatched, and delivered to customers whereupon invoicing can take place.

The challenge for most manufacturers is to execute this order-to-cash process error free, efficiently and at low cost while producing high-quality products and meeting customer delivery dates. In addition most manufacturers want to balancing supply with demand to optimise inventory. This depends on accurate planning, sales analysis and prediction of demand, all of which are analytical processes. Just imagine then, the impact on core manufacturing operational and analytical processes if the data flowing through these processes is unreliable.

What Is Data Reliability?

Data reliability is about guaranteeing that core manufacturing data is secure, correct and complete wherever it is used throughout the enterprise. It is also about the meaning of that data being clearly understood everywhere it is used. Even if data is correct, ambiguous naming may well render it unusable simply because business users do not understand what the data means. Reliable, trustworthy data is therefore dependent on two things being in place for every manufacturing data item in use wherever that data is used. These are:

• Data Quality – i.e. actual data values being correct and complete
• Metadata Quality – i.e. commonly understood data definitions

Only when data is trusted can it be used in confidence in all manufacturing operational and analytical process activities. Guaranteeing reliable data, therefore, is an obligation all companies should strive for. This means companies need to invest in the necessary people, processes and technology required to govern their data on an enterprise wide basis.

This paper examines the impact of unreliable data on manufacturing companies. It then defines the requirements needed to guarantee data reliability in manufacturing and offers a practical approach to creating and governing that data. It then shows how you can get started in making trusted data available in to help improve operational efficiency, planning, inventory optimisation, customer service, compliance and profitability.

We’ll define CDI as the automation of the integration, reconciliation and management of customer reference data from enterprise systems and to enterprise systems. In other words, CDI solutions are purpose-built to package specialized data cleansing, rigorously-defined business rules, formalized policymaking (also known as governance), and on-going stewardship to create a single source of the truth about customer data as a service to the enterprise at-large. In this paper, we’ll describe the lifecycle of pre-facto CDI, which can be divided into three main phases:

• The Pitch
• The Planning
• The People

The ability to deconstruct the early stages1 of a CDI effort into these three components has been a boon to companies who need integrated, well-managed and continually propagated customer data to a range of systems and users. Success in these areas virtually ensures a useful and sustainable CDI program.

All organizations have data on their products, inventory, parts and services – and most organizations have more product data than they have customer data. Increasingly, this information is becoming even more important to the overall health of a business. Yet this means that poor-quality product data is also becoming increasingly problematic. The unique challenges in the management of product data can inhibit the search for supply chain optimization, spend management and a more unified view of the enterprise.

The problems with product or item data stem from the wide variety of structure and conventions for this type of information. While customer data has a relatively small set of defined and universal attributes (name, address, email address, phone number), product data is much more complex. For example, a single company may have multiple definitions and descriptions of something as simple as a 60-watt light bulb within its product data.

Just imagine how complex, inconsistent and unreliable product data can be if it arrives from a dozen different suppliers in your trading network. Organizations are also grappling with the fact that enterprise resource planning (ERP), supply chain management (SCM) and other applications have done little to solve these issues. These applications can encapsulate the processes that drive a business every day, yet they typically have no integrated data quality capabilities to find and eliminate bad data. But creating additional ERP or SCM applications on top of existing applications to correct these issues essentially develops redundant silos of product information, and further complicates an already complex task.

Issues such as duplicate product numbers, obsolete product IDs and inconsistent item descriptions exist across all departments within every organization, impacting every level of the operation. An inability to understand the products that are being sold can affect the organization’s ability to plan for new products in the future. Similarly, a confused, disparate view of direct and indirect spending can foil the most well-intentioned spend management efforts.

The bottom line is that poor-quality product data creates difficulties in controlling the costs of production, promoting the productivity of the company, and the delivering finished goods. After all, the data within your applications drives every decision, from long-range strategic planning to day-to-day operations.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

• How organizations can analyze existing data prior to data migration efforts
• Methods to standardize, validate and verify data throughout the data migration process
• Ways to turn a data migration strategy into an ongoing data governance program

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Join us for an informative webcast with DataFlux, the leader in data quality and data integration, and CPP Plc, an international marketing services business, to understand how master data management (MDM) can help you move your business forward.

Watching this webcast will help you discover:

• How CPP's MDM programme won approval within the company
   
• How CPP implemented its MDM strategy and logged half a million pounds worth of savings in 10 months
   
• How the DataFlux qMDM platform has enabled CPP to improve overall customer service

This webcast is a must-see for professionals that are facing data-related challenges in their day-to-day operations.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Industry thought leader Mike Ferguson of Intelligent Business Strategies and Dan Soceanu from DataFlux look at the importance of reliable data to the insurance industry in this informative webcast. While focusing on the impact of poor data, they also discuss how an enterprise data quality approach can help you regain control of your data.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

• Identify and reconcile duplicate information
• Improve the quality of product, customer, materials and asset data
• Integrate data governance controls within the SAP environment

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

Note: This webcast requires Microsoft Internet Explorer 7.0 or above.

The Company

One of the nation’s largest not-for-profit health plans, this company has over 50 years of history in serving its clients and now serves nearly nine million members. The organization is dedicated to delivering health care in the most efficient and effective manner possible by constantly involving itself in research and innovation.

The Challenge

With an enterprise IT environment containing 4,000 applications over eight distinct business regions, each generating massive quantities of data, the company’s overall data store is one of the largest in the world. Managing and ensuring the quality of this data was a high priority for the company.

Like any business, the company ultimately needed clear data that uniquely and consistently represented each business transaction that occurred at the company. The company’s goals were to enforce custom data quality rules and to measure the quality of data over time. The company also sought a solution that would allow business users to access and have control over data quality reports across the enterprise. Therefore, it required a highly scalable, highly distributed data quality tool that could perform all aspects of data profiling, data quality, master data management and data monitoring.

After evaluating products and solutions from multiple vendors, the company selected DataFlux to meet its needs.

The DataFlux Solution

The award-winning DataFlux data quality and data integration platform offers a unique set of workflow tools built on an industry-leading technology platform that encompasses every facet of the data management process. Through its intuitive interface, DataFlux gives business users powerful data improvement capabilities and complete control over enterprise data.

The company’s data stewards, business analysts and data owners are now able to build complex data improvement workflows quickly and logically. DataFlux technology then allows those jobs to be implemented, in batch or real time, transforming data quality projects into ongoing data governance policies.

The Results

The company’s goal in using DataFlux technology was to take the contents from multiple tables – each holding up to 200 million records – across the enterprise and distill it into meaningful, useful data. By integrating DataFlux technology into its daily, weekly and monthly ETL routines the company was able to transform this disparate data into high-quality, managed and useful information to drive business intelligence.

The company’s initial implementation of DataFlux technology was centered on customer data, particularly patient healthcare records and care delivery. It utilized DataFlux to provide the real-time quality, accuracy and availability of this data, as well as rationalizing and de-duplicating data across its multiple data regions.

Moving forward, the company intends to extend DataFlux’s master data management capabilities as the basis of a real-time enterprise data governance program.

The Business

An international family resort destination company has a global presence and hosts millions of visitors every year. To accommodate the sheer volume of customer interactions, the company operates a massive guest reservation system linked to multiple data sources. With thousands of new entries manually entering the system daily, data quality problems are inevitable.

The Challenge

The company’s guest reservation system is composed of more than 14 underlying systems, and obtaining consistent data from these systems creates both data integration and data quality challenges for the company. The organization sought a solution to cleanse, standardize, match and improve customer data.

The company needed to validate, correct and improve the accuracy of its guest address information by standardizing and de-duplicating its existing data via batch cleansing. This was part of a larger effort to institute data governance controls in real time to maintain high levels of data quality.

Since improving the overall usefulness of its data was a major goal for this company, it sought to integrate list management into its data quality solution. As a family destination, the company was particularly interested in implementing householding — combining multiple records from individual guests who live at the same address into a master household record. Furthermore, the company wanted to enrich its guest database with demographic data to create better, more targeted marketing initiatives and better analytics.

After analyzing a number of proposals — based on criteria such as the quality of the service offering and vendor experience in their market — the company chose DataFlux.

The DataFlux Solution

The company selected DataFlux to analyze, improve and control data. With its industry-leading graphical user interface,DataFlux gives business users powerful data management capabilities and complete control over data quality initiatives, allowing business users to easily create business rules to ensure that data meets company standards.

Once the rules have been created, DataFlux technology can extend those same rules throughout the IT environment in batch or real time, making data quality an integral component of day-to-day operations.

The Results

DataFlux technology helped the company more effectively manage its information — validating, cleansing and improving its customer data — to help enhance relationships with its customers.

Using the pre-configured address standardization capabilities, business users were easily able to create workflows that could recognize and standardize the multiple international address formats within the company’s data. DataFlux users were also able to use address information to combine records on family members into master household entities, helping the company easily recognize repeat customers and more effectively target their marketing efforts. More importantly, as the company continues to expand to support other customer-facing initiatives, the scalable DataFlux solution will be able to grow with it.

The Business

This global division of a highly advanced technology company managed the corporation’s information competencies with an expansive global delivery capability. This company is a leading federal services and IT contractor with a strong heritage of delivering world-class solutions and delivering advanced technology across a broad spectrum of domains.

The Challenge

To support its wide-ranging operations, the company must manage extensive amounts of product data. The company had over a million existing, unique item records and an additional 10-20K new records were generated each month. A substantial portion of these records were for similar or identical items that were bought elsewhere in the company.

As a result, the company had a common data problem – duplicate data – but had no idea precisely what data was duplicated. This was not just a data management problem, however. Accurate spend analysis was extremely difficult because the duplicate items were hard to reconcile in reports.

The company saw a need for accurate, comprehensive commodity coding. A single code, when added to a record, could provide a universal identifier for the item, regardless of the originating application or source. The organization initially sent this data to a third party-vendor for manual coding. But with a per-record cost and an eight-week turnaround time, the company found this solution to be expensive and inefficient.

Seeking a more cost-effective and efficient commodity coding solution, the company turned to DataFlux.

The DataFlux Solution

DataFlux technology enabled business users at the company to easily assign UNSPSC commodity codes to item data. The DataFlux Accelerator for Commodity Coding gave the company a commodity coding process that was able to comprehensively and accurately categorize the company’s data.

With the intuitive DataFlux interface, business users can drive the commodity coding process. The software looks at item descriptions and other details to find a probable match in the UNSPSC registry. A business analyst can check these matches and refine them over time. The technology saves the rules created by the business user, allowing the company to train the software to the needs of the business.

The Results

Taking commodity coding tasks in house represented an immediate and significant cost savings for the organization. The entire DataFlux implementation was brought in for about half of the cost of an average year’s worth of outsourced data processing. Furthermore, processed data was now available immediately, instead of after an eight-week delay.

Furthermore, the company found that the quality of the data processed through DataFlux was a significant improvement over what had been provided by the outsourcing vendor. The company found that a substantial portion of its data had previously been incorrectly coded – or single items had been coded to multiple UNSPSC descriptions. In one example, a single item had been coded under 121 unique UNSPSC classifications.

DataFlux allowed the company to correct these issues, resulting in consistent, accurate and reliable data for the company. With more accurate commodity codes, the company was able to perform more accurate spend analysis and consequently reduce costs.

The Business

An insurance provider with operations throughout the world handles millions of policies annually. This company is one of the largest property and casualty insurers in the United States, with a significant presence in commercial and specialty markets, and is consistently listed among the Fortune 500. Like any insurance provider, much of the success of their business resides upon having a complete and accurate view of their customers.

The Challenge

With a history of growth through mergers and acquisitions, the company had data residing in divergent and incompatible systems. And, even though this same data was crucial to their decision-making, business owners had little control over it.

Householding was also a particular concern for the company. Without a single view of the customer across systems, the company had no easy way of identifying customers who held multiple policies with the company, or worse, they could not prevent mistakenly soliciting an individual who was already covered under a policy held by another member of the same household.

Poor data quality affected the company in many ways. The company discovered that it had been sending multiple solicitations to the same clients, approaching clients about policies for coverage they already had, and missing opportunities to offer clients and prospects services they genuinely needed.

The company decided to undertake a “philosophical change” and make data quality a top priority throughout the enterprise. For this effort, they needed a data quality solution with the ability to give business users control over the data.

The DataFlux Solution

The company selected DataFlux to increase the quality of customer information. The intuitive DataFlux interface allowed the company to establish business users as data stewards. Business users appreciated the logical, sequential layout that allowed them to construct jobs to rationalize, standardize and transform data, giving them the ability to manage business-critical data without relying on time-consuming and costly IT implementations.

DataFlux technology allowed business users to manage data across sources. Duplicate and incomplete customer records were isolated and corrected, and the company gained a more realistic view of customers by properly householding client information.

The company made data quality a part of its day-to-day operations. Existing data was corrected and standardized, and new data now conformed to the established standards.

The Results

DataFlux technology produced immediate results. In one instance, DataFlux drastically reduced a previously manual claims review process, resulting in a verified 93% savings on the exercise. The company established a Data Quality Center of Excellence to help replicate these initial successes throughout the enterprise.

Improved customer householding led to increased efficiencies and reduced costs. Multiple members of households covered under separate polices were merged into a single master record, allowing the company to provide better customer service and limit ineffective targeting of clients.

With DataFlux, the company became more active in integrating data quality into its operational philosophy. With more direct control over the quality of the data in their hands, the business users driving the decisions that directly affected the company’s profitability had more control over their ability to make the correct decisions.

The Business

Sun Microsystems is a Fortune 500 company that develops and provides a diversity of software, systems, services and microelectronics that power everything from consumer electronics to the world’s most powerful datacenters. Its network computing platforms are used by nearly every sector of society and industry and are the backbones of some of the world’s best known search, social networking, entertainment, financial services, manufacturing, healthcare, retail, news, energy and engineering companies. With over 33,000 employees worldwide, Sun is a leader and visionary in its fields of expertise.

The Challenge

With over 800 disparate legacy applications, Sun tackled a large-scale master data management (MDM) project to consolidate the customer data held in those applications to a unified customer data hub (CDH). The ultimate goal was to have a single source of truth that would enable a 360° view of the customer.

To meet the goal of one true view of the customer, several challenges needed to be overcome. At the outset, the most critical was to apply a common structure to each of the numerous data systems as they were combined. As these systems were brought together, Sun discovered the presence of duplicate data – a result of both the integration process and also previous data that was already repeated in the systems.

Not surprisingly, since duplicated data already existed within each of the systems, it became magnified and overexposed because the newly centralized data was available to a much larger audience. To solve this challenge, Sun turned to DataFlux.

The DataFlux Solution

Sun chose DataFlux to perform data quality tasks, such as data profiling, metadata analysis and data de-duplication. DataFlux offers a unique set of workflow tools built on an industry-leading technology platform that encompasses every facet of the data management process. Through its intuitive interface, DataFlux technology provided Sun’s business users with powerful data improvement capabilities and complete control over data quality and data governance initiatives, while allowing the IT team to visualize the data improvements as they happened.

The DataFlux Accelerator for Customer Data Analysis was also used to discover exactly what data problems existed in Sun’s new unified customer data repository and used the pre-built scorecards to gauge the health and integrity of its data after the data cleansing was completed and new data entered the system.

The Results

Sun uses the automation functionality in the DataFlux technology to gain an accurate customer view. “dfPower Studio has allowed us to automate several tasks within our complex process,” said Dalton Cervo, customer data quality lead at Sun Microsystems. “It has given us the ability to quickly and accurately execute what would otherwise be very time-consuming and labor-intensive steps. DataFlux is a critical piece in making this process scalable and repeatable.”

Sun now uses DataFlux technology to assist its data analysts to understand and make decisions based on the trusted and accurate information that now resides within its data store. Analysts are able to:

• Identify potential duplicates
• Collect customer detail data for scoring
• Review results and get approvals
• Consider disposition and execute actions

“Without DataFlux, it would have been impossible to quickly produce the required results,” Cervo said. “With a few data analysts and the DataFlux technology, we can process dozens of company data sets in a single day. Otherwise, we would spend days analyzing a single company.”

The Business

SmartBase Solutions is a full-service database marketing company that improves marketing and sales activities for its clients through the analysis and measurement of their customer data.

SmartBase Solutions manages its clients’ customer data, integrating information from disparate sources — internal customer data, rented lists, potential prospect lists and independent databases – into a more manageable source of information. To create databases for each client, SmartBase must ensure that every item of data is cleansed, standardized, and assigned to the proper customer for search returns and direct mailings - and then accurately compile the data within a single repository.

The Challenge

SmartBase is a business built on data. For the company to remain competitive, the quality of the data that SmartBase provides to its clients must meet or surpass their expectations.

In creating databases for each client, SmartBase must ensure that every item of data is cleansed, standardized, and contains a match code to accurately compile it within a single repository. Assigning these match codes manually would be expensive and time-consuming, severely limiting SmartBase’s potential for growth. SmartBase knew that automating the assignment of these codes would give the company a significant competitive advantage, but because of its diverse client base the company needed a solution which could assign match codes while being both accurate and flexible.

After an extensive search, SmartBase chose to partner with DataFlux to ensure that it was providing high-quality data to its clients.

DataFlux/SmartBase Solutions

SmartBase processes each customer data file that its clients send over through DataFlux dfPower Studio and the DataFlux Integration Server to find and resolve problems in customer data across a client’s various data sources.

SmartBase also uses DataFlux technology to determine the ideal match code for a record before loading the file into the main client database. SmartBase can then apply household or cluster match codes to ensure its clients perform accurate direct mailings and marketing outreach. With the flexibility of DataFlux technology, SmartBase can tailor its match code options based on the content of each customer data file, adjusting sensitivity and security levels as necessary.

“Working with match codes is an art, not a science,” says Ward Rabuse, database manager at SmartBase. “Data is a fluid and dynamic resource, and we have to have tools that can adapt to the nuances of each individual data source. DataFlux has given us the flexibility we need to effectively manage client databases.”

The Results

DataFlux is in its eighth successful year helping SmartBase Solutions deliver more valuable customer data to its clients. DataFlux technology offers a tremendous benefit for SmartBase, eliminating the need for manual cleansing and match-coding, a process that would involve a significant amount of time and money. Using a quick and thorough solution like dfPower Studio has allowed SmartBase to grow its client roster without any loss of efficiency.

“DataFlux technology has helped us to manage our client databases since 2000,” says Rabuse. “Not only are we able to provide an industry-leading level of service, but the time and stress that DataFlux has saved us since we first started using it is unquantifiable.”

The Business

A prominent travel and leisure destination developer builds, maintains and markets dozens of four-star, high-end ski villages, golf resorts and beach properties. Each year, the company hosts millions of visitors at these luxury destinations, and to enhance these customer relationships, the company focuses on retentions and return visitors. To keep customers interested and informed, the company is also striving to find new and innovative ways to promote existing services to their client base.

The Challenge

Like all marketers of high-end products, the resort developer had a problem: how do you market to an audience that expects – if not demands – highly personalized interactions? To accurately communicate with its customer base, the company had to build a more usable store of customer data. This store of customer data could then be used to segment customers, personalize customer communications, and market to customers more effectively.

Because each property had its own business systems, including customer relationship management (CRM) and operational applications, customer data was available in separate “silos” throughout the company. The company had no easy way to understand that the Smith family that hit the slopes at one of its ski resorts in the winter and the Smith family that relaxed at one of its beach properties in the summer was, in fact, the same Smith family. On top of this, there were separate systems within each resort that captured transactional data on lodging, retail, food and beverage and so forth.

As a result of this disparate network of data sources, the company had duplicate, redundant or unaffiliated data from system to system. The company needed to clean up their data to build a unified, accurate and reliable customer master file to build a true view of the customer.

The DataFlux Solution

Many companies implement customer data integration (CDI) programs without understanding the role of data quality in building the reference master file. If the result of the integration project allows multiple records per customer, a marketing campaign may send the same person several duplicate mailers. More importantly, the company might fail to realize the true lifetime value of the customer. This resort developer chose DataFlux to help build robust customer profiles that could better support personalized marketing campaigns.

The company uses DataFlux to analyze, correct, integrate and enhance customer data as it enters the master customer database. With DataFlux’s sophisticated matching technology, they can link to information in the legacy, resort-specific CRM applications, creating an aggregate record that contained all the details about the customer’s account history.

From hotel reservations to ski lift tickets, information from each resort rolls up into the master reference system. Regular use of DataFlux helps correct and validate information to ensure the customer data is an asset for the entire organization.

The Results

After implementing DataFlux technology, the company can now build more effective marketing campaigns. Customer data is now centralized into one system, allowing marketers to segment customers according to similar interests, hobbies and spending patterns. Since the data is much more reliable, they can create more accurate, personalized campaigns.

The ultimate goal of the resort developer is to create a lifetime relationship with its customers. As data is collected over time, they can offer promotions for different stages of life. For instance, a 40-year-old person who is a frequent skier at the resorts would receive promotions for time-share opportunities at the developer’s newest ski resort. By utilizing more accurate customer data, the company can build better marketing programs – and create more profitable lifetime relationships.

The Company

Since 1968, PBS has managed and developed payment systems and payment cards. They are currently the industry’s leading operator in the Scandinavian market. PBS has 850 employees and handles over 1.3 billion card transactions annually through one of the world’s most advanced and future-proof payment platforms.

The Challenge

Over 130 million monetary transfers, or giros, are initiated in Denmark every year. Sending a giro normally requires that the payer travel to the post office to complete a form. PBS sought to enhance the efficiency of this process for itself and its clients by introducing an electronic payment process to replace the paper-initiated giro. But because of the “push” nature of the giro payment, where money is taken from a payers account and delivered to the payee – with the payee initiating the whole process – it is critical that the funds are delivered to the correct account.

The electronic payment system introduced by PBS effectively replaced giro payments, allowing individuals or corporations to register to receive payments electronically – resulting in greater convenience and cost savings for everyone involved.

However, for this process to work correctly, the system processing the payments needed to be able to correctly match the payee and payer, necessitating high-quality data and sophisticated matching throughout the system. The company needed a system that could accurately match the information with registered payees in batch. However, because the existing system relied on one-at-a-time user inputs to initiate the payments, exact matches were unlikely. To address this issue, PBS turned to DataFlux.

The DataFlux Solution

PBS chose DataFlux to perform data matching within the new electronic system. DataFlux technology allows users to extend business rules across enterprise systems for real-time data matching and monitoring. Additionally, because DataFlux offers pre-built localization software for over 240 countries, PBS was able to utilize existing Danish name, address and company standardization rules present within the software, saving a significant amount of development time.

PBS used DataFlux technology to profile the data in order to get a quick overview of the data quality problems, and then standardized the data from various sources.

The Results

After installing DataFlux technology, PBS launched a pilot project to verify the accuracy of the data matches. “We performed various tests of random checks of production data,” Dorthe Grabau, director, Domestic Payments at PBS said.” In the beginning, we were very strict, as our goal was zero mistakes. We asked 10 people to check 500 matches between the creditor’s data and the online bank’s data, and we found no errors in these 5000 matches”.

After the successful pilot, the electronic payment system was introduced via online banks and television commercials, with a target group of 2.8 million citizens who have a Web-based bank account. Now, customers have the option to receive an email or an SMS to alert them of a waiting payment at their bank.

With DataFlux technology, PBS is able to automate a traditional part of the outreach process – delivering the correct invoice to the right person. DataFlux technology is able to automatically perform the analysis and matching to correctly link the accounts in batch. DataFlux allowed PBS to automate a time-consuming process – with no errors.

The Business

A major international oil company operates in every aspect of the industry, from exploration and refinement to retail sales. Each of its business units faces unique challenges, exacerbated by an intense period of mergers and acquisitions that left the company with a complex information architecture and competing data standards.

Communication across business lines is crucial in this highly competitive industry, but each business unit also has its own unique needs for the data. The company knew that it needed a solution that could simultaneously meet both of these needs, so the staff decided to look at an enterprisewide data integration and data quality management solution.

The Challenge

The company faced several unique challenges. The company’s pipeline operations were accountable to strict regulations mandating that it routinely provide the federal government with detailed and accurate information on pipeline location, maintenance and repair. Inaccurate or incomplete information could lead to substantial fines for non-compliance.

At the same time, the company’s billing operations were struggling with maintaining multiple databases containing conflicting and constantly changing customer information. Reconciling these databases to provide a single view of the customer was recognized as a top priority for the company.

This oil and gas company was also preparing to launch a large-scale data migration to a single instance of SAP – a project that was expected to be monumentally cost- and labor-intensive. The company found a single data quality and data integration solution for each of these efforts – regulatory compliance, billing controls and SAP migration – through DataFlux technology.

The DataFlux Solution

With DataFlux technology, the company was able to launch an enterprisewide data quality initiative. The initiative introduced data quality as an integral part of day-to-day operations across the company’s different business segments across the globe.

Furthermore, the company maintained consistent, accurate and reliable data within its SAP environment with DataFlux technology. The same rules used to load data into SAP could also govern the quality of incoming data in real time.

The Results

With DataFlux technology, the company has seen substantial improvements in the quality of its data, freeing resources for more profitable exercises. The pipeline operation drastically reduced the amount of fines paid to government agencies due to incomplete or inaccurate information. With more accurate customer information, billing operations significantly improved their timelines. And an SAP migration scheduled to take 36 months took only 18.

Impressed with the results, the firm created a Data Quality Center of Excellence that offers support services for data quality management enterprisewide. The company plans to expand its data quality management initiatives and has chosen to continue to rely on DataFlux technology to drive these improvements.

The Business

This nationwide provider of disability income insurance products for professionals, executives and business owners has served its customers for over 150 years. The company has a nationwide network of agents and agencies throughout the United States.

The Challenge

The company manages over 250,000 policies across five distinct nationwide systems, with over 4,000 active claims at any given time. Data constantly enters these systems from hundreds of offices nationwide. The company’s growth and the layers of legacy data systems that came with that growth meant the company held this data in multiple, detached data silos with little ability to communicate with one another.

The company knew of intrinsic problems in its data — individual inconsistencies would regularly be discovered during routine processing or maintenance — but the company lacked the ability to determine just how numerous and pervasive these errors were. Additionally, trying to retroactively transform a decade’s worth of data provided a significant challenge, particularly with older systems that had been since retired.

However, the company was aware that the quality of its data was a significant driver to the success of its business. Because the company’s very business is driven by the need for accurate data, inconsistent or inaccurate information presented a potentially significant liability in terms of lost revenue, increased costs and even potential regulatory issues. The company clearly saw that it required a consistent, reliable view of its data across the enterprise.

The DataFlux Solution

The company selected he award-winning DataFlux data quality and data integration platform. DataFlux technology offers a unique set of workflow tools built on an industry-leading technology platform that encompasses every facet of the data management process. Through its intuitive interface, DataFlux gives business users powerful data improvement capabilities and extensive control over data quality and data governance initiatives.

DataFlux technology allowed the company to address data quality issues at the source, putting the ability to examine data for business issues in the hands of business users through a powerful and award-winning graphical user interface that allows users to analyze, improve and control enterprise data.

The Results

With DataFlux solutions in place throughout the company, the benefits were quickly seen. In one instance, various departments had noticed that there were often inconsistencies in various client records in one particular field on the records — the birth date of the policyholder — during their routine processing of policies. This seemingly simple piece of information is of vital importance to the company, since many of its key business efforts are driven off of a policyholder’s age. Prior to DataFlux data profiling technology being in place, the company had no way of knowing how prevalent the issue was. With DataFlux, the company is now able to easily scan all its data and immediately identify records which required corrective action, before any negative business impact is seen.

Building on the success of its current initiative, the company has been actively implementing a data governance program, addressing data quality in source systems across the enterprise and preventing poor-quality data from entering into the system. These efforts have resulted in further savings in time, resources and money.

The Business

A company that purchases, securitizes and invests in the secondary mortgage market indirectly finances one out of every six homes in the United States. Since its inception, the company has financed more than 50 million homes. In recent years, as regulatory pressures increased, the company found that it was relying on outmoded technology and processes to attempt to meet real and immediate compliance requirements.

The Challenge

Like every publicly-traded company, this business has faced increasing Federal oversight in the wake of many high-profile corporate scandals. New regulatory requirements, such as the Sarbanes-Oxley (SOX) legislation, only added to the compliance burden in an already heavily-regulated industry. The company was particularly concerned with new quarterly and annual financial reporting requirements. To meet these regulations, the company decided to institute a proactive data governance initiative to ensure that it was operating from the best possible data.

In the past, separate lines of business within the company were individually responsible for their own data quality. Each unit would work directly with the corporate IT division, creating data quality rules, which would then be implemented on application or data source by IT. This process had a slow turnaround time, with changes to the rules taking weeks or months to be implemented. Plus, the business analysts who depended on the data had very little control over it.

The company saw a need for a faster, more efficient and more reliable means of managing data quality. With the stakes raised due to SOX and other legislation, the company needed a more robust, reliable enterprise data governance system. Seeking a system with faster implementation times that could produce more reliable data — and that would place control of data quality in the hands of business analysts — the company turned to DataFlux.

The DataFlux Solution

The company chose DataFlux technology to help it meets its compliance goals. Using DataFlux data profiling and data monitoring capabilities, the company’s business analysts were able to work separately from the IT department to develop their own business rules for data quality.

By profiling their data, the company was able to seek out and correct erroneous or duplicate data to ensure that their data governance initiatives focused on the most troublesome data sources. Through statistical and numeric range analysis, business analysts verified that key metrics, such as loan-to-value ratios or total unpaid balances, fell within acceptable ranges. Once the initial data profiling had been performed, analysts established these rules as ongoing data monitoring routines, which were then deployed as real-time services using the DataFlux Integration Server.

The Results

By ensuring that its Federally-mandated reporting was built on high-quality data, the company is now much more confident about its quarterly and annual financial reports. Instead of multiple divisions having separate data quality rules, the company now has one source for data governance rules across all applications and data sources.

Putting data quality in the hands of the business users allowed the company to reduce the time needed to implement data quality initiatives from weeks and months to a few hours. Business users – acting independently of IT – could analyze the data and establish real-time controls. With business users controlling data, the company also gained greater flexibility to respond to changes and meet challenges as they arose.

Furthermore, the company is now certain that it has the technology in place to be compliant with SOX. By making data governance an ongoing part of its operations, this mortgage company is prepared to meet changing regulatory obligations in the future.

The Business

A top-ranked insurance company has issued more than 10 million auto policies nationwide. The company sells policies from a direct business unit and through more than 30,000 independent agents and brokers.

Like many organizations, this insurance company had millions of customer records in multiple data warehouses. Since the organization’s ability to write suitable policies was driven by having accurate information on its clients, maintaining the integrity of that data was essential.

The Challenge

Because of the sheer amount of data this company and its independent agents processed on a daily basis, it was inevitable that inconsistencies would occur. Different business units — and individual employees within these units — would apply different standards on customer records. With high volumes of data, those small inconsistencies could quickly snowball into major issues.

Since manually monitoring the data was impossible, the company sought a data monitoring solution that could automate this process. The company evaluated multiple vendors based on several key factors, including requirements that the solution include comprehensive monitoring and auditing functionality — powerful enough to address millions of records — and that business staff could manage the system without relying on IT staff. The solution would also need to operate seamlessly within existing applications, adding critical data governance controls to the current IT infrastructure.

The DataFlux Solution

The company selected the intuitive DataFlux interface to give business users a powerful platform to develop a complete set of business rules for maintaining high-quality data.

The company uses DataFlux to monitor multiple data warehouses, as well as an application that examines the appropriateness of policies. When new data enters the source systems in a monthly upload, the company can examine the new information in the tables, compare that data to the previous month, and then analyze the results against user-defined business rules.

Any differences found during this exercise are output to a database, where a data quality analyst examines the results to eliminate the explainable exceptions. The exceptions each month that cannot be easily reconciled are sent to a data warehouse programmer for investigation and resolution.

The Results

This company immediately saw success in the improved data quality in its data warehouse. The data warehouse supported better analysis of business events and provided more insight into trends in the customer base. The company also found that its initial success led to even more success in refining business operations.

The company discovered patterns in the nature of the problems that the monthly data quality run was uncovering. Because of the unique, intuitive DataFlux interface, business users were able to quickly build business rules to address these repeating issues – and build policies that can enforce corporate standards for data quality.

This allowed the company to improve its entire data quality process - making it more efficient by eliminating false positives while also increasing the number of potential problem areas that could be checked.

The Business

LexisNexis^® – the global leader in comprehensive and authoritative legal, news and business information – provides extensive online services that combine searchable access to more than 4 billion documents from thousands of sources, including the world’s most respected legal publishers.

The Challenge

In previous years, LexisNexis outsourced the mailing of more than 12 million pieces of mail supporting more than 2,000 campaigns. To reduce these costs, LexisNexis needed to create a concise, accurate and up-to-date marketing database from a wide range of sources, including state attorney registrations, internal data sources and third-party databases. They also wanted to provide more targeted offers to their customer base. Due to the different origins of data, the company faced a number of data management problems, such as duplicate names and invalid addresses. This meant that LexisNexis frequently:

• Mailed multiple pieces to the same address because there were similar records in different data sources
• Sent prospect offerings to attorneys or law firms that were already LexisNexis customers
• Found it difficult — if not impossible — to target and communicate effectively with specific individuals

The DataFlux Solution

LexisNexis turned to DataFlux to refine customer data and help ensure that the nation’s attorneys and law firms weren’t bombarded with duplicate or unwanted messages. The company selected DataFlux for its ability to allow both business and IT users to manage and improve their marketing database.

“Our former outsourcing vendor didn’t worry about data quality because limiting the number of mailings wasn’t in their best interest,” said Bill Welch, LexisNexis’ marketing systems manager. “That was one of the reasons we took this on, and the results have been outstanding.”

The Results

DataFlux technology allowed LexisNexis to reduce the number of redundant and erroneous mailings. By eliminating many unnecessary communications — and managing customer data more efficiently in-house — LexisNexis projects a savings of $1 million a year.

Additionally, LexisNexis was able to build better information resources about its customer base, allowing the company to create a 360-degree view of the customer and their specific needs and challenges. This ability to provide more accurate, personalized service led to improved customer satisfaction and retention rates.

“DataFlux was the key element that enabled us to merge individuals from various data sources into one database,” Welch said. “With better control over our customer information, we know that we aren’t mailing hundreds or thousands of pieces to bad addresses — and that we can provide a higher level of service to our clients.”

The Business

This leading contract resource organization offers a broad range of professional services in drug development, financial partnering and commercialization for the pharmaceutical and healthcare industries. This multi-national company has over 15,000 employees and offices in more than 50 countries and is focused on providing customer-centric solutions, making it an undisputed leader in its industry.

The Challenge

This company offers a range of integrated product development services that meet the needs of pharmaceutical companies during the research and development phases of new products. Within the company itself, each project creates large amounts of data, which is often stored in individual applications. The information that the company has compiled about projects, doctors and patients has been kept in isolated applications, leading to stark silos of operational data.

To fulfill a vision of having a more integrated operational environment, the company sought a solution that would allow it to meet its required high levels of data quality and successfully integrate its siloed data. Most importantly, the company wanted a solution that would allow these efforts to be built into an enterprisewide master data management (MDM) initiative.

The DataFlux Solution

The company chose a phased approach to MDM powered by DataFlux technology. The first step was to implement a data quality program to examine and align this disparate data. To further improve efficiencies and create a more effective organization, the company selected the DataFlux qMDM Solution to bring order to its diverse data environment of hundreds of projects spread out across the world and provide a more unified view of projects, doctors and patients. The DataFlux qMDM Solution served as the engine to power a major master data management initiative to drive value from its data quality and data integration challenges and build a more consistent, unified view of enterprise information.

The Results

DataFlux had originally enabled the company to unify its view of customers, patients, products, along with all other data types. With the DataFlux qMDM Solution, the company is able to not only address the quality of its information, but the company can now begin to intelligently consolidate that data, and embark on a true MDM effort.

DataFlux technology provides the ability to build detailed data quality and data integration routines to correct, standardize and de-duplicate data. By incorporating this award-winning technology before it enters the master repository, the DataFlux qMDM Solution not only ensures that the data is managed effectively — but that the data becomes a business asset.

The Business

One of the largest financial institutions in North America serves millions of personal, business and public sector accounts. The company has offices throughout North America and in more than 30 countries around the world.

The Challenge

Since 2000, no industry has undergone as much upheaval and widespread change than the financial services sector. Financial scandals and a marked downturn in the economy contributed to a shift in the regulatory oversight on banks, insurers and accounting firms. The new laws came fast and furious – Sarbanes-Oxley, the PATRIOT Act, Office of Foreign Assets Control (OFAC) – causing financial services providers to adjust and react to a new marketplace.

The implication for financial institutions was clear. Verify that you are doing business with reputable individuals and companies – or face severe fines. The laws specifically required a review of customer records against a list of names of people or organizations suspected of terrorist activity. To accomplish this, financial services institutions need a technology to monitor, identify and flag suspicious activity for compliance officers.

Immediately after these regulations went into effect, this financial services company installed a system that used exact or near-exact matching to identify questionable transactions. As a result, the compliance team received a high number of “false positives” – transactions that looked suspicious but were actually valid – to review and verify each day.

The DataFlux Solution

To meet the demands of the new regulatory standards, the company needed a technology solution that could intelligently find customer records and transactions that could pose a threat to national security. This would help minimize the impact on the compliance department while keeping the company compliant with regulations.

The financial services company selected DataFlux software to help fine-tune the matching process to help them achieve compliance with federal mandates. DataFlux technology allows the company to compare transactional data to a third-party database that contains updated lists of known criminals, terrorists or affiliated organizations. They also built an interactive interface for tellers and customer service staff to input information manually and verify against a third-party list.

Using proprietary fuzzy matching technology, DataFlux solutions can intelligently identify relationships between different data sources. The technology allows the company to customize match rules as conditions change, allowing the financial services provider to refine match results over time.

The Results

After implementing DataFlux technology, the financial services company saw a quick reduction in the number of false positives reaching the compliance team. This process improvement allowed the staff to do more thorough checks of questionable transactions – and reduced the workload on the already over-burdened compliance staff.

With better matching technology in place, the company no longer “over-regulates” transactions. Transactions are scrutinized more accurately with DataFlux technology, which improves processing time for compliance checks – and allows the company to provide more responsive customer service.

The Business

A leading provider of business information supplies company specific credit reports and profiles to a global clientèle of corporate clients. The company also provides e-commerce enablement tools, including sales and lead generation lists.

The Challenge

This business information provider currently has more than 54 million active organizations in its information database. The information is used by its clients to make better decisions regarding credit risk, purchasing and marketing when conducting business-to-business (B2B) transactions. The company receives data for its information database from several different sources, including websites and data entry by internal account representatives.

With nearly 500 companies logging on each day and inputting new or edited data into the company database, the business information supplier needed to be able to automatically validate and verify the status of the company inputting information, such as if the company was:

• A current contact
• A new contact but with an existing company record
• A completely new contact

Because people might enter their name or their company name differently each time they accessed the system, the business information provider found that up to 20 percent of the information flowing into the business database consisted of duplicate records.

In addition, the information in the business information database often served as the foundation for database marketing efforts for the company. Lack of reliable information could hinder the company’s efforts to retain and acquire new customers.

The DataFlux Solution

Since almost one-fifth of the new data entering the system was unusable, the company had to take steps to address the spread of bad information that could jeopardize the company’s primary product: business data.

The business information provider chose DataFlux to routinely match and verify customer data across any platform, including Microsoft SQL Server and Oracle databases that the company used to store data for different purposes. DataFlux helps the company match inquiries to company data and integrate information into their existing IT architecture. And prior to database marketing campaigns, the company can compare mailing lists to their customer records to remove duplicate entries.

The Results

With better, more reliable corporate data, the business information provider was able to present a better product to the marketplace. High-quality data helped them process customer data faster and more efficiently, and the removal or consolidation of duplicate data allowed them to lower data storage costs.

The marketing department also benefited from better data. Previously, 15-20 percent of all mailings to prospective customers came back undeliverable. With DataFlux solutions in place, duplicate mailings disappeared almost entirely, saving the company time and money on wasted mailing and production costs.

The Business

One of the largest banks in the Southeast US has more than $100 billion in both loans and deposits. The bank has offices in 11 states and prides itself on a decentralized management structure that puts business decisions in the hands of local executives.

Like any financial services institution, having timely, accurate data was essential to the company’s performance, stability, and profitability. Because of the increasingly competitive nature of the business, and the continually shifting financial landscape, the bank knew that having the most accurate data meant having a real competitive advantage.

The Challenge

Although decision-making was decentralized throughout the company, the bank’s corporate headquarters still needed to create credit risk management reports that would allow those local decisions to be consistent with corporate goals and objectives for the entire company. This would ensure that executives at the bank’s 50 units would have the information they needed to approach each decision with an eye towards understanding how much lending risk they could assume.

A credit risk unit provided oversight of this process. Periodically, the unit would have to divert from other urgent business and spend days manually compiling data from multiple sources into spreadsheets for analysis and reporting. Using this labor-intensive process, it would routinely take three weeks to compile a single risk management report, meaning that the report could easily be out of date by the time it was completed.

Furthermore, because this manual process was error-prone and difficult to verify, the organization had little or no confidence in the validity of the reports that they were creating, even after spending a significant amount in bringing in an external consulting firm to assist with creating the reports.

The DataFlux Solution

The bank chose DataFlux technology to review and compare multiple data sources simultaneously through data quality and data integration workflows. The bank created a set of business logic rules and applying these rules to the data that the bank collects from all its lending units. DataFlux offers graphical workflow tools and a powerful, intuitive interface to give high-level data quality and data integration capabilities to business users. Analysts in the credit risk units could automate their accumulated business rules and create a better mechanism for inspecting and correcting data.

With DataFlux technology, the bank’s staff automated the process for calculating and recalculating risk ratios in various loan sub-categories on a nearly continual basis. At the same time, the system could alert users when credit risk exceeds appropriate levels.

Through its data monitoring capabilities, dfPower Studio can generate automated email alerts when a potential loan is outside the bank’s lending parameters. Feedback on credit risk, once an arduous and latent process, became an ongoing piece of the company’s IT infrastructure.

The Results

With DataFlux technology, it takes hours – not weeks – to calculate risk ratios. For the first time, the bank’s credit risk group feels confident in the reports it is producing due to higher levels of data quality – and the ability to monitor acceptable levels of data quality over time.

The technology is currently used by 10 analysts (with plans to increase that number to 25) who do not have programming skills. Further, the group is freeing itself from dependence on expensive consultants, helping the company realize an impressive ROI from the initial deployment.

The Business

Intellidyn is a leading information and analytic services company, delivering database integration, target marketing and strategic insight to consumer businesses. Intellidyn maintains one of the largest prospect databases in the industry and maintains the largest repository of integrated credit, demographic, lifestyle, auto ownership and purchase behavior data available in the United States. The company provides unparalleled expertise in using data to improve marketing ROI.

The Challenge

Building a successful data warehouse and a unified consumer view has been a challenge. As everyone knows, more than 90 percent of these projects have failed. Intellidyn learned that a major obstacle was in understanding that warehousing and CRM is not all about IT. The challenge is to customize those functions to the business application.

Intellidyn continues to move deeper into a dynamic environment. The challenge is how to handle the new volume of data from dynamic transactions such as ATM, VRU activity, website hits or purchase transactions. The ultimate challenge is being able to take all that data and do all of your modeling and scoring on the fly, so that the next day you’ve wrapped in all of the previous day’s behavior. According to Peter Harvey, President of Intellidyn, “It all comes down to the consumer’s demand to marketers — know me…or my loyalty drops to zero.”

In this dynamic world, how do you know them? “You know them by their transactions,” said Harvey. “When you can show consumers that you know them, it’s amazing what happens in terms of loyalty and renewals, which drives profitability.”

The DataFlux Solution

Intellidyn has implemented an enterprise data warehouse using a suite of SAS products including Warehouse Administrator. Presently, the data warehouse is approaching 6TB in size. Also, there’s the addition of DataFlux data management technology integrated with SAS software to handle data profiling, data quality, data integration and data augmentation. “DataFlux provides the fundamental ability to build the customer-centric or individual view. Without this critical data foundation, all other data warehousing and CRM functionality is compromised,” added Harvey.

The Results

Intellidyn has benefited from the DataFlux data management solution in a variety of ways, including:

• Decreased the access time to three different data sources from 22 to six hours.
• Intellidyn clients now have current, accurate and reliable views of their customers within hours instead of days or weeks.
• The enterprise data management solution was fully functional within three months, offering a 360-degree view of every consumer in the United States.
• Integrating external data – such as purchase behavior and risk exposure via credit data – with their internal transaction data: driving history, accident history, car ownership, past insurance company.

DataFlux gives Intellidyn an important competitive edge. It provides an end-to-end data management approach; it also does the best job of data integration; and DataFlux has the talent, plus the ability to respond immediately when the parameters of a project change, with unmatched service quality.

The Business

One of the world’s largest insurers has divisions and business units across the globe. The company provides a full spectrum of insurance services – including an array of life insurance programs, pensions and affiliated savings options – for a broad client base.

The Challenge

A division of this insurance provider based in the USA had information on customers and policies contained in a legacy policy administration system. The information included term dates, policy numbers and customer contact information. To provide more responsive service, the division wanted to migrate the data to a new, cutting-edge policy management application.

The insurance provider had tried to implement similar data consolidations in other divisions. Due to inconsistent, unreliable or inaccurate data coming from legacy systems, the data transformation process was long, arduous and over-budget. The IT staff found errors or inconsistencies in the data only after trying to move data to the target, causing massive rework.

As the other business units of this insurance provider began data conversion projects of their own, this group learned from the mistakes of their sister divisions. They identified and rectified problems before loading the data into the new system – building better data that could more effectively drive the application.

The DataFlux Solution

The insurance selected a DataFlux data management solution that allowed both business and IT analysts to inspect, correct, integrate and enhance data. Through an easy-to-use, intuitive interface, DataFlux technology allowed the content owners (business staff) to serve as a key component during the transformation process. This lowered the overall workload on the IT staff and allowed the business users to stay involved throughout the process.

With DataFlux, staff members can use data profiling to find data errors, anomalies or inconsistencies within the source systems. Then, through the software’s integrated data quality technology, the analysts can build rules to standardize, validate, verify and match data from these legacy systems.

And since DataFlux can directly access legacy data in mainframe applications, the company had an option for accessing and profiling that data at the source before the migration process. This functionality in dfPower Studio allows users to analyze data where it resides without unnecessary file conversions, saving companies time and money on this critical first step.

The Results

With DataFlux in place, this insurance company was able to slash the time and resources required to migrate data from legacy applications to the new policy management system. Unlike similar projects that lasted well over one year, the data consolidation and transformation process took only a matter of months.

And in the process, DataFlux’s data management functionality allowed the company to find and address data errors before loading them into the new system. So, when the new policy administration application went live, it was filled with consistent, accurate and reliable information – the kind of data that the company can use to enhance their customer support and product offerings over time.

The Business

A global leader in petrochemical manufacturing and refining has a truly worldwide reach, with offices or operations on every inhabited continent, spanning more than 150 countries around the world. The company provides a wide range of products and services in the oil and gas arena, including exploration, refining, and developing new and improved technologies to help make petrochemical processing better and more efficient.

The Challenge

The large petrochemical company faced a challenge that was familiar to many of its companion companies listed on the Fortune 500. The company had increased in size over time through a mixture of organic growth and strategic mergers and acquisitions. As the business grew, its expansion not only brought in new revenue streams, it also increased the number and complexity of business applications and information systems within the enterprise.

To create better information that was both more reliable and more accessible across business or geographical units, the company began to implement global data warehouses, massive information repositories designed to store standardized and validated versions of all critical business information.

One of these data warehouses was a resource for product data, containing details, descriptions and sales of the company’s products. This data would allow the organization to make an accurate assessment of worldwide market needs and product availability.

Unfortunately, product data is notoriously difficult to standardize. Even within the same business, companies have different ways to describe products and brand names. The company’s worldwide presence made the problem even more difficult by adding language differences and other cultural variables, making the task of building a global product data warehouse significantly daunting.

The DataFlux Solution

The worldwide petrochemical provider selected DataFlux technology to help them assess the data that they had – and validate and verify information before loading it into a data warehouse. Through pre-built and customizable matching algorithms, the company could automatically standardize data as it was loaded into the data warehouse.

With DataFlux solutions, the company was able to integrate business rules and existing logic into the software. Prior to implementing DataFlux, business analysts had to look at the data to understand that HD meant “high density” while HDTO stood for “high density transmission oil.” DataFlux technology incorporated those definitions – and associated permutations of those words or phrases – to create matching rules that could identify similar data and standardize information across millions of product records.

The Results

Before implementing DataFlux, the oil and gas company had to manually browse through records on a monthly basis to standardize data across sites. This process typically took two weeks each month to finalize, meaning business analysts spent roughly one-half of their time on this mundane, laborious task.

With DataFlux technology, the oil company could capture the knowledge of the business analyst in the matching and standardization routines within the software. The monthly routine – which once took almost two weeks to complete – now takes only 10 seconds.

As an added benefit, the oil company receives better data on products. Now, it can make better evaluations of product performance and create more accurate, usable projections for successful product offerings in the future.

The Business

Euro RSCG Discovery, a subsidiary of Havas, is a leading marketing and technology services organization, providing a comprehensive suite of marketing data services that offers its clients a more powerful competitive edge for their prospecting and CRM programs.

As part of its services, Euro RSCG Discovery creates and manages targeted direct marketing campaigns for its clients. To reach the appropriate groups and segments, the company relies on multiple purchased mailing lists along with existing customer information.

The Challenge

In the world of targeted marketing efforts, bad data has a direct impact on the effectiveness of the campaign and the bottom line. Each piece of mail that goes out to an incorrect or inappropriate address represents a missed opportunity and wasted expense. For this reason, householding and de-duplicating records were a top priority for Euro RSCG Discovery’s client base.

Euro RSCG Discovery previously used an in-house UNIX-based system to manage these marketing lists – a system that required extensive programming knowledge to operate. The company saw a need to open access to this capability to business users – those within the agency who knew what the data should look like. Because the company processed and integrated data from multiple sources, data standardization was a priority, but Euro RSCG Discovery needed a solution that would be flexible enough to enable quick and easy management of the data according to each client’s unique rules and requirements.

“We sought a solution that would complement an existing SAS BI implementation, as well as offer the flexibility that we needed,” says Rodney Mullins, director of technology for Euro RSCG Discovery. “While we considered DataFlux’s relationship with SAS during this search, it was really the flexibility and ease of use of the technology that ultimately convinced the company to purchase DataFlux.”

The DataFlux Solution

Euro RSCG Discovery chose DataFlux. The intuitive DataFlux user interface allows business users to quickly build sophisticated matching and de-duplication rules. With DataFlux technology, data from multiple, differing sources can easily be profiled, standardized and integrated into a single master record.

“Because we would be dealing with information from multiple data sources that would need to be assembled in different configurations for different clients, we needed a solution that would allow data routines to be built once and deployed in different ways,” says Paul Chandler, group account director for Euro RSCG Discovery. “DataFlux technology gave us the ability to store a library of data quality routines, which could be shared throughout the organization and deployed as needed in the configurations needed.”

The Results

DataFlux technology has significantly increased the efficiency of Euro RSCG Discovery’s householding and de-duplication efforts. Routines that previously had to be recoded by hand each week are now automated, easily adjusted and available on a daily basis.

The company also uses DataFlux technology to profile data sets from its customers prior to loading them into its data warehouse. This preemptive measure helps them maintain high data quality on an ongoing basis, and allows Euro RSCG Discovery to quickly identify any data anomalies from their customers’ source systems.

Euro RSCG Discovery has also found a significant amount of added value in DataFlux technology. The geocoding capabilities of DataFlux technology have allowed the agency to offer its clients more targeted campaigns, presenting them with the ability to drive mailings to deliver the right customers to targeted stores.

“The flexibility of DataFlux technology has been invaluable to us,” Mullins says. “We’ve seen a huge data quality reward for our clients.”

A large energy company develops and manages energy-related businesses and services nationwide, and directly provides electricity to millions of businesses and residences in the Midwest.This company had recently undertaken a series of mergers and acquisitions, which expanded its presence into new areas and new markets.

The Challenge

A side effect of any corporate growth is the proliferation of business systems. This company had dozens of enterprise resource planning (ERP) systems in use across the company, each with detailed information on customers and the entire supply chain. While each of these systems was useful for the specific business unit that depended upon it, nowhere did the company have a single, consistent view of this critical business data across the entire enterprise. In fact, the acquisitions had created an inconsistent, disjointed network of systems and applications across the various divisions and business units.

This lack of an enterprisewide view was troubling for a company with millions of customers. The company had difficulty providing service for customers with multiple accounts across different lines of business.

Even more troubling, this lack of consistent, reliable and accessible data meant that the company’s ability to respond effectively during emergencies was compromised. Since they provided a basic service in a part of the country where severe weather was inevitable, the company was determined that data quality issues should not interrupt a critical utility during an emergency.

The energy corporation decided to standardize all information from existing ERP systems on a single SAP application – a multi-year data migration project involving hundreds of IT and business employees dedicated to delivering better data.

The DataFlux Solution

The company chose the unique DataFlux GUI-based design environment to address and resolve problematic data before it entered the ERP system. DataFlux allowed business and IT staffs to work together to develop business rules that ensured only high-quality data fed the new system.

DataFlux technology helped users to profile the data to understand the state of data in each source system. Then, they could create new standardization, correction or de-duplication routines to deliver consistent data to the new ERP system.

Since the company had everything from customer information to product details in the source systems, the users had to discover and improve the quality of a wide variety of information. The company built detailed business rules to standardize and validate data on inventory, assets and other entities.

And with DataFlux technology, the same rules can be surfaced in ERP packages like SAP. So, data quality can be managed over time within the application, using the same rules that govern data quality during the implementation phase.

The Results

Large-scale data consolidation projects are notorious for running over budget and beyond anticipated schedules. Often, bad data “pollutes” the system, requiring massive rework and reloading of data during the implementation process. Additionally, end users become frustrated with new software if the underlying data isn’t useful or accurate.

However, by making data quality a priority from the beginning, this utility company met or exceeded budgetary targets and time schedules throughout the implementation. With the new ERP system in place, the company began to obtain a unified view of the enterprise, helping the company create a more efficient, effective corporate culture.

The Business

DSM creates innovative products and services in life and materials sciences. DSM’s products are used globally in a wide range of markets and applications, supporting a healthier, more sustainable and enjoyable way of living. DSM is committed to continuously providing outstanding products and services for human and animal well-being.

With its worldwide reach and extensive manufacturing operations, DSM found itself with a real need to improve and more effectively manage the amount it was spending on direct and indirect items. Any gain in efficiency could deliver a significant return on investment — and improve profitability.

The Challenge

DSM was seeking a solution that could uncover and fix general data quality issues as well as help the company manage data specific to its industry and its operations. To support its manufacturing processes, DSM manages extensive product, inventory and item data. As a result, spend reporting and spend analysis were critical needs within the organization. However, DSM found that it had no enterprisewide system for spend categorization — and frequently not even spend categorization at the business unit level. DSM sought to assign expenditures to defined spend categories to enable spend reporting and analysis.

To better manage this process, DSM needed a solution that would allow for coding to eCl@ss, the international standard for the description and classification of products and services. All of this also had to be seamlessly integrated into DSM’s existing SAP SRM systems in real time through a service-oriented architecture (SOA).

“Worldwide, there were a thousand people raising purchase orders, and we were asking them to make consistent judgments about which of the 27,500 codes was applicable to every order,” said Joachim Beurskens, Global Data Manager Purchasing for DSM. The company saw that automating the assignment of classification codes would eliminate the inevitable errors which would come from assigning these codes manually. Ultimately, the company sought to use the improved data for more accurate direct and indirect spend analysis. To achieve these goals, DSM turned to DataFlux.

The DataFlux Solution

The company selected DataFlux to give DSM’s business users an intuitive interface to find problems and develop rules to fix these errors. Once these rules were complete, the DataFlux technology offered the ability to integrate these business rules seamlessly within DSM’s existing SAP systems.

For eCl@ss commodity coding, DataFlux offered an intelligent, automated matching system that could analyze DSM’s existing data and correctly assign eCl@ss codes based upon the available data. The patented DataFlux matching technology drove the eCl@ss classification, automating what would have otherwise been an extensive and time-consuming manual effort.

Furthermore, for easy ongoing maintenance, DataFlux was able to enable the matching list to be drawn from a continually updated industry standard eCl@ss list. As new items were added to the SAP system, the staff could quickly update these items with eCl@ss codes, continuing the process of organizing spend data over time.

The Results

DataFlux provided DSM with reliable and consistent data — and enriched that data with industry standard eCl@ss codes. The SOA-enabled approach from DataFlux allowed the data quality processes to be reused across systems and architectures. This enhanced data quality enabled DSM to adhere to its data governance strategy, leading to measurable results and more efficient achievement of business goals.

“Users enter the text for the item they want to buy, along with any items codes that they have, and a screen pops up saying: ‘Here are the top five recommended eCl@ss codes based on what you’ve typed’,” Beurskens explains, “It looks exactly like the normal purchase order process, but behind the scenes it is matching the text against hundreds of thousands of keywords in several languages, and generating eCl@ss codes.”

With DataFlux technology in place, DSM has been able to correctly assign eCl@ss codes — driving improved spend analysis.

The Business

A major provider of document management products operates in 16 European countries. The company provides printing and copying equipment, as well as consulting services designed to help companies efficiently manage their documents.

The Challenge

The company approached DataFlux as it was undergoing a large-scale SAP migration spanning all countries in its European region. The company knew the continuity and control it wanted from its SAP system would be compromised if the data within SAP was inconsistent, inaccurate and unreliable. To complicate the project, the SAP system would incorporate data from 10 legacy systems from each of the company’s 16 European regions.

European countries struggle with unique data quality challenges due to multiple languages and different standards (for addresses,names, product IDs, etc.). These issues can undermine the ability to keep one unified file for each customer. As a result, data analysts at this company spent time cleaning data rather than supplying expertise in interpreting data and defining business requirements.

The company also wanted to make certain that data was entered correctly into its new SAP system. For instance, it needed a way to ensure that the same customer wasn’t entered in the system multiple times because of a slight variation in the name or address.

The DataFlux Solution

The company chose DataFlux for its intuitive interface that allowed analysts to quickly clean data from legacy systems and transfer it to the new SAP system, and for its ability to deliver connectivity with the SAP environment. It used DataFlux technology to make sure new data gets entered correctly – and the company can use those same rules in a real-time environment to provide a critical check on data quality.

“This company’s goal was simple: Ask a question once, store the answer once and make it available many times internationally,” says Richard Onslow, business solutions director, UK for DataFlux. “However, most organizations don’t have the data quality and data integration framework to make this a reality.”

The Results

Today, the company’s picture of its European customers is clearer. The company feels confident when they analyze data for patterns, trends and marketing opportunities. For example, a customer with a poor payment history can’t buy or lease from a different country’s business unit, because each unit has access to data from the entire European market.

Better data also means better customer retention. The company knows which customers have the highest value and can offer them the best pricing. “They know they are working with quality data,” Onslow says. “They have more flexibility and functionality and, most importantly, a single view of the customer.”

The Business

Zag is committed to improving the automotive retail experience by providing transparency throughout the car buying process. Zag offers its affinity partners and their members a guaranteed upfront price with information on how that price compares to what others have paid for the same car, and offers its certified dealers a steady stream of qualified buyers and incremental sales volume. Through its alliances with affinity partners, Zag provides buyers a trusted interaction, a guaranteed upfront price and a fair deal.

The Challenge

Zag’s entire business model relies on accurate data. Zag provides a clearinghouse that allows buyers to find a specific vehicle and a list of dealers who can provide that vehicle. For these transactions to take place, all the parties involved need access to accurate and reliable information.

In managing this data, Zag faced a problem. The inventory data on which Zag relied originated from multiple, unrelated sources. Though all the data was of a similar nature, there was no standardization, no agreed formats and no uniformity between the data sources. For Zag, each new import of inventory data meant a significant and gaining efficiencies and unique development effort to transform the source data to conform to Zag’s internal standards.

Zag found that it was spending a considerable amount of time standardizing inventory data before it was useable. The company saw that having the data rationalized and standardized before it entered its systems would mean a marked improvement in efficiency. To help it achieve this efficiency, the company turned to DataFlux.

The DataFlux Solution

The company selected DataFlux to standardize the incoming inventory data. The award-winning, intuitive DataFlux user interface allows business users to drive improvements to data. With DataFlux technology, business users at Zag were able to easily transform the incoming inventory data to conform to Zag’s standards. Once Zag’s standards were created, DataFlux technology automatically detected the nature of the incoming data and applied the correct formatting.
 
“Our business users have found DataFlux very user-friendly and requires no technical background. It’s easy to figure out and amazingly easy to use,” says Michael Gibson, vice president of engineering at Zag. ”Plus there were very few demands on the technical team to support it.”

The Results

With DataFlux technology in place, all incoming inventory data was correctly formatted and rationalized to Zag’s standards.

Zag was able to measurably reduce the development efforts that had been previously associated with incoming inventory files. This represented a significant gain in efficiency, eliminating the requirement to have an IT resource regularly dedicated to solving what was actually a business problem.

“Our entire experience with DataFlux has been very positive,” says Gibson, “The installation was easy, the technical support has been excellent, and we really like the fact that the software is so user friendly.”

The Business

A communications company in Canada operates a variety of distinct media and telecommunications offerings. The corporation has millions of cable television and high-speed Internet subscribers as well as wireless telephone customers across Canada. In addition, this diversified company operates a network of radio and television stations.

The Challenge

Like any large business, this communications company struggles with selling products across product lines. The questions are common for any multi-faceted communications organization. How can the company turn a valuable cable customer into a wireless customer? Are there effective ways to market to existing wireless customers to maximize cross-sell opportunities? Which existing customers could be enticed to try another product at a reduced price?

But first, the communications provider had to answer a more basic question: who, exactly, are its customers? Because each business unit (cable, wireless, Internet) had different lists of customers, the organization had no comprehensive view of the customer across product silos. And that lack of a true view of the customer hindered cross-promotional efforts.

The company decided to implement a customer data integration (CDI) project, which would consolidate subscriber information from different data sources into one master file. From that, they could discern who its most valuable customers were – and begin to intelligently market different services to customers.

The DataFlux Solution

To help create a single, unified view of the customer, the company turned to DataFlux data management technology. With DataFlux, IT and business users could collaborate to create a set of business rules that would guide the process of creating a master set of customer data.

Moreover, with DataFlux, those business rules could be used for both batch and real-time processing. So, once the company created a master customer reference database, they could enforce the business rules on a transactional basis to keep high-quality data within the master customer database.

The Results

Business users within the communications company used the unique DataFlux GUI-based interface to profile data to uncover anomalies, errors or inconsistencies. The DataFlux single-platform architecture allowed the business users (also known as data stewards) to immediately begin building rules to correct data problems.

DataFlux technology saves all business rules to the same metadata repository. Once established, the schemes and rules created by one business user can be replicated across the enterprise. Instead of duplicating efforts across business units, the company could effectively create routines once and reuse them over time.

After the initial project, the company had a clean, accurate customer master file – and now knows what products its customers have across different business units. That information is invaluable as the company targets cross-sell opportunities across business lines. Customer service reps can quickly resolve customer issues - and spend more time helping them select the products and services that make sense for their lifestyles.

The Business

Headquartered in Cincinnati, Cintas Corporation provides highly specialized services to businesses of all types throughout North America. Cintas designs, manufactures and implements corporate identity uniform programs, and provides entrance mats, restroom supplies, promotional products and first aid and safety products for over 500,000 businesses.

The Challenge

Like any company with a large, widespread customer base, Cintas knows that communication with customers about new products and offerings can be a daunting task given the fluid and dynamic nature of customer data. Companies change addresses. Contacts within organizations come and go. Businesses merge with or acquire other businesses. As a result, customer data – the foundation of Cintas’ sales and marketing efforts – is in a constant state of flux.

Cintas is also looking to market its products and services to new target audiences. As a result, the company needs to remove existing customers from prospect lists to avoid confusing their current client base with messages intended for new contacts.

The DataFlux Solution

Cintas chose DataFlux to ensure that information for various mailings was consistent, accurate and reliable. With DataFlux, they can quickly assess the quality of their customer data – and compare existing data to third-party marketing lists to remove existing customers from prospect communications.

“DataFlux technology allows our data analysis group to conduct in-depth data matching and standardization techniques,” said Becki Wessel, manager of marketing databases for Cintas. “We can use the pre-built standardization schemes to identify and resolve duplicate, inaccurate or invalid data. Or we can create our own standardization routines, which helps us tailor the technology to meet the specific requirements of an individual project.”

Cintas also utilizes DataFlux technology to correct and validate lists of prospective customers that are transmitted to sales staff. Instead of receiving raw data from prospecting efforts, DataFlux helps Cintas analyze and correct information before sending this data to the sales force.

The Results

With DataFlux, Cintas now has tighter control over customer and prospect data. By removing duplicate or inaccurate records from existing data sources, the company lowered the percentage of unnecessary or wasted communications. The improved customer data translates to identifiable savings in reduced production and mailing costs. Removing existing customers from prospect mailings led to even more savings from database marketing efforts.

For the sales group, better customer data means that they receive more accurate and timely leads on a regular basis. Instead of contacting an existing customer or an unreachable address, the sales force spends more time working actionable leads, leading to productivity gains throughout the sales cycle.

“At Cintas, customer data is a crucial element to any of our outreach programs, whether driven by marketing or sales,” Wessel said. “DataFlux's sophisticated matching technology helps us quickly inspect and correct new and existing data. With DataFlux, we are confident our customer and prospect data is a useful tool that helps us communicate more effectively with our target audiences.”

The Business

BMC Software, Inc. is a leading provider of enterprise management solutions that empower companies to manage IT from a business perspective. Delivering Business Service Management, BMC Software solutions span enterprise systems, applications, databases and service management.

The Challenge

Like many growing companies, BMC Software has been adding customer information to its primary customer relationship management (CRM) system for years. Although the company was a longtime Siebel customer, data quality was an issue. Different employees added data to the company’s CRM system, often creating non-standard or duplicate records.

The impact of inconsistent, inaccurate and unreliable data directly impacted the sales team’s productivity. If the sales team had poor-quality data in the CRM system, they would not be able to communicate effectively with customers. Additionally, bad data could lead to inaccurate sales forecasts and affect other aspects of new business efforts.

“Put simply, data quality has a dramatic effect on any of our customer-facing initiatives,” says Kelly Williams, data analyst for BMC Software, “We needed a solution capable of codifying our business rules for data quality – and then enforcing those in both batch and real-time environments.”

The DataFlux Solution

To meet the growing need for high-quality data, BMC Software began a data quality and data integration program designed to identify and resolve duplicate records (in batch and real-time). The data improvement initiative also concentrated on validating addresses – both primary and secondary addresses – to help build a more unified view of the customer.

BMC Software selected the award-winning DataFlux suite of applications designed to help organizations analyze, improve and control corporate information. The company uses DataFlux to visually design workflows for data quality and data integration rules. DataFlux technology provides an environment to utilize those rules via service oriented architecture (SOA) in real time, allowing the company to find and resolve poor-quality data before it reaches the application.

The Results

As BMC expanded its sales efforts, DataFlux provided high-quality data that fueled better outreach to prospects and customers. Now, as new data enters the Siebel application, DataFlux real-time data quality and data integration rules ensure that a new customer or a new account isn’t a duplicate of an existing record. If it is a duplicate, the user has the ability to just append the record of an existing customer.

“With DataFlux, we have the power to look at more than the primary address for matching accounts,” Williams says. “DataFlux was the only vendor that allowed us to use secondary addresses and other details to match duplicates.”

DataFlux technology also gave managers and executives more confidence in sales forecasts and pipeline projections. Now, the list of prospects is more consistent and reliable, leading to a more accurate view of the sales cycle.

“Everyone in sales appreciates a more realistic view of the prospect base,” Williams says. “In that way, adding DataFlux to our CRM efforts has enhanced our ability to meet customer demands – and find ways to grow into new markets.”

The Business

Bell Canada Enterprises (BCE) is Canada’s largest communications company, providing wireline, wireless, data/Internet and satellite services throughout Canada and arts of the United States. With over 4 million wireless subscribers, Bell Mobility supports a complete range of wireless communications solutions — including PCS and cellular, mobile Web browsing, SMS text messaging, paging, and airline passenger communications services.

The Challenge

To gain new subscribers, Bell Mobility uses telemarketing and direct mail campaigns to contact and engage prospective customers. Bell Mobility conducts a number of these highly targeted campaigns annually, typically receiving the prospect data lists from external sources. Because accurate information is essential to any direct marketing campaign, the company had to perform careful and thorough matching, cleansing and de-duplication of this third-party data before each campaign. The company also relied on outside vendors to perform address verification before each mailing.

“Traditionally, we used SQL code to create the matching procedures that helped us eliminate duplicate entries,” says Jim Gallagher, database manager with Bell Mobility. “This was a time-consuming and labor-intensive process, and we needed a better tool to quickly and effectively build an effective data quality process for contact lists.”

The DataFlux Solution

Having better, more accurate contact information meant the company could more intelligently target its direct marketing efforts, improve response rates and build better customer relationships from the beginning by more completely anticipating customers’ needs.

To help it meet these goals, the company chose the award-winning suite of DataFlux data quality tools. With the intuitive DataFlux graphical user interface, business users were able to quickly and easily create data matching, cleansing and de-duplication routines. The flexibility of DataFlux technology also ensured that data from multiple sources could be matched and standardized before being integrated into the master list, eliminating duplicate prospect records and enhancing the quality and accuracy of the mailing and telemarketing lists.

The Results

By performing extensive data quality routines on new and existing contact information, Bell Mobility provided more reliable data to its telemarketing and direct mail groups, allowing them to contact more of their targeted audience than in previous campaigns — and spend less time on inappropriate contacts.

Armed with this better prospect data, the company was able to add over $1 million CAD in annualized revenue through more effective database marketing campaigns — for a return on investment (ROI) of over 1,300 percent. Additionally, by no longer outsourcing address verification for its targeted mailing lists, the company found it was saving an average of $5,000 CAD per mailing campaign.

“The effectiveness of our database marketing programs is based on the quality of data that we have,” Gallagher says. “DataFlux allows us to create cleaner and more accurate lists that drive our marketing programs. Plus, we could find ways to make the management of customer data more efficient, helping us eliminate some unnecessary costs at the same time.”

The company has also found new uses for DataFlux technology within its systems. Discovering that DataFlux data monitoring capabilities could be used for natural-language monitoring, Bell Mobility was able to use DataFlux technology to parse and sort SMS text message communications with its clients. Through a DataFlux monitoring routine, Bell Mobility could automatically forward the messages to the appropriate queue, allowing a resource that had previously been dedicated to manually sorting these messages to take on more cost-effective tasks.

The Company

Barclays is a major global financial services provider with an extensive international presence in Europe, the USA, Africa and Asia. With over 300 years of history and expertise in banking, Barclays operates in over 50 countries and employs over 155,000 people. Barclays moves, lends, invests and protects money for over 48 million customers and clients worldwide.

The Challenge

With new government regulations on the horizon, Barclays chose to take a proactive approach to compliance with an enterprise data governance program. The primary driver for the project was to assure the quality of data ultimately reported to regulators and to demonstrate a robust, transparent and accountable process for generating regulatory reports in respect of the Basel II Capital Adequacy Directive. Additionally there were several areas where Barclays wanted to improve operational efficiency through data management:

• Barclays wished to gain a better understanding of the interrelationships between its 50,000 ‘buy-to-let’ mortgage customers, properties and their associations. Without the ability to pro-actively analyse interrelation-ships in this data, Barclays could not adequately manage risk within this customer segment.
• To comply with the Third Consumer Credit Act, the bank needed to complete a large data migration. There was a need to improve automation within the data migration process in order to cut costs and improve timescales.

The DataFlux Solution

Barclays deployed DataFlux technology as a central resource that monitors and reports on the quality of data across an extensive enterprise data warehouse environment. Using the award-winning GUI based design environment of the DataFlux data quality and data integration platform, business users at Barclays were able to easily design customisable business rules to govern data. DataFlux profiling and monitoring technology provided the ability for Barclays to understand its existing data assets and report on the quality of its data over time – a critical element for accurate and reliable regulatory reporting.

Kingsley James, senior data analyst, Barclays said, “Technically this solution allows us to easily profile and monitor our data, which has been extracted from more than 30 source systems and 12 million customer records. It’s a powerful capability we didn’t have before.”

Additionally, Barclays selected the DataFlux Accelerator for Customer Data Analysis to allow its business users to compare and contrast disparate data sets. It also allowed users to set thresholds of acceptability and weight the importance of different data fields, adding organization-specific context to Barclays’ data quality reports.

The Results

When developing its data governance framework, Barclays involved a wide range of business stakeholders to define policy and implement a documented process. The company has now proactively met the need to comply with its regulatory data quality needs. Working within existing IT infrastructure parameters and integrating seamlessly with Barclays data warehouse applications, the bank has utilized DataFlux to add crucial data governance controls resulting in a fully assured process for regulatory reporting.

Pankaj Mistry, head of data governance, UK Retail Risk, Barclays commented, “This project has enabled us to deliver high standards of data quality in our critical regulatory processes.”

Just six months after deploying the DataFlux solution at an enterprisewide level, the project has achieved a positive ROI and has enabled Barclays to reduce its annual IT integration costs by £500,000 when compared to tactical data management deployments. This innovation has enabled Barclays to respond more quickly in the event of suspected credit card fraud and improve operational efficiency.

Additionally, the project has enabled Barclays to better understand its 50,000 buy-to-let customers that hold mortgages. The company can now analyze this customer base identifying interrelationships between the individual customers, mortgages and properties. This process provides Barclays with the ability to better manage the risks associated with making multiple loans to the same individual. The project is planned to go even further to improve the quality of Barclays customer contact data which will increase the rate at which the operations division is able to reach customers on the first occasion.

Tony Fisher's The Data Asset: How Smart Companies Govern Their Data for Business Success provides the guidance not only for building the business case for data quality and data governance but also for developing methodologies and processes that will enable organizations to better treat their data as a strategic asset.

In the first paper in this mini-series we defined what data governance is, looked at why it is necessary and then discussed three key elements that need to be in place to lay the foundations of any data governance initiative. These three elements are:

1. Data governance processes
2. A technology platform for data governance
3. A set of data governance services that automate run-time data governance processes for master and transactional data in order to formally govern this data, no matter where it is used in the enterprise.

In this paper we will focus on the last of the three key foundational elements – the set of data governance services that execute on the data management platform to explore, analyse, improve and monitor data. In particular, we will discuss how data governance services can be systematically deployed in layers so that re-use of data services can guarantee consistency when automating the tasks needed to formally govern data on an enterprise-wide basis. The purpose of these services is to accelerate the time to production and guarantee rock solid master and transaction data throughout the enterprise.

Having understood the approach to implementing data governance services we will then look at how one vendor, DataFlux, is shipping pre-built services called DataFlux Accelerators to address the data governance problem.

Anytime data crosses an organizational boundary, it should be governed, whether you’re sharing data among business units internally or publishing data to customers, partners, auditors, and regulatory bodies externally. Furthermore, we now live in the “age of accountability,” which (among other things) demands stricter oversight for data usage, quality, privacy, and security. User organizations are under renewed pressure to ensure that compliance and accountability requirements are met as the scope of data integration broadens. In response to this situation, many organizations are turning to data governance.

TDWI’s definition of data governance covers most of its components and goals:

Data governance (DG) is usually manifested as an executive-level data governance board, committee, or other organizational structure that creates and enforces policies and procedures for the business use and technical management of data across the entire organization. Common goals of data governance are to improve data’s quality; remediate its inconsistencies; share it broadly; leverage its aggregate for competitive advantage; manage change relative to data usage; and comply with internal and external regulations and standards for data usage. In a nutshell, data governance is an organizational structure that oversees the broad use and usability of data as an enterprise asset.

The Four Imperatives of Data Governance

As you can see, there’s a lot to data governance. Luckily, it’s not as difficult to grasp as it seems, because the many goals and tasks associated with DG distill down to four imperatives, which in turn group into a pair of organizational imperatives and a pair of technical ones.

• Formal data governance helps make cross-functional decisions effectively.
• It helps identify data stakeholders and gives them a voice in establishing rules and policies for how information is managed and used.
• It provides a mechanism for orderly and thorough escalation and resolution of data-related issues.
• It brings together business and technology representatives with multiple perspectives to collaboratively examine issues and suggest controls.
• Data governance helps establish standards that contribute to increasing the value of information assets, to cost containment, and to compliance.

While these are common outcomes of data governance programs and projects, not all data governance efforts are equal. Some are large, involving many participants and areas of an organization, while others may consist of one facilitator/administrator and scattered input by others. Some data governance programs look only at strategic issues and decisions, while others dive into detailed needs and processes. And while some data governance programs may exist to support IT-centric efforts such as data warehouses, master data management (MDM) or metadata management projects, others may focus on bringing cross-functional perspective and power to the work of setting policy, aligning business rules and definitions, or supporting architectural decisions.

Regardless of the primary focus of a data governance program, there are two efforts that nearly every program is expected to support in some way: data quality/standardization and compliance.

How much attention should any data governance program give to these efforts? How much should be spent, and what is the expected rate of return or return on investment (ROI) for the involvement of data governance – especially in the area of meeting compliance requirements? When is it reasonable to measure ROI, and how do we go about measuring it when our data governance efforts do not directly result in revenue? In this paper, we’ll look at the role of data governance programs in supporting compliance efforts. We’ll look at the types of contributions they make, especially in the area of managing compliance costs. And we’ll introduce an ROI formula you can use in those circumstances where it’s important to quantify the value of those contributions.

However, even as companies struggle with an incomplete view of business-critical functions, companies are receiving more and more information via every conceivable venue. A 2008 IDC study found that by 2011, there will be 1,800 exabytes of electronic data in the world – about 1.8 zettabytes or 1.8 billion gigabytes.1 Even as this avalanche of data reaches the enterprise, the reality for most of these organizations is simple: “more is less.” More data is arriving at an organization, but they are less prepared to turn it into meaningful information.

Over the past 15-20 years, companies have attempted to manage this deluge of data and create a more efficient culture by implementing enterprise applications to manage different aspects of the business. The two most common – customer relationship management (CRM) and enterprise resource planning (ERP) – are increasingly relied on to manage customer data and supply chain data, respectively.

In a vacuum, these applications will work “as advertised” by helping maintain and control a segment of the businesses. Where CRM, ERP and other enterprise systems fail, however, is that companies do not operate in a pristine, unaffected environment. They install multiple CRM or ERP packages for different business units and divisions.

Organizations acquire new organizations – and all of the legacy data from these new business units. Data entry is sometimes hurried, and there is little, if any, training for employees charged with collecting information. The result is a chaotic, disparate and disjointed view of the enterprise. The answer for most organizations is to consolidate, migrate and modernize, expecting that a more coherent view of the enterprise will emerge if the data is centralized onto few applications. But while moving to a smaller number of applications will have incredible benefits, it also poses incredible risks. Companies who have already done major data migration or consolidation work often struggle with:

• Incompatible system designs and technologies – Moving data is never as simple as a copy-paste endeavor. Each system has peculiarities on how data is stored, how it is labeled, etc.
• Limited knowledge of what data exists, where it came from, and what it represents – Companies may have data that predates anyone in the organization. Without a cursory knowledge of what’s in the old systems, how can you make intelligent decisions on what to migrate and what to archive.
• Lack of standards on what constitutes “good” or “valuable” data – When modernizing a system, do you bring all data to the new system or just that which is most current? Is there a standard method for evaluating data characteristics like timeliness, consistency or validity? Without a business framework to govern these decisions, good data could get left out – or inconsequential data could populate the new systems.

All of these issues have the same root cause: inconsistent, unreliable and inaccurate data in the source systems that is capable of polluting the new application. This white paper will examine how data management technology can solve these issues through a three phase process:

• Analyze – Use data discovery and data profiling techniques to identify problematic data, verify data structures, evaluate data complexity and uncover any data characteristics that can affect the data migration process.
• Improve – Use data quality and data integration technology to match, merge and standardize data from across sources into a single, unified data structure within the target application.
• Control – Monitor data on an ongoing basis to understand when new data arrives that violates business rules or does not meet standards for data quality.

Companies now understand that information is key to overcome current challenges. Marketing cycles are shorter, regulations are ever-evolving, customers continuously expect more responsiveness to their needs, etc. these challenges present a mandate for companies today: Enterprises need to increase their agility - or risk falling behind. this need for agility is generally fulfilled by companies with plans to make a more intense and intelligent use of their operational data. That's where they begin thinking about master data management (MDM), which objective is to consistently define and manage the non-transactional data entities of an organization - also called reference data.

MDM should indeed be considered as a noteworthy solution for information management challenges. However, there is a considerable risk of missing out in the objective and entering the "tunnel effect". Think back on the huge corporate warehouse projects launched when business intelligence (BI) was still immature. These projects had great justification and business cases. Yet, just like you cannot exit a tunnel before you reach the end, a project deliverable of this kind of project is impossible before establishing a substantial foundation. Due to their vast scope and technically driven approaches, these projects needed a massive effort to deliver the initial report. Additionally, the reports delivered most likely corresponded to business needs formulated 10 to 15 months earlier. In the mean time the business had evolved and, with it, the associated needs. Moreover, the quality of the delivered information was also questionable. Not overcoming these issues meant the gradual disconnection of those projects with the business needs and ultimately many of these projects were abandoned because they never brought the promised business value.

Let’s assume that we have passed the critical initial stages of developing a master data management (MDM) program — we have clarified the business needs, assessed the information architectures, and profiled available data sets to identify candidate sources for master data objects. Having used our tools and techniques to determine our master data types and determine where master data is managed across organization applications, we are now at a point when we must consider bringing the data together into a managed environment.

At this point, we again we face a challenge. Despite the fact that we have been able to identify sources of master data, the underlying formats, structures, and content are undoubtedly different. Yet to accommodate the conceptual master repository, all of the data in these different formats and structures needs to be consolidated into a centralized resource that can both accommodate those differences and, in turn, feed back into those different representations. That implies the following:

1. There must be a consolidated master representation model to act as the core repository
2. Processes for capturing data, resolving the variant references into a single set, selecting the “best” record, and transforming the data into the repository model must be defined
3. Processes must be created to enable the publication and sharing of master data back to the participating applications

Accomplishing these goals is dependent on two things: creating a suitable and extensible model for the master repository, and providing the management layer that can finesse the issue of legacy model differences.

To meet the objectives of the MDM initiative, the data from participating business applications will eventually be extracted, transformed, and consolidated within a master data object model. Once the master data repository is populated, depending on the architectural style selected for the MDM implementation, there will be varying amounts of coordinated interaction between the applications and the master repository — either directly, or indirectly through the integration process flow.

Once an organization has decided to institute a data quality scorecard, what types of metrics should be used for data quality performance management? Too often, data governance teams rely on existing measurements as the metrics used to populate a data quality scorecard. But without a defined understanding of the relationship between specific measurement scores and the business’s success criteria, it is difficult to determine how to react to emergent data quality issues - and determine whether their fixing these problems has any measurable business value. When it comes to data governance, differentiating between “so-what” measurements and relevant metrics becomes a success factor in managing business use expectations for data quality.

This paper explores ways to qualify data control and measures to support the governance program. We will also examine how data management practitioners can define metrics that are relevant to the achievement of business objectives. For this, organizations must look at the characteristics of relevant data quality metrics, and then provide a process for characterizing business impacts in association with specific data quality issues. The next step is providing a framework for defining measurement processes in a way that reflects the business value of high quality data in a quantifiable manner.

Processes for computing raw data quality scores for base-level metrics can then feed different hierarchies of complex metrics, with different views addressing the scorecard needs of different constituencies across the organization. Ultimately, this drives the description, definition and management of base-level and complex data quality metrics such that:

• Scorecards reflecting business relevance are driven by a hierarchical rollup of metrics
• The definition of metrics are separated from their contextual use, thereby allowing the same measurement to be used in different contexts with different acceptability thresholds and weights
• The appropriate level of presentation can be generated based on the level of detail expected for the data consumer’s specific data governance role and accountability

Useful vs. “So-What” Metrics

The famous physicist and inventor Lord Kelvin’s quote about measurement – “If you cannot measure it, then you cannot improve it” – is a rallying cry for the data quality community. The need for measurement has driven quality analysts to evaluate ways to define metrics and their corresponding processes for measurement. Unfortunately, in their zeal to identify and exploit different kinds of metrics, many people have inadvertently flipped the concept around in a number of ways, thinking that “If you can measure it, you can improve it,” or even less helpfully, “If it is measured, you can report it.”

Why Data Governance Can Help

Data governance programs come in many “flavors.” Some concentrate on supporting compliance, security, and access to data. Some focus on supporting data quality. Others focus on enabling data integration, increasing the value of information assets, and enabling transformative efforts. Data governance programs may be tightly focused on a single repository, set of data, or business problem. Or, they may cast a wide net, seeking to implement standardization, auditability, and appropriate decision-making across an enterprise.

Regardless of the flavor of data governance, all programs eventually address certain activities:

• Defining/aligning policies, standards, and rules
• Establishing decision rights
• Setting data-related accountabilities
• Providing mechanisms for issue escalation/resolution
• Identifying data stakeholders and understanding their needs
• Communicating with data stakeholders

These are cross-functional activities, undertaken by representatives from across the enterprise. As a result, data governance programs and participants develop unique capabilities.

• They become aware of gaps and overlaps in management efforts
• They learn about software products and how they can be leveraged to achieve multiple goals
• They observe proactive, reactive, and ongoing processes
• They see where money is being spent, and they often see where costs could be trimmed

Following are seven value propositions for data governance. If even one applies to your organization, you may find that data governance can pay for itself simply by avoiding other costs. If more than one of these value propositions applies to your situation, you may find that your budget for data governance is some of the smartest money you’re spending these days.

The types of commodity classification taxonomies run the gamut, often providing hierarchy information to handle a variety of products, materials and services. Examples include:

• SKU (Stock Keeping Unit)
• UPC (Universal Product Code)
• APN (Australian Product Number)
• GTIN (Global Trade Item Number)
• UNSPSC (United Nations Standard Product and Service Code)
• eCl@ss (hierarchical system for grouping)
• AHFC (American Hospital Formulary Service)

Before talking about the management of commodity data, a definition of commodity is in order. Simply put, a commodity is a product that is in demand and is the same no matter who produces it. There is no difference across the market in a commodity product. This definition can be applied to crude oil, coal, salt, sugar, gold and silver. Commoditization occurs when a product (or a service) loses market differentiation. Generic pharmaceuticals, computer chips, and other electronic components also fall into this category.

So, commodity data is data that can be coded (and categorized) the same across industries, countries and organizations. For these products, a taxonomy is very useful. Taxonomy is the practice of classification of products, services or basically anything usually in a hierarchy of codes. The hierarchy allows for “drill-down” and summarizations at higher levels of the hierarchy (perfect for business intelligence). A sample taxonomy could be applied to motorized vehicles. A car is a subtype of a vehicle, but not every vehicle is car. There are trucks, SUVs, tractors and ATVs as well as passenger vehicles.

• What’s the state of most organizations’ data?
• How should organizations begin data quality initiatives?
• How do organizations “graduate” to higher levels of data maturity?
• How do technology, people, and process relate to data?

The last two years in retail banking have been turbulent times to say the least, particularly in the United States and Europe. Banks have been taken over by governments, government "rescue" loans or had to raise money from the markets to stay afloat. Some have even gone out of business. After enjoying unprecedented growth from 2001 to 2007, led by the booming mortgage market, growth in mortgage revenue came to an abrupt halt in 2008 as high-risk borrowers in the sub-prime market defaulted on their mortgages. The excellent 2009 World Retail Banking survey of 203 retail banks in 26 countries1 provides insight into what is happening in the retail banking industry. The survey looks at two main areas: mortgage profitability and bank charges (prices) for day-to-day banking services –two key sources of revenue in retail banking.

The Mortgage Crisis

In the area of mortgages, banks switched from lending out of their own deposits, to a credit model of operating whereby mortgage lending uses funds borrowed from the capital markets. Having access to these markets meant banks had much more capital available to lend than if they were dependent just on deposits alone. It is not surprising that fierce competition broke out among retail banks all pursuing aggressive mortgage revenue growth by marketing heavily to a wider prospect base while continuing to drop mortgage interest rates. The result was less and less mortgage unit profitability compensated by growth in volume of mortgages sold. Lowering interest rates made the cost of borrowing more affordable and attracted more borrowers. However more aggressive marketing - coupled with many retail banks having only product-level risk management systems, as opposed to a single view of customer level risk - meant that it was inevitable that higher risk customer acquisition occurred. In addition mortgages were bought on the markets, thereby expanding the global customer base. The moment the sub-prime mortgage market crashed, any bank operating a mortgage book based on a credit model found themselves facing rocketing cost of funds in the markets. This left some banks short of operating capital and with an increasingly less profitable mortgage book. In addition the threat of higher risk continued to rise. Many have even resorted to borrowing directly from the public by offering bonds at a lower rate than the markets in order to reduce cost of funds.

The World Retail Banking survey found that, despite reducing costs and generating revenue through other initiatives, retail banks could still not compensate for the reduction net banking income caused by mortgage interest rates being lowered to compete more aggressively in the market.

1. To increase revenue or the value of assets
2. To reduce costs or complexity
3. To support risk management or compliance

When you’re concentrating on increasing revenue, you’re probably paying a lot of attention to metrics such as return on investment (ROI). You’re also probably asking what the ROI is for data governance, data quality, metadata, master data, or other data-related programs, projects or ongoing processes. And you’ve learned that measuring value for these types of efforts isn’t always simple. Using the ROI metric can be challenging for two reasons:

1. Data efforts are sometimes two or more “degrees of separation” from actual hard-dollar benefits. If you want to calculate ROI for such efforts, you’ll need to use a modified ROI formula.
2. Data efforts are sometimes conducted for a different reason altogether. They’re used to test assumptions about data; these assumptions are baked into another project’s ROI or value proposition. In this case, another metric might be more appropriate – CIDDA (Confidence in Data-Dependent Assumptions).

So let’s look at what it means to be more than one “degree of separation” from a goal, when it makes sense to use ROI to measure value for supporting efforts, and when CIDDA might be used in place of or to supplement ROI.

“Degrees of Separation” from the Ultimate Benefit

Projects that are just “one degree of separation” from money are easy to understand. Consider, for example, a direct-mail campaign. Conduct the campaign, and you can expect a certain amount of revenue. If you know the costs of the campaign and your projected response, it’s simple to compute ROI for the campaign.

On the other hand, consider an effort to clean up customer data before conducting the campaign or an effort to integrate two data sets in preparation for the campaign. Both of these efforts are “two degrees of separation” from the ultimate benefit. They should result in a higher return for the campaign, so they deserve credit for their contributions. However, they don’t receive credit for the revenue that results from the mailing. Efforts that are two or more degrees of separation from a benefit can only claim credit for their own contributions.

Now consider the results of analysis by a data governance council – based on a true story. In this scenario, a marketing director comes into a council meeting with a problem. Her group conducts marketing campaigns in which they offer credit to consumers. Because her staff spends so much time manually filtering and combining data sets, she can only conduct five email campaigns per year instead of the six she wants.

She would like to automate some of that preparatory work, and the corporate IT group is willing to help. They estimate about 200 hours to help her set up automated routines, but they can’t get to her job for many months. Do any of the other members of the council have suggestions for her?

Thirty minutes of discussion by the council uncovers a different path she could follow. This path, with alternate sourcing of her data, requires only 40 hours of effort rather than 200 hours. Her own staff could do the work, so she could complete it faster, and she would be able to have six mailings after all.

Happy, she sits at the council table scribbling calculations in her notebook. After a few minutes she interrupts the discussion to announce her findings: the data governance program has just paid for itself for an entire year!

If you can’t answer these questions correctly, you may be searching for a way to address the need to understand and use your data more effectively and provide greater accountability for the capture, storage and usage of data. Data governance is a program that helps you discover the power of leveraging your data assets and gives your organization a clear picture of its performance. In essence, data governance helps control the valuable assets of data and information.

Data governance plays a fundamental role in any enterprise data management framework. An enterprise data management program is designed to manage information as an asset. Most enterprises carefully manage other assets (financial, physical, and human) but often overlook the immense value inherent in their data. Typically, if an organization is aware of the data it captures, stores and uses, it is only aware of the deficiencies of that data – not the ways that data can be transformed into valuable information. Instituting a data governance program helps with this transformation by providing a central focus for identifying and controlling the collection, storage and disposition of information resources.

Today, businesses are discovering that their success is increasingly tied to the quality of their information. Organizations rely on this data to make significant decisions that can affect customer retention, supply chain efficiency and regulatory compliance. As companies collect more and more information about their customers, products, suppliers, inventory and finances, it becomes more difficult to accurately maintain that information in a usable, logical framework.

The data management challenges facing today’s business stem from the way that IT systems have evolved. Enterprise data is frequently held in disparate applications across multiple departments and geographies. The confusion caused by this disjointed network of applications leads to poor customer service, redundant marketing campaigns, inaccurate product shipments and, ultimately, a higher cost of doing business.

To address the spread of data – and eliminate silos of corporate information – many companies implement enterprisewide data governance programs, which attempt to codify and enforce best practices for data management across the organization. Although the goal is clear – the quality of information must improve to support core business initiatives – there is no definitive roadmap for starting these projects.

For any organization, the first step to address the quality and value of corporate data is to take an honest assessment of the data management infrastructure. Through the Data Governance Maturity Model, organizations can identify and quantify precisely where they are – and where they can go – to create an environment that can deliver and sustain high-quality information. This paper explores:

• The major issues of building better data across the enterprise
• Ways to utilize the existing people, business policies and technology to achieve more effective data quality policies across multiple departments
• How to determine the maturity of an organization’s data management capabilities – and find a data governance strategy that fits the organization

Introduction

If there is one certainty in modern business, it is that change is inevitable. In a quest for competitive advantage, organizations simply have no choice but to implement frequent advances in their information systems. The need to migrate information from systems, departments or even whole organizations to a new technology and business environment is essential for remaining competitive and cost-effective. Data migration, so often the enabler for these corporate changes, is therefore not just a technical discipline but a business necessity. Achieve a successful data migration and your business will reap competitive advantages in record time, but get it wrong and the benefits afforded by the new system may be severely diminished.

This guidebook provides a series of practical tips and techniques for business leaders who wish to learn more about the challenges imposed by data migration projects on their particular area of the business.

Who Should Read This Guidebook?

Anyone who has direct responsibility for a business unit, department, organization, team or service that is affected by a data migration should read this guidebook. Data migration is as much about engaging and aligning the business as it is about the technicalities of moving bits and bytes of data.

If your data, staff, applications, services or customers will be touched by a data migration then you will find this guide invaluable for understanding how you can provide leadership to help support a smoother, more effective data migration.

Why Should You Read This Guidebook?

This guide will help you make better decisions before, during and after the data migration project. If you are not directly responsible for the data migration project, but still have a stake in its success, it will help you understand exactly what will be expected of you and your part of the business.

Many business leaders misunderstand the data migration process and as a result place themselves at risk.

By understanding how the business should engage with a data migration and what is expected of you personally you will be able to:

• Create an early plan and roadmap so you don't become a bottleneck
• Define the ‘rules of engagement’ within your area of responsibility so the business can function as normal whilst providing the correct level of assistance to the migration team
• Understand why data migration projects suffer a high failure rate and what you can do to help prevent this situation arising in your business
• Educate those around you so they also understand their responsibilities in the data migration process
• Ensure the migration implementation team understands your dependency on the project so they align their objectives with your business needs

• What data elements constitute our “master data?”
• How do we locate and isolate master data objects that exist within the enterprise? How do we assess the variances between the different representations in order to consolidate instances into a single view?

Because of the ways that diffused application architectures have evolved within different organizations, it is likely that while there are a relatively small number of core master objects used, there are many different ways that these objects are modeled, represented and stored. For example, any application that must manage contact information for individual customers will rely on a data model that maintains the customer’s name. Yet one application will track an individual’s full name, while others will break up the name into its first, middle and last parts. And even for those that track the given and family names of a customer will do it differently – perform a quick scan of the data sets within your own organization and you are likely to find “LAST_NAME” attributes with a wide range of field lengths.

SLAs are familiar to anyone with an IT background, but they are typically focused on issues of system availability, service turnaround and other issues. This paper discusses how implementing a DQ SLA via formalized processes can transform data quality management from a constant “fire-fighting” mode to a more consistent, proactive approach.

The objective of the data quality service level agreement is establishing data quality control. This relies on monitoring conformance to data quality rules define using agreed to dimensions of data quality, such as accuracy, completeness, consistency, reasonableness, and identifiability, among others. We will consider these dimensions of data quality, and the ways that data quality rules are defined. Despite the best efforts to ensure high data quality, there are always going to be issues requiring attention and remediation. As a result, identifying data errors as early as possible in the processing stream(s) supports the objective of the DQ SLA: notifying the right individuals to address emergent issues and resolving their root causes in a reasonable amount of time. We will look at the process of defining data quality rules, their different levels of granularity, approaches for introducing measurement processes, and choosing appropriate acceptability thresholds.

This paper will then consider the relevance of measurement and monitoring: defining inspection routines, inserting them into the end-to-end application processing, and reporting measurements. When the quality of data does not meet the level of acceptability, data quality issue events are generated, the issues are logged in a data quality incident tracking system, and the individuals specified in the data quality service level agreement are charged with diagnosis and remediation. Through this operational data governance, an organization can internalize the observance of the DQ SLAs, and consequently continuously monitor and control the quality of organizational data.

The consequences can be poor customer service, inept business processes, shipping or invoicing errors, lack of compliance, penalties from regulatory reporting issues and many others. Additionally, misinformed decisions by information consumers responding to industry market changes can have significant costs to and impact on the organization’s health.

This dilemma often results from organizations that fail to take advantage of the opportunity and initiative to improve data quality and metadata in the enterprise. This missed opportunity leads to increased time and expenses required to reconcile and audit data in the enterprise for accurate and reliable use as information. Through planning, design and implementation of data quality and managed metadata – as components of an overall enterprise data management framework – organizations can gain competitive advantage through effective and confident use of their information assets.

The migration to an organization that relies exclusively on master data management does not take place overnight; the shift evolves through a number of transitional information management stages. Recognizing that the process involves more than purchasing a software package or engaging outside solution vendors is the first step towards achieving the MDM evolution. But it is more than that – it means understanding the essential capabilities necessary for a successful MDM deployment and the degree of maturity of those capabilities necessary to make MDM actionable.

No “functionality list” completely captures the inventory of services that a specific business requires from its master repository. However, it is worthwhile to explore a high level enumeration of core MDM capabilities, and in this white paper we will provide a conceptual outline of technical MDM components. This white paper explores levels of maturity based on the ability to provide MDM services. By presenting the MDM component layers in terms of their maturity, enterprise architects can target a desired level of MDM maturity and develop a design and implementation roadmap that articulates the steps to take when assembling an MDM program.

MDM Basics

The proliferation of enterprise-level applications (along with expectation for shared, synchronized information) drives the need for the development of a single view of the key data entities in common use across the organization. At the technical level, the drivers and fundamentals of MDM can be summarized as processes for consolidating variant versions of instances of core data objects, distributed across the organization, into a unique representation. In turn, that unique representation is continually synchronized across the enterprise application architecture to make master data a shared resource. The result is a master repository of uniquely identified key data entity instances that are integrated through a service layer with applications across the organization.

Like many technology projects, the devil is in the details. To accomplish what may seem to be a relatively straightforward set of ideas, the organization must prepare for the technical, operational, and management challenges that will appear along the way. In fact, the deployment of an MDM solution could evolve through a number of iterations, introducing data object consolidation for analytic purposes as an initial step, then following on with increasing levels of integration, service and synchronization.

Understanding some of these challenges – and adopting a strategy to address them from the beginning of the program – will enable a savvy program manager to build a project plan that identifies key tactical milestones while providing a smooth transition towards the strategic end of a unified MDM repository. In this white paper, we will explore some expected challenges in implementing an MDM program, and provide some suggestions that can ease the transition to the MDM environment.

Challenges to Success

Introducing new technology always brings new challenges for any organization.The decision to deploy a master data management system is no exception. A centralized master repository of commonly-used and shared data sets can increase consistency, improve data quality, reduce management and development costs, and enable more effective operations and analytics.

However, transitioning from an organization with distributed data sets managed within lines-of-business to a model where replicated data is pulled under enterprise management might introduce some stresses to the overall environment. But understanding the nature of some of these challenges ahead of time allows for proper preparation and planning.

Most of the challenges stem from what might be considered a “shock to the system.” Too often, management views data as raw input to applications across distributed lines of business. Establishing an MDM program as part of an overall organizational data and information management strategy implies that senior managers believe in treating their information as an enterprise asset. In essence, the decision impacts everyone across the system – managers, data modelers, application developers, business clients, and even customers, all of whom may need to modify their behaviors.

While we won’t be able to enumerate all of the challenges to a successful MDM deployment, we can categorize those obstacles into three different segments: Organizational (i.e., people issues), Operational (transition and integration), and Technical (architecture, and migration) challenges.

As new data sets contribute information to the master data repository, proper data governance policies and procedures will ensure continuous data quality. However, integrating and consolidating data alone will not accomplish the objectives of MDM. Rather, organizations realize the true value only when the consolidated master data is integrated back into operational and analytical use by the participating applications – and the enterprise has a single, synchronized view its business data.

The abstraction of the data integration layer as it relates to business application development exposes two ways that master data is integrated into a services-based framework. Tactically, a services layer must be introduced to facilitate the transition of applications to use of a master repository. Strategically, the abstraction of the core master entities at a data integration layer establishes a hierarchical set of information services that support the rapid and efficient development of business applications. Both of these imperatives are satisfied by a services-oriented architecture (SOA).

In essence, there is a symbiotic relationship between MDM and SOA. A services-oriented approach to business solution development relies on the existence of a master data repository. At the same time, the success of an MDM program depends on the ability to deploy and use master object access and manipulation services. This white paper explores the symbiotic relationship between MDM and SOA – and how you can effectively create a strategy that encompasses both of these initiatives.

In a connected world, collaboration and sharing are key principals. In particular, the faster our networks are and the better our connectivity is, the more your organization will benefit from information sharing and operational collaboration. In turn, as we share more information among our partners, our connectedness is enhanced as well. It is not just systems that work better together, but the people managing those systems forge better working relationships, leading to more effective management of the business and ultimately, to competitive advantage.

However, the more we share information, the more we realize that years of distribution of computing power and business applications into vertical lines of business has led to “islands of information coherence.” Data architectures designed to support operational processes within each business application area require their own definitions, dictionaries, structures, etc., all defined from the aspect of that particular business application.

The result is that the enterprise is composed of multiple, sometimes disparate sets of data that are intended to represent the same, or similar, business concepts. Yet, to exploit that information for both operational and analytical processes, an organization must be able to clearly define those business concepts, identify the different ways that data sets represent those concepts, integrate that data, and then make it available across the organization. And this need has introduced a significant opportunity for organizational information integration, management and sharing.

In this white paper, we will discuss how these processes comprise a master data management (MDM) program. We will look at questions such as:

• What is master data management?
• What are the characteristics of “master data”?
• What are some architectural approaches to MDM?
• What are the organizational challenges of instituting an MDM program?
• How does data quality figure in?

This paper is the first in a series exploring the basics of master data management, the policies and procedures employed, and the tools and techniques used for a successful MDM program.

Therefore, for many organizations the selection of SAP packaged applications has been a strategic investment that is driven by the need to improve and underpin core operational and analytical business processes. However, to maximize the benefits of an investment in SAP applications it is also necessary to integrate them with non-SAP applications to increase process integration — and to share core master and transaction data created in SAP with other systems in other parts of the enterprise.

Whatever the investment in SAP, one thing is clear — the benefit from process improvement expected from such a strategic investment is not going to be down to just the applications and processes themselves. It is also dependent on the quality of data within these applications and the ability to synchronize changes to this data across SAP and non-SAP applications. In addition, most companies need to integrate SAP and non- SAP data to create common shared master data and common shared business intelligence in an enterprise data warehouse. Therefore, a critical success factor when implementing SAP is to not just focus on the packaged applications, but to also make sure that data quality and data integration are managed in order to yield the maximum return on investment. Without data quality and data integration, business processes will still be plagued by defects caused by inability to eradicate data errors, and an inability to integrate, consolidate, share and synchronize core operational data, master data and historical analytical data across the enterprise.

This paper looks at the SAP applications and SAP infrastructure technologies and the impact of poor data quality in an SAP environment. It then defines the requirements needed to support data quality and data integration for SAP customers and the components needed for enterprise data management in an SAP environment. The paper concludes by looking at how one supplier – DataFlux – is bringing products to market to support these needs.

Good data should be considered the lifeblood of any organization. If correctly managed, it will not only help companies comply with legislation, it will also deliver fundamental business value through operational efficiencies, cost savings, improved customer service and, ultimately, a competitive advantage. To survive and thrive, the financial industry must have the right processes in place to ensure that their data is accurate.

In the summer of 2009, DataFlux conducted a survey to understand data management trends in the financial services industry. The research had several goals:

• Benchmark the approach the industry is taking towards its data
• Understand the breadth and depth of data governance in this sector
• Discover what motivates data management strategies
• Determine what kind of rules the industry thinks should be introduced in the future to promote success in this sector

The statistics referenced in this white paper were gathered from an online survey that was administered by Lodestar Research, a third-party research firm. The survey was offered via email to readers of five leading financial industry publications. Almost 300 individuals responded to the 24-question survey, from various departments and levels of seniority.

Data quality and data governance: on the boardroom agenda

Organizations have long viewed tangible items such as buildings, products and people as “assets.” It is easy to calculate the value of a building and see how facility improvements or a lack of care can increase or decrease the value. The products on the shelves or the people working inside that building all bring revenue, so these items often have an assigned value within the company. However, data hasn’t always been seen in the same light, but this seems to be changing. The survey reveals that financial services companies are at the forefront of the movement to accentuate the value of data.

All businesses, no matter what their size, rely on data to record and analyse business activity. It is the lifeblood of any business operation. Data enters the enterprise during specific process activities, either through the keyboard, via electronic messages or via electronic files. It then flows throughout the enterprise to support every process activity from registering new customers and sales order taking to supplier procurement, product fulfillment, product delivery, invoicing and payment collection. Yet, when you boil down the complexity of business operations and look at data underpinning it in simple terms, there are two broad categories of structured data that any business relies on. These are:

• Master data
• Transaction data

Master data is simply the data associated with core business entities such as customer, employee, supplier, product, partner, asset, etc. This data can reside in many different systems. For example, customer data may reside in a sales force automation system, an e-commerce system, a marketing system, a billing system and a distribution system. Equally, product data may reside in product development systems, manufacturing systems, planning systems and storage systems. A trait of master data, therefore, is that subsets of it are needed in multiple systems to control continuity of business operations as processes progress throughout the enterprise.

Transaction data, on the other hand, is very simple and straight forward. This is the recording of business transactions such as orders in manufacturing, mortgage, loan and credit card payments in banking, and premium payments and claims in insurance. In retail, transaction data is product sales, either at point-of-sale terminals in stores or online. In aviation it is airline ticket sales.

Looking at corporate data in this context makes it look very straightforward. Both types of data together describe everything associated with core business activity. For example, Mr David Jameson (Customer) paid £0.89 on 21st January 2008 (the transaction) for a loaf of bread (Product) in the Oxford Street store (Store) in central London (Location). Here the combination of master and transaction data describes the business activity precisely.

Having understood the simple way in which master and transaction data record business activity, this paper focuses on the former of these, namely master data. More specifically, we will look at what it is, why it is needed, how to get started in managing it, and methodologies for implementing master data management. Master data management forms part of an overall enterprise governance program that aims to establish trusted data throughout the enterprise.

It was also found that over half of the companies showing interest in data governance have already received sponsorship for their programs. However, funding for these programs is being sought under the guise of other initiatives such as larger data management and business intelligence (BI)/data warehousing programs or as part of master data management (MDM) projects as opposed to a stand-alone data governance program. In terms of data that businesses are seeking to govern, the focus is on structured data rather than unstructured data. Customer, product and financial data are especially seen as high priorities.

In terms of implementation, organisational structure is still not well established. Many companies have data stewards spread across their business units but they are only undertaking their data stewardship duties on an ad hoc or part time basis. Frameworks, policies and processes for data stewards are not in place. And less than a quarter of the companies surveyed responded that they have a data governance council in place to approve new data attributes or decommission data. The survey also found the establishment of data governance processes and policies is still in its early stages with around 25% of companies having these in place. A slightly higher percentage of companies already have software in place to get them started on a data governance program but these tools are generally stand-alone products rather than being part of an integrated suite of tools.

Finally even though the scope of data governance projects is restricted to a specific type of data (e.g. customer) there is clear evidence that data governance initiatives are still only being undertaken in specific departmental or line of business areas rather than being tackled on an enterprise-wide basis. The problem with this approach is that customer data for example (which was flagged as the highest priority by respondents participating in this survey) may appear in several data systems - front office CRM (sales, marketing and service), finance and in distribution. These systems are found in different departments and may often span several lines of business. The same is true for product data. If customer and product data is to be brought under control, doing so in one department or one line of business is not enough. This vision must be across the enterprise. Companies also need to establish strong backing from enterprise level executives if they are to drive an enterprise-wide data governance program.

However, when errors in the data are identified, the data stewards responsible for the data must take action. This paper reviews the pieces of that immediate action plan: the triage and analysis tasks performed by data quality analysts or data stewards when an issue is identified and logged in the data quality incident tracking system. This includes:

• Evaluating and assessing the issue and determining the scope and extent of the problem from both a business impact perspective and from an operational perspective
• Reviewing the information process map to determine the likely locations for the source of introduction of the problem
• Determining strategies for correcting the problem
• Researching strategies for eliminating its root cause
• Planning and applying operational aspects, including data correction, monitoring, and prevention

Evaluating criticality, assessing the frequency and severity of discovered issues, and prioritizing tasks for remediation are all part of the data steward’s role. Formalizing the different tasks to perform when issues of different levels of criticality occur will reduce the effort for remediation while speeding the time to resolution.

The Data Quality Service Level Agreement

An emerging trend in the data quality arena is the concept of a DQ SLA, which provides a valuable link between the IT and business sides throughout a data quality or data governance effort. A DQ SLA is a contract between a data provider and a data consumer that specifies the data provider’s responsibilities with respect to different measurable aspects of what is being provided, such as availability, performance, response time for problems, as well as reasonable expectations for response and remediation when data errors and flaws are identified.

These organizations have invested heavily in data-driven initiatives such as customer relationship management (CRM) and enterprise resource planning (ERP) applications, hoping to build more profitable relationships with customers and hold product-related expenses in check. Despite good intentions, however, most of these applications have faltered due to inefficient, outdated data. In fact, industry estimates show these projects fail or go over budget up to 75 percent of the time.

The very foundation of CRM and ERP systems is the data that drives these implementations. Beginning a data-driven initiative without first understanding the existing, underlying data is like repairing an automobile without first understanding the problems inside the engine. To repair the engine, the mechanic first has to understand the breadth and depth of the problem.

Successful data quality begins with a clear understanding of the integrity of your current data. Data profiling, also called data discovery, gives you the diagnosis of your existing data to begin building a successful data improvement and integration effort through consistent, accurate and reliable data throughout your organization.

The Problems with Data

Data problems abound in most organizations. Some of the more common problems today include: outdated, inconsistent, missing, orphaned or duplicated data; data anomalies or outliers; and data that does not meet specified business rules. Before you begin any data quality improvement initiative, you should ask and address some key questions:

• Do you trust the quality of the data you are using in this initiative?
• Does the data for this initiative conform to the business rules monitoring process you expect to set up later?
• Will the existing data support the needed functionality?
• Is the data you are using complete enough to populate the needed data repository?

Engaging in any data initiative without a clear understanding of these issues will lead to large development and cost overruns or potential project failures. The effect can be incredibly costly. For example, one company spent more than $100,000 in labor costs identifying and correcting 111 different spellings of the company AT&T. Data problems within your organization can lead to sub-standard customer relations, wasted expenses, poor decisions, lost sales, and ultimately, failed businesses.

The wave of workgroup and desktop computing in the 1980s led to distributed data management, resulting in applications supporting line of business operations with similar requirements yet variant models, representations and management of information objects. Data replication across mainframe, servers and the desktop has led to ambiguity in representation and semantics associated with implementing business concepts.

Initiatives in centralization (such as data warehousing) intend to consolidate organizational data into an information asset to be mined for actionable knowledge. Although centralization of information for analysis and reporting has great promise, a new challenge emerges: as data sets are integrated and transformed for analysis and reporting, cleansing and corrections applied at the warehouse imply that the analysis and reports may no longer be synchronized with the source data, suggesting the necessity for having a single source of truth for all applications – not just analysis and/or reporting.

Over the past ten years, data profiling, data cleansing and matching, and data integration tools have matured in concert with a desire to aggregate and consolidate “master data,” but today’s master data management (MDM) initiatives differ from previous attempts at enterprise data consolidation. An MDM program creates a synchronized, consistent repository of quality master data to feed enterprise applications. Successful MDM solutions require quality integration of master data from across the enterprise, relying on:

• Inventory and identification of candidate master data objects;
• Resolution of semantics, hierarchies and relationships for master entities;
• Seamless standardized information extraction, sharing and delivery;
• A migration process for consolidating the “best records” for the master repository;
• A service-oriented approach for accessing the consolidated master directory;
• Managing enterprise data integration using a data governance framework.

These tasks depend on traditional data quality and integration techniques: data profiling for discovery and analysis; parsing; standardization for data cleansing; duplicate analysis/householding and matching for identity resolution; data integration for information sharing; and data governance, stewardship, and standards oversight to ensure ongoing consistency. Essentially, data profiling, data integration and data quality tools are the three pillars upon which today’s MDM solutions are supported. Vendor and customer analyses indicate that:

• Many master data programs have evolved from customer data quality, product data quality, data assessment and validation, and data integration activities.
• MDM solutions are triggered by the introduction of data quality activities to support technical infrastructure acquired for a specific purpose (e.g., enterprise resource planning or customer relationship management).
• Data governance is a common success theme for MDM.

During the conversations and interviews with both vendors and their customers, recurring themes led us to draw some conclusions about the evolution of successful master data management initiatives:

• There is a significant bidirectional influence between data quality and master data management.
• Customer data still is the main focus of MDM activities, but product information management is rapidly growing in importance.
• Formalizing data governance is a critical success factor for MDM.
• Master data management is not always about consolidation of data.
• The need for semantic integration has driven users to adapt existing tools for broader purposes than originally intended.

As organizations increasingly focus on master data integration, their reliance on readily available technologies, couched within an enterprise governance framework, will continue to drive both analytic and operational productivity improvement for the foreseeable future.

Almost all executives who manage a business or part of a business are tasked with trying to improve business performance and keep customers loyal. In fact, most are compensated for improving profitability, driving revenue growth and improving the quality of customer service. All of these are key metrics closely monitored by senior business managers and they often want to find and eliminate defects that impact on performance. It is strange, therefore, that although business performance is deemed critical, few business managers take time to understand how performance is impacted by bad data that enters and spreads though across parts of their business operation. This might seem a minor point on the surface but let us consider two examples of businesses from financial services and from manufacturing severely impacted by data not managed and kept in a high quality state.

In a retail bank, risk management is absolutely key. In fact, risk management is the number one issue for many retail banking executives as they strive to reduce the cost of losses and widen profit margins. Although all banks would claim publicly that they have risk management under control, many are also dissatisfied with the losses they incur.

This problem is often rooted in the way many banks have historically managed risk which is at a product level and not at a customer level. Product-level risk management means that a bank may have many different product-level risk management systems, each of which is limited to monitoring risk exposure of customers of single product. This means they can see customer risk exposure for mortgages, loans or credit cards.

However, understanding full risk exposure at a customer level for all products that a customer owns has often not yet been achieved. The limitations of “stand-alone” product-level risk systems are well-known and these limitations can have serious implications on business performance especially when customer data across such systems conflicts. It is often also the case that each product-level risk management system also feeds a corresponding product-level marketing system. Given that customer data in different product-level risk management and marketing systems often conflicts, inconsistent customer treatment can occur.

Also, customer data is not integrated across such systems making it difficult to fully uncover the true picture of a customer’s exposure, payment behavior, etc. The result can be increasing losses due to an incomplete risk picture and inaccurate, conflicting marketing campaigns caused by stand-alone product risk systems that pass conflicting and overlapping customer data on to different marketing systems. These end up recruiting bad-risk customers while the best customers are enticed away by better deals offered by competitors. Essentially, data ownership and management of data quality are critical to getting risk management under control. Data defects and incomplete data can quickly result in mounting losses and inaccurate marketing. One bank in particular lost 16% of its mortgage business in the last 18 months due to these problems while losses mount in its credit card business.

As a second example, consider manufacturing. Many manufacturers are at the mercy of large powerful retailers, which have a much better understanding of demand than manufacturing. With increasingly larger amounts of manufacturing business coming from large retailers, many manufacturers see it as critical that they can align their processes with that of their large retail customers to keep them happy. This includes the ability for a retailer to order centrally for all or a subset of their stores, and also the ability for a specific retailer store to order locally from a specific manufacturing site.

Supporting both central and local ordering imposes added problems on manufacturers. For example, each manufacturing site then has to collect order data from central ordering and local ordering systems to get a complete picture on what to manufacture at each site. If data is not well managed in this scenario, the business performance can be severely impacted. In one such case, a manufacturer that attempted to implement central and local ordering to keep a retailer happy later found it difficult to process orders correctly at each manufacturing site due to data being in multiple places not flowing between systems.

The business impact from lack of data ownership and control of how order data flowed in business operations was considerable. Conflicting and duplicate business processes at each manufacturing site were causing data errors leading to mistakes in manufacturing, packing and shipments. All of this resulted in low customer satisfaction as deliveries turned up late, with incorrect products being delivered. This finally led to the loss of their biggest customer and a lucrative €200 million contract. Integrated common processes and formal enterprise data management were both critical to reducing costs and preventing process defects across all manufacturing sites. Realizing this after loss of business was a bitter pill to swallow.

These are just two examples of a lack of data management, data ownership and data quality having a major impact on business performance. Looking at the impact of loosely-managed data in this context puts the importance of data ownership and accurate, timely integrated data much further up the priority list in helping improve business performance. Data has a significant impact on a business, and IT has a duty to the business to manage it well and uphold data quality to avoid business problems caused by data defects in business operations.

Issues of data integrity also complicate the issue of compliance. Compliance relies on rock-solid data and trusted metrics produced in regulatory reporting. Therefore, data ownership, data quality and formally-managed data is high on the agenda of CFOs and CEOs who are held personally accountable if their company is found to be in violation of regulations.

This paper is the first of a three-part series that attempts to address the problem of data ownership and enterprise data management. It will define data ownership and the importance of managing data quality as data flows throughout the enterprise. The other two papers in the series will raise awareness of technology that allows companies to delegate the management of enterprise data quality and also what companies need to consider when implementing a common data quality strategy across the enterprise.

In the first white paper of this three-part series on data ownership, I referred to data as an enterprise asset and something that needs to be robustly managed on an enterprise-wide level. This means maintaining data quality while ensuring that data remains clearly understood as it flows between applications and processes during business operations. I also discussed how data ownership is about enterprise data management, and that key requirements for enterprise data management included the need to:

• Establish a common suite of technologies for end-to-end data management
• Dedicate IT personnel to enterprise data management
• Establish policies for data governance

In this second white paper on data ownership, I want to look more in-depth at the first of those key requirements, specifically how an enterprise-wide standard suite of technology can help companies solve the problem of data ownership and enterprise data management.

Corporate Nirvana

Over the years, as systems have rolled out and we have moved from one era of computing to another, the job of managing data has not gotten any easier. In fact, most companies would say that it is getting progressively harder. Looking back to the 1990’s, it was the client/server computing and open systems era that spawned the distribution of applications and databases onto stand-alone servers across the enterprise. I recall one executive commenting that they understood the need to distribute the application logic, “but whose idea was it to fracture the data and scatter it to the wind?”

In operational systems, the client/server and open systems era gave rise to duplication of functionality and fractured subsets of operational data across multiple operational systems and data stores. This in turn caused inefficiencies and overspending in many operational processes. While we have known about this problem for years, we have not been very successful in dealing with it. Some companies purchased enterprise application integration (EAI) products and asynchronous message queuing products to try to keep data synchronized. While this had had some success, applications with no application programming interfaces (APIs) could not be integrated with EAI software, and so batch-file update processing continues to be needed.

However, the arrival of the Internet has made business executives realize that there is potentially a way back to data simplicity. If the web can allow everyone to gain access to common processes, common application services and common information via a web browser irrespective of their location, then why do companies need multiple versions of the same process, multiple applications with duplicate application functionality, and multiple versions of fractured operational data? Corporate nirvana would be to share common operational data across applications and access common services via the Web. It would also be better if core business entity master data was maintained in one place, with changes forwarded to applications that need this data.

This realization that there is a possible way out of the fractured complexity of operational systems has sparked a massive demand for operational data consolidation and integration, as well as master data management. Add to this the need for compliance, and you quickly see why companies are also pushing for common understanding of data with common data names and common data definitions, and also raising the priority of data quality to get trusted and accurate information.

Corporate nirvana would look something like Figure 1 (below) where operational applications share common operational data and use common services to maintain master data such as customer, product and employee attributes. In addition, the operational data and the historical data in business intelligence (BI) systems would be based on common data names and data definitions (metadata) so as to guarantee common understanding. Enterprise data management (EDM) services are available to request data integration, data quality checks and metadata.

So far in this three-part series on data ownership, I have discussed what data ownership is, why it is important, what the key requirements of enterprise data management (EDM) are and how companies can address the data management problem by standardizing on a suite of technologies, which I referred to as an EDM suite. In this, the third and final paper in this short series, I want to look at what needs to be done from a strategy perspective to be able to establish personnel and procedures for enterprise data management, and what needs to be done in order to leverage the technologies available in an EDM suite to get maximum return on investment.

Enterprise Data Management Strategies

In the first paper of this series, we outlined three key requirements for enterprise data management. These requirements are:

• Establish a common suite of technologies for end-to-end data management
• Dedicate IT personnel to enterprise data management
• Establish policies for data governance

Having looked at the first of these already in the second paper, we now turn our attention to organizational structure and data governance – concepts that are fundamental to any data management strategy.

Organizational Structures for Enterprise Data Management

One of the key appointments any company can make to help get their data under control is the position of a Chief Data Architect. This is often a position overlooked in IT and sometimes not well understood by business. If it does exist, this person must have a business mandate to cause change so that data can be brought under control. Fundamentally, the job of a Data Architect is to understand how data is used in business on an enterprise-wide basis and to formally define the data used. This individual is also responsible for setting policies and procedures for the use of that data, for maintaining data quality, and for ensuring a common consistent understanding of what data means. Ideally, a Data Architect should have extensive experience in the vertical industry that he or she works so that they can clearly discuss data in the context of its business use. Data Architects must also have expertise in data management skills such as:

• Implementing data standards and establishing policies for developers and business users, including defining standard enterprise-wide data vocabularies
• In-depth understanding of the relational model and navigating XML schemas
• Data modeling and modeling techniques such as normalization and star schema multi-dimensional modeling, as well as some fluency in the use of data modeling tools
• Logical and physical database design
• Data profiling and defining rules for data content cleanup
• Understanding of the requirements that regulations and legislation impose on data for the purposes of compliance

Ideally, data architects should have an enterprise-wide remit in the sense that they need to operate across all lines of business when managing data. This is especially important in setting strategy and patterns (best practices) around specific data management processes such as:

• Master data management
• Data profiling and data monitoring
• Data migration and consolidation
• Data replication and change data capture
• Data synchronisation
• Data federation
• Data warehousing and data aggregation
• Data security
• Taxonomy design

Many companies are starting to create centralized IT expertise in business integration by creating Integration Competency Centers so that IT professionals responsible for different types of integration are able to coordinate their work. The data architect is at the center of data management, data quality and data integration and should be a key member of any integration competency center initiative. Figure 1 shows five levels of business integration. Data and metadata integration (and management) underpin and are a key piece of any business integration initiative.

But an organization cannot simply cleanse and improve the quality of its data, then expect this data to be a static resource over time. Data is a fluid, dynamic and ever changing resource, so data quality is not a once-and-done activity. The need for data quality never goes away. It requires continual oversight.

Data, in essence, reflects the changing world around you. Customer records become obsolete as people move or switch jobs. Catalogs for products and supplies become outdated. Without a commitment to ongoing data quality, the integrity of an organization’s data quickly becomes incorrect or invalid as it reaches core applications.

Data monitoring has become a key component of a complete data quality integration practice, giving you the tools you need to understand how and when your data strays from its intended purpose. Monitoring also helps you identify and correct these inefficiencies through the automated, ongoing enforcement of customizable business rules. Data monitoring ensures that once your data becomes consistent, accurate and reliable, it remains that way over time, giving you confidence when making information-based decisions for your organization.

The project leader plays a critical role in ensuring a successful data migration but exactly what responsibilities and activities should project leaders fulfill?

This guidebook provides the answers with a series of detailed sections including essential activities such as planning, forecasting, risk management, team selection, communication and collaboration.

Introduction

Data migration projects can pose significant challenges for project leaders. As a discipline, data migration is still in its relative infancy. There is a noticeable lack of industry associations, formal education and published best practices compared to similar disciplines such as data warehousing, data quality and data integration.

There are many pitfalls awaiting the poorly skilled and inexperienced project leader and as a result far more data migration projects fail than succeed. The aim of this guidebook is to help reverse this trend by providing expert advice and a set of best practices to help chart your data migration project to future success.

Who Should Read This Guidebook?

This guidebook is of value to data migration project leaders but will also benefit:

• Project sponsors
• Data quality analysts
• Software developers
• Business analysts

In fact, anyone who needs to interact with the project will find the guidebook useful because it provides a thorough account of the activities and responsibilities required throughout.

Why Should You Read This Guidebook?

The practical techniques and advice found within the guidebook are based on many years of experience within the sector. As a result, this guidebook will help the reader effectively plan and react to the many obstacles and challenges a typical data migration project can present.

In short, this guidebook will help you create a more successful data migration project.

One of the major values of a master data management (MDM) program is that, because it is an enterprise initiative, a successful initiative will be accompanied by the integration of a data governance program. As more lines of business integrate with core master data object repositories, there must be some assurance of adherence to the rules that govern participation. Yet while MDM success relies on data governance, a governance program can be applied across different operational domains, providing economies of scale for enterprisewide deployment.

There are many different perceptions of what is meant by the term “data governance.” Data governance is expected to address issues of data stewardship, ownership, compliance, privacy, data risks, data sensitivity, metadata management, MDM and even data security. What is the common denominator? Each of these issues revolves around ways that technical data management is integrated with management oversight and organizational observance of different kinds of information policies.

Whether we are discussing data sensitivity or financial reporting, the goal is to integrate the business policy requirements as part of the metadata employed in automating the collection and reporting of conformance to those policies. Especially in an age where noncompliance with external reporting requirements (e.g., Sarbanes-Oxley) can result in fines and prison sentences, the level of sensitivity to governance of information management will only continue to grow.

However, few companies have the ability to gain a comprehensive perspective of the products and services purchased and their associated providers. This confounds the ability to identify opportunities for improvement, and wasteful and duplicate spending can continue unabated. The difficulty in gaining this enterprisewide perspective is complicated by a number of factors, such as:

• There are often multiple systems used during the procurement process. With data spread across different data silos, it is difficult to consolidate spend data to provide summarizations across providers, products or commodity types.
• Different vendors and providers use variant product and service identifiers and descriptions. The inconsistent naming and identification introduces challenges when analyzing purchases by product or product category.
• Often, transactional data associated with purchasing is missing important characteristics that are used to influence and inform both the strategic and the operational decision-making processes for procurement.

Even when improvements such as negotiated product prices have been identified, ensuring that the negotiated savings can be achieved in practice require additional visibility into purchasing, supplier fulfillment and delivery data. Yet, according to an Aberdeen Group study, the top challenges for spend analysis include “poor data quality,” “too many data sources,” and “lack of standardized processes.” In essence, the biggest challenges to procurement improvements have to do with information, and the benefits of spend analysis can only be achieved when the spend analysis tools have access to the right data.

In this paper, we look at the business drivers and organizational objectives of a spend analysis program, and then consider establishing performance indicators and associated metrics for managing the efficiencies and realizing cost savings. The paper then reviews spend analysis techniques along with the data management procedures necessary to enable the process. Last, we consider some of the most important challenges and associated techniques for driving a successful spend analysis program.

When reviewing the popular literature on customer data integration and master data management (MDM), a frequently recurring business driver is the ability to establish a “360-degree view of the customer.” The allure of this appealing concept has turned it into the holy grail of comprehensive customer intelligence. A multitude of analyst articles, reports, and white papers all extol the virtues of the 360-degree view, and “achieved a 360-degree view” and “enhanced customer analyses” are ranked among the highest benefits from customer data integration.

Beyond the marketing hype, it’s important to examine the term and find the reality behind the buzzword. For instance, what does it mean to have a 360-degree view of a customer? What value drivers are positively affected by full-bore customer insight? How do you know if your company needs it? How do individuals exploit this capability? This paper is intended to explore these questions – as well as examine ways to assess business needs and master data modeling questions – to determine how the 360-degree view of the customer is supported by MDM and customer data integration.

To help you get started, the paper will provide a list of questions that should be considered and reviewed during any project designed to deliver a 360-degree view. By using these questions for preliminary guidance, one may clearly articulate where the business can derive value from the 360-degree view, determine performance metrics and thresholds, and improve the chances of success. The paper then explores how information sharing and identity resolution combine to show the connection between MDM and enterprise customer-centricity.

What is a “360-degree View of the Customer”?

The concept of the “360-degree view” emerged from customer relationship management (CRM) projects. From an abstract standpoint, the concept of the 360-degree view of the customer is quite appealing, especially when the term is used to mean various aspects of “knowing everything about each customer.” It suggests a comprehensive view of all information about each and every customer that is available to users both internally and externally. Depending on the context, this can range from a single integrated view of all customer data to a single integrated view of all customer activity, logging all corresponding interactions, all facilitated by automatically capturing all customer touch points through all channels and sharing the customer information across all stakeholders in all departments.

But to deploy an enterprise initiative, one must understand who the key stakeholders are within the organization; identify the individuals who will participate in the marketing, education, championing, design, implementation and ongoing support of the program; and delineate a process for identifying their needs and requirements for the purpose of engineering a high quality master data asset. Before starting to build and configure the master data management environment, team members should perform a number of tasks specifically intended to:

• Identify the people in the organization that can benefit from MDM
• Establish the business value of MDM
• Collect and prioritize the data requirements that will drive the design and development of the underlying MDM infrastructure
• Design a plan for enterprise data integration
• Design a migration plan for the participating applications

To do this, we must work with many stakeholders and align their expectations so that the delivery of value from the maturing master data environment is correlated to milestones along the project lifecycle. In this paper, we look at the different individual roles within the organization that are associated with master data management – and examine what the responsibilities and accountabilities are for each of these roles.

Communicating Business Value

Interestingly, there is a difference between establishing a reasonable business case supporting the transition to MDM and communicating its value. There are a number of ways that a synchronized master repository supports business productivity improvement while increasing the organization’s ability to respond quickly to business opportunities. But since this value proposition is more relevant for achieving senior management buy-in and championship, it does not address drivers targeted internally to each participant and stakeholder.

Addressing this level of stakeholder involvement is particularly important at the beginning of the program in order to assemble the right team to do the analysis and design. People with the most subject matter expertise in each line of business are the same people who will be impacted by a migration to an enterprise information framework. Program champions must be able to engage the business managers in a way that demonstrates the specific value that MDM provides to each line of business.

Because of this, communicating the value proposition for each stakeholder must focus on improving each individual’s ability to effectively get his or her job done. This may encompass a number of different aspects of productivity improvement, including, but not limited to, improved data quality, reduction in operational complexity, simplification of the design and implementation of applicationware, and ease of integration.

However, it's possible to have a comprehensive view of enterprise data and still make the wrong decisions, if the data in those systems is of poor quality. Bad data is persistent scar on the ERP landscape. ERP systems will, by their nature, have multiple points of entry for data — with every point of entry for data being an open door for bad data to get into the system. The ordinary operations of business, such as mergers and acquisitions, system upgrades and the day-today typing of human beings, can cumulatively damage the overall quality of data to the point where the systems become unusable. Bad data can take many forms - duplicate or outdated information, information that is simply incorrect and has no connection to reality, or information which may be correct in form and content but incorrect in relation to your business needs.

Whatever its source or form, once bad data is in the system, it becomes a problem that must be addressed. For this reason, data quality must be made an essential component of any ERP implementation. Today, data quality technology is available that can automate the formatting, standardization, de-duplication and cleansing of corporate data, resulting in consistent and accurate information. Building a data quality capability into an ERP system ensures that the data is usable and supports informed decision making.

This white paper examines the general principles of data quality as they apply to ERP systems, with a particular emphasis on SAP implementations. The focus here will be on the unique types of data quality management in ERP — batch processing for data loading and migration, and real-time data monitoring for ongoing quality maintenance — can complement and enhance each other if approached with the right technology. We'll also take a detailed look at how one company managed to successfully incorporate data quality into its ERP migration and implementation by working with Infosys and data quality vendor DataFlux. ERP systems are now the dominant feature on the corporate information landscape - but how reliable is the data they contain?

There are few businesses today that are not reliant on high quality information to support performance and productivity. In today’s organizations, the importance of high-quality data within any environment is dictated by the needs of the operational and the analytical applications that will process the data. Data governance is instituted as a means for data quality assurance, which has two contexts:

1. The ability to protect against negative business impacts by identifying data quality issues before any material impact takes place (such as failure to comply with regulations or allowing fraudulent transactions to occur).
2. Establishing pervasive trust in the data and providing confidence that the organization can take advantage of business opportunities as they arise.

Operational data governance is the manifestation of the processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization’s business needs. A data governance program defines the roles, responsibilities, and accountabilities associated with managing data quality. Rewarding those individuals who are successful at their roles and responsibilities can ensure the success of the data governance program. To measure this, a “data quality scorecard” provides an effective management tool for monitoring organizational performance with respect to data quality control.

Establishing Business Objectives

In this paper we look at taking the concepts of data governance into general practice as a byproduct of the processes of inspecting and managing data quality control. By considering how the business is impacted by poor data quality – and establishing measurable metrics that correlate data quality to business goals – organizational data quality can be quantified and reported within the context of a scorecard that describes the level of trustworthiness of enterprise data.

Properly-managed enterprise data enables data governance by presenting senior management with a consistent and accurate examination of the organization’s business activity. On the other hand, poorly managed enterprise data can lead to a false sense of security that ultimately causes bad business decisions to be made affecting the stability of the organization. Many organizations are succeeding at using data as an enterprise asset by implementing an enterprise data management (EDM) program.

EDM is a set of disciplines, technologies and best practices to create and sustain a consistent interpretation of business data for all stakeholders throughout the enterprise. EDM provides a semantic layer that is implemented across the enterprise, not just for a single application or IT project. And it provides constant insight into the business information layer across the enterprise describing all types of reference data, structured and unstructured, across the organization.

In the first and second papers in this mini-series we defined what data governance is, and why it is necessary. We then we looked at data governance foundations and presented a cyclic methodology for building data services associated with master and transaction data. In that methodology, we looked at defining common enterprise wide data names and data definitions for master and transaction data. This is known as a shared business vocabulary (SBV).

From this vocabulary data models are built and disparate data identified and mapped to the SBV. Once this is done, disparate data can be profiled and analysed to determine the quality of the data. From here, run-time data services can be built to clean, integrate and enrich the data before serving it up to applications processes and portals or loading it into data warehouses and master data management systems. The data services are used in batch, on demand and on an event-driven basis to enforce data governance throughout the enterprise.

Finally, to make sure that data remains in a high-quality state, we can monitor it and either correct it automatically or alert people to take corrective action if it the data being monitored falls below agreed quality levels. This third paper in the Accelerating Enterprise Data Governance series looks in detail at what one vendor, DataFlux, is delivering in the way of pre-built services to expedite the time to implement enterprise data governance.

Most companies would agree that today, data is the very lifeblood of their business. It is created, maintained and exchanged through the organisation from one end to the other as part of everyday business operations. Yet for many, this invaluable asset is plagued with problems. For example, identifiers and data names are inconsistent across systems for the same data. Also subsets of the same data are often duplicated across systems, making it difficult to maintain and keep any changes to this data synchronised. Customer data, for example, may be created by front office sales personnel but it is also used in customer service, finance (e.g. billing and ledger) and in distribution. Equally, product data may be created by product development personnel but may be used in manufacturing, stores and planning. In addition, data quality problems often occur as data enters the enterprise or flows between systems, and it may also be the case that there is no complete integrated system of record on core data to support business-user reporting. Therefore, when business users require data to be integrated, they often have to manually attempt this —just to produce basic reports for operations management and/or compliance purposes. All of this is symptomatic of data that needs to be more robustly controlled and managed. The term given to managing this enterprise data problem is data governance.

This series of mini white papers defines what data governance is and then looks at the requirements that need to be met for full data governance to be implemented. Once these have been defined, I will then discuss one approach, showing how you can systematically build re-usable data services to automate the tasks needed to formally govern data on an enterprise-wide basis in order to accelerate the time to production and guarantee rock-solid data. Finally, in order to apply real technology to this problem I will also show how one specific vendor, DataFlux, is starting to address the problem of Data Governance by shipping pre-built services called Accelerators that run on the DataFlux technology platform.

Within any insurance company, data and documents associated with customers, insured risks and policies, flow through the organisation as it processes quote requests, renewals, premium payments (revenue), re-insurance premiums and claims. The main operational processes handling this data are underwriting, reinsurance, policy administration and claims. Insurance is also heavily dependent on data in analytical processes that help it manage risk, estimate claims and manage its product portfolios.

Generally speaking, insurance revenue comes from actual net premiums, investment income (accrued from investing premium income in the financial markets) and re-insurance claims. Insurance costs on the other hand come from incurred claims, claims incurred but not reported (IBNR), third party fees (e.g. broker commissions and third party claims assessment fees), re-insurance premiums and operating expenses (e.g. salaries, buildings, etc.).

While calculating profitability is not straight forward in the insurance business, most insurance companies strive to run as efficiently as possible with minimal cost at the best possible loss ratios across their entire portfolio. In addition they want to maintain high levels of customer service and pursue growth by continuing to attract new low risk business.

A key concern is keeping costs to a minimum. This means:

• Avoiding the underwriting of high risks by ensuring underwriters have access to risk factor data, incurred claims, and claims IBNR data during new business quote and renewal processing
• Managing risks by undertaking risk inspections both before rating (pricing) and after writing business if deemed necessary
• Continually strengthening rating rules through claims analysis
• Striking good re-insurance deals with reputable re-insurers as early as possible while re-insurance capacity remains in the market
• Reducing the cost of claims where possible
• Managing portfolios by monitoring actual claims versus ultimate claims and loss ratios

In a 2009 survey of 403 insurance companies in 39 countries by the Underwriting Centre for the Study of Financial Innovation (CSFI), the top ten risk areas facing insurers in this tough economy were:

1. Investment performance
2. Equity markets
3. Capital availability
4. Macro-economic trends
5. Too much regulation
6. Risk management techniques
7. Reinsurance security
8. Complex instruments
9. Actuarial assumptions
10. Long tail liabilities

These risk areas show a clear concern about investment income and the ability to recover losses through re-insurance claims. Both of these issues stem from the current crisis in the financial markets. They also indicate that insurers that depend on the increasing flow of premiums to cover both claims and operating expense may find it difficult in a tough economy where investment income is low and growth is low. Efficient, low cost operational processes and strong analytical processes are therefore fundamental to performance.

In these tough times, insurance companies need to attract the right customers, price correctly, write the right business, decline high-risk business, mitigate risk, reserve correctly while maintaining cash flow, manage outstanding claims and get the best re-insurance deals and minimise operating expense. Just imagine then, the impact on core insurance operational and analytical processes if the data flowing through these processes is unreliable.

Because data is the foundation of practically everything that goes on in today’s organizations, managing data quality is paramount. An organization’s data is one of the true competitive advantages that it can employ. Data contains insight about the company, its health, its customers and its finances. Even the most sophisticated BI system won’t do any good if the data it analyzes is faulty from the get-go. If the data is not consistent, accurate and reliable, the company will make poor decisions – and see poor results. In this podcast, DataFlux senior director of marketing, Daniel Teachey, discusses how companies can work to maximize the benefits of BI by investing in data quality.