The DataFlux Community of Experts is a forum for industry thought leaders to provide perspective and insight and engage in discussions on issues surrounding data governance, data quality, data integration and master data management. Our regular contributors include David Loshin author of Master Data Management and president of Knowledge Integrity, Inc., Joyce Norris-Montanari consultant and president of DBTech Solutions, Jim Harris blogger-in-chief at Obsessive-Compulsive Data Quality, Dylan Jones founder of Data Quality Pro and Data Migration Pro, master data management blogger Charles Blyth from www.charlesblyth.co.uk, and Phil Simon, consultant, author of The Next Wave of Technologies and blogger at Phil Simon Systems.
Aristotle, Patrick Swayze, and People Issues
Posted by Phil Simon in data integration, data quality on March 11th, 2010
Last month, I participated in a radio show with several data quality and data integrity experts sponsored by DataFlux. It’s always interesting to exchange ideas, war stories, and best practices with really smart people. I figure that there’s typically strength in numbers. To paraphrase Aristotle, the whole can be greater than the sum of the parts.
So, why do I invoke Aristotle so early in the post? Three reasons:
- That’s kind of the rationale for my next book, but I digress.
- Hey, I have to quote someone intellectual to keep up with Jim Harris’ Proust references.
- Because, quote-wise, I’m going to the opposite end of the spectrum later in this post. Trust me. I want to be the first person in the history of blogging to write a cogent post with both Aristotle and Patrick Swayze.
Oughtn’t you audit?
Posted by Jim Harris in data quality on March 10th, 2010
Ensuring that the data being used to make critical business decisions is as reliable and accurate as possible is why data quality is so vitally important to the success of your organization.
That statement would probably make most people throughout your organization nod their head in an ostentatious demonstration of agreement with this universally acknowledged truth.
So why then, on a daily basis, do these very same people:
- Never check that data is complete and accurate before sharing it with others
- Never seek to understand what the data means within a business context
- Never consider the costs and other risks associated with poor data quality
- Never verify the data that they are using to make critical business decisions
Clap your hands if you believe in data quality
Data Quality Business Impact Categories
Posted by David Loshin in compliance, customer data integration, data governance, data quality on March 9th, 2010
From a philosophical perspective we try to help our clients figure out the relationship between data issues and achieving their business objectives. From a practical perspective this requires a means for linking business issues to data failures. A big challenge is bridging the gap between IT staff, who understand the data side, and the subject area experts, who understand the business side. Our approach is to try to simplify the analysis process by introducing business impact categories that can be affected by data issues, then use those categories as a guide for assessing where the gaps exist.
Physical Data Models (PDM)
Posted by Joyce Norris-Montanari in data profiling on March 8th, 2010
In the last couple of blogs, I discussed Subject Area Models (SA or SAM), and Logical Data Models (LDM). The next step, in our modeling adventure, is the Physical Data Model (PDM). The Physical Data Model takes the required portions of the LDM towards a technical solution, based on the platform. A platform is the hardware and software designated for the business and technical solution. Again, the PDM for our MDM (isn’t this fun) solution of Customer and Product would only hold information about those subject areas needed for the business and technical solution.
Data Quality Lip Service
Posted by Phil Simon in data governance, data quality, master data management on March 4th, 2010
One of the cool things about the Internet is that you can constantly expand your knowledge base. I have already learned a great deal reading the posts on this forum. I had posted a few weeks ago on my own site about how writing books counts as my second IT education, and perhaps being a part of this community is my third. After two months of posting on this site, one thing to me is crystal clear:
I’m certainly not alone in my belief that many organizations don’t take data quality seriously–or at least as seriously as they should.
In this post, I’d like to focus on why leaders at many organizations merely pay lip service to data quality.
At some level, they know that DQ is important.
I’ve had many spirited discussions with both IT folks and line managers in my years of consulting. I have yet to encounter anyone who would admit that the integrity and quality of his/her organization’s data was not important. Read the rest of this entry »
The HedgeFoxian Hypothesis
Posted by Jim Harris in data governance, data quality, master data management on March 3rd, 2010
Data quality, master data management, and data governance are all terms used to describe enterprise initiatives.
Lively debates can be had over whether these terms are related or if one is the primary discipline and the others are subordinates.
Regardless, many organizations look for a framework to follow, either “one theory to rule them all” or a one-size-fits-all methodology.
However, is it better to approach such a complex challenge with one all-encompassing “theory of everything” or with a best practice based on awareness and adaptation?
The Hedgehog and the Fox
In his excellent essay The Hedgehog and the Fox, Isaiah Berlin described two very different types of thinkers using the ancient Greek expression:
“The Fox knows many things, but the Hedgehog knows one big thing.”
Noise and Signal
Posted by David Loshin in data enrichment, data integration on March 2nd, 2010
In a white paper I recently released, I referred to an article quoting a report from 2008 discussing the need for storage solutions, and this report mentioned that the volume of unstructured data was growing with a 61.7% compound annual growth rate, which reflects a pretty huge increase. Quick question: what are the types of content that are feeding into this growth?
Of course, the explosion of picture, audio, and video artifacts streaming into sites like YouTube and Flickr (for example) does contribute significantly. While many of these items are pirated copies of previously broadcast material, video resumes of prospective Hollywood superstars or the country’s funniest home videos, there are also some serious pieces carrying semantically worthy content (from numerous broadcast channels, as well as *ahem* “experts” in respective fields). Read the rest of this entry »
Logical Data Models (LDM)
Posted by Joyce Norris-Montanari in data profiling on March 1st, 2010
I am still on my data modeling kick! Remember the subject area model is a diagram of a specific area of interest to a corporation. Customer and Product are two subject areas that are considered number one on anyone’s list. The logical data model (LDM) is the next iteration that depicts the business need for data and information.
I sometimes call the LDM the ‘drill-down’ that includes multiple subject areas, because we are taking the information in the subject area models and extending it into a business solution. The LDM is based on a business solution or a specific project OR sometimes multiple projects. Let’s take an example of a financial company that has silos of data that applications rely on. These applications have redundant data, so a MDM (Master Data Management) initiative is started. After the subject area models are completed for Customer and Product, the business requirements are gathered, the LDM is created. The LDM for the MDM project will only include WHAT IS NEEDED FOR THE ENTERPRISE in the Customer and Product subject areas. This would include extending customer contact information, and understanding the packaging, bundling, or parts that make up a product.
Does your data quality help customers succeed?
Posted by Dylan Jones in data quality on February 26th, 2010
When we’re thinking about data quality improvement we often focus on the internal benefits in an effort to gain traction with business sponsors and the user community.
I’ve been guilty of this a lot lately with some of the articles and posts I’ve written, it’s easy to slip into this mindset as building the internal business case is the first hurdle we have to overcome.
But I think we must always keep one eye on the ultimate prize - helping the customer.
Ken O’Connor (who writes at http://kenoconnordata.wordpress.com) recently picked up a great quote from Craig Newmark, the founder of Craigslist.com:
Large organisations are normally run in a way that people tell their boss what they think their boss wants to hear, and that continues right up the ladder. Because of this, the result is that the people making decisions rarely get good-quality information.
This same information invariably flows out to our customers so the damage can spread far beyond the internal reporting lines. Read the rest of this entry »
Foreigner and First Time IT Project Myths
Posted by Phil Simon in data migration, data quality on February 25th, 2010
I’m a big fan of 70s and 80s music. I mean huge. Maybe it’s nostalgia or maybe the music was just “better.” Maybe I just refuse to grow up. Regardless, I just never got over that era. I doubt that I’m alone in that regard.
As an acolyte of older music, I often listen to the songs of my youth on my ever-present iPod. This week, Feels Like the First Time by Foreigner graced my ears during one of my after-work trips to the gym. I started thinking about how many IT projects involve the following:
- people who have never participated in a system mirgration project
- big time data issues
- issues related to numbers one and two
First-Timers typically approach IT projects very differently than their more experienced and sometimes (justifiably) cynical counterparts. I suppose that you can put me in the latter group. Read the rest of this entry »