The DataFlux Data Governance Maturity Model

The DataFlux Data Governance Maturity Model helps organizations understand their current level of data management and identify a path for growth in the future

While achieving a single, unified enterprise view is an evolutionary process, an organization’s growth toward this ultimate goal invariably follows an understood and established path, marked by four distinct stages.

Companies that plan their evolution in a systematic fashion gain over those that are forced to change by external events. The Data Governance Maturity Model helps control that change by determining what stage is appropriate for the business – and how and when to move to the next stage. Each stage requires certain investments, both in internal resources and from third-party technology. However, the rewards from a data governance program escalate while risks decrease as the organization progresses through each stage.

The four stages of data governance maturity are:

Stage One – Undisciplined

At the initial stage of the Data Governance Maturity Model, an organization has few defined rules and policies regarding data quality and data integration. The same data may exist in multiple applications, and redundant data resides in different sources, formats and records. Companies in this stage have little or no executive-level insight into the costs of bad or poorly-integrated data.

Stage Two – Reactive

A reactive organization locates and confronts data-centric problems only after they occur. Enterprise resource planning (ERP) or customer relationship management (CRM) applications perform specific tasks, and organizations experience varied levels of data quality. While certain employees understand the importance of high-quality information, overall corporate management support – as well as a designated team for data management – is lacking.

Stage Three – Proactive

Reaching the proactive stage of the maturity model gives companies the ability to avoid risk and reduce uncertainty. At this stage, data goes from an undervalued commodity to an asset that can be used to help organizations make more informed decisions.

A proactive organization implements and uses customer or product master data management (MDM) solutions – taking a domain-specific approach to MDM efforts. The choice of customer or product data depends on the importance of each data set to the overall business. A retail or financial services company has obvious reasons to centralize customer data. Manufacturers or distributors would take product-centric approaches. Other companies may identify corporate assets, employees or production materials as a logical starting point for MDM.

Stage Four – Governed

At the governed stage, an organization has a unified data governance strategy throughout the enterprise. Data quality, data integration and data synchronization are integral parts of all business processes, and the organization is more agile and responsive due to the single, unified view of the enterprise.

At this stage, business process automation becomes a reality, and enterprise systems can work to meet the needs of employees, not vice versa. For example, a company that achieves this stage can focus on providing superior customer service, as they understand various facets of a customer’s interactions due to a single repository of all relevant information. Companies can also use an MDM repository to fuel other initiatives, such as refining the supply chain by using better product and inventory data to leverage buying power with the supplier network.

Applying the Data Governance Maturity Model

Any organization that wants to improve the quality of its data must understand that achieving the highest level of data management is an evolutionary process. A company that has created a disconnected network filled with poor-quality, disjointed data cannot expect to progress to the latter stages quickly. The infrastructure – both from an IT standpoint as well as from corporate leadership and data governance policies – is simply not in place to allow a company to move quickly from the undisciplined stage to the governed stage.

However, the Data Governance Maturity Model shows that data management issues – including data quality, data migrations, data integration and MDM – are not “all or nothing” efforts. For example, companies often assume that a solution that centralizes their customer data or product data is the panacea for their problematic data and that they should implement a new system immediately. But the lessons of large-scale ERP and CRM implementations, where a vast majority of implementations failed or underperformed, illustrate that the goals of data management cannot be met solely with technology. The root cause of failure is often a lack of support across all phases of the enterprise.

To improve the data health of the organization, organizations must adapt the culture to a data governance-focused approach – from how staff collects data to the technology that manages that information. Although this sounds daunting, the successes enjoyed by an organization in earlier stages can be reapplied on a larger scale as the organization matures. The result is an evolutionary approach to data governance that grows with the organization – and provides the best chance for a solid, enterprisewide data management initiative.