July 26, 2011
Many organizations know that they need to initiate a data governance program of some kind. They usually know exactly why they need data governance. It’s typically to address business issues in compliance and risk or to address technical issues in data standards and quality. In other cases, data governance is required to share data across departmental boundaries for the sake of business integration, business intelligence, or competitive advantage. Still others need quality and control measures that will give information consumers greater confidence in trusted data. Some know they need all the above.
Yet, organizations don’t know where to start or how to sustain data governance over the long haul. They don’t know what kind of organizational structure they need to support data governance or how to staff data governance. They’re not sure what kinds of tools and techniques they can leverage to scale up and automate governance as it grows. They don’t know how to quantify governance with data policies or how to enforce such policies.
The barriers to data governance are erased when an organization adopts the techniques and best practices of data quality and the closely related practice of data stewardship. That’s because the business-to-IT collaboration established by quality and stewardship practices is also required of data governance. In fact, quality and governance practices are similar, except that the needs of governance are broader, encompassing both enterprise data standards and business issues relative to data, such as compliance, risk, and privacy.
Instead of reinventing the wheel, user organizations can borrow some of the organizational structures and processes of data quality and apply them to data governance. This minimizes the risks and decreases the time-to-use of data governance. Likewise, there are data quality tool capabilities that can help document, automate, and scale up data governance processes.
This TDWI Checklist Report makes a case for applying data quality (DQ) techniques and best practices to data governance (DG) as a way of kick-starting and sustaining data governance.