Defining Data Governance and Its Maturation Cycles

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.