More and more companies are recognizing that they’re accumulating ever-increasing amounts of data, but not necessarily gaining business insights from it. The missing link is the transformation of data into information that is comprehensive, consistent, correct and current. That is a problem technology cannot solve for you; it requires people.

The answer is establishing a data governance program to help your organization truly treat its data as a corporate asset and maximize its value. The data governance program will enforce consistent definitions, rules, business metrics, policies and procedures for areas such as:

  • Data creation
  • Data movement, transformation and integration
  • Business metrics and data definitions
  • BI data models
  • Master or reference data
  • Use cases for specific BI tools and self-service BI
  • Data change management
  • Monitoring governance
  • Information access and delivery
  • Information consumption (reporting and analysis)

Data governance helps ensure that your data is trustworthy and provides business value. The process of governance, however, is becoming more challenging as organizations rely more on data that is unstructured and from the cloud, as well as big data.

It is critical that data governance manages not only data creation, but also data consumption. Too often enterprises concentrate solely on creation only to have business and IT people alter data in their spreadmarts or BI applications. It is seemingly pointless to spend all one’s efforts to ensure consistent data creation, only to have the data altered, and thus become inconsistent when business people conduct their analytics.

How we've helped others with their data governance needs

  • An online service provider had inconsistent data definitions and business metrics in their reports and dashboards, which were being used by many people in various groups. We analyzed the flow of data between systems and examined the data management processes so we could recommend changes to their business processes and roles and responsibilities. With good data governance practices they achieved consistent definitions and metrics.