A Broader Look at Data Governance

Loraine Lawson

What's data governance got to do with integration? I mean, beyond the obvious way in which data governance impacts any data initiative.

 

Recently, I wrote about the puzzling picture of data governance's acceptance and adoption in organizations. Then again, as Andy Hayler, founder of Kalido, pointed out in November, it's still a nascent discipline.

 

In fact, there's still disagreement over what, exactly, data governance encompasses. In March, Jim Ericson cautioned against viewing it too broadly, writing:

As a set of policies and controls, governance is a rulebook for who owns data, who can access it and who can manipulate it. What governance is not-and was never intended to be-is a playbook for how effectively we use, or more appropriately, measure our effective use of data.

When you consider that view, it seems fairly limited. But others define data governance in a broader, more strategic context. Jim Harris, an independent consultant specializing in data quality and a blogger-in-chief at Obsessive-Compulsive Data Quality, offered this definition in his Declaration of Data Governance:

From my perspective, the primary focus of data governance is the strategic alignment of people throughout the organization through the definition, and enforcement, of policies in relation to data access, data sharing, data quality, and effective data usage, all for the purposes of supporting critical business decisions and enabling optimal business performance.

I think Harris' definition gets a boost from various data governance maturity models. A 2009 report from NASCIO looked at eight different maturity models, including models from IBM, Gartner and Oracle. In most, data governance evolves into a broader, enterprise-wide information management strategy-with "strategy" being a key word.

 

If you'd prefer something more concrete, check out this August article from BPM, which shows how one company used data governance to solve obstacles in a BI-related integration project. Actually, it gives three examples total where data governance-or lack thereof-lead to the success or failure of BPM and BI initiatives.


 

In the last example from the BPM article, a leading pharmaceutical company encountered data integration problems and other, more political, issues on a business intelligence project. To resolve the conflicts, the company formed a data governance team, which identified:

  • Business rules, including policies, standards, data definitions and compliance requirements
  • Decision rights
  • Accountability for governing the use of data
  • Control mechanisms
  • Governance metrics and success measures
  • A data facilitator to provide checks and balances for business, IT and compliance

 

As you can see, that's a pretty comprehensive approach. And it worked:

The focus on data governance was instrumental in enabling the company to implement a successful BI solution to integrate the data and make it usable for analysis, reporting, and decision-making. The organization also achieved increased agility in responding to requests for information from business stakeholders and external agencies ...

At that company, data governance helped by defining who owned the data and how it could be used. In effect, it broke down the business unit silos that were slowing down the integration work.

 

That got me to thinking: How would an enterprise-wide data governance program make integration easier, quicker and more successful? In my next post, I'll share five ways I think creating a data governance program could make your work more efficient when it comes to integration.



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