About three years ago, Steven Adler was running the privacy consulting practice at IBM. As he talked with IBM customers, he discovered that many of the large customers had some pretty complex IT challenges that didn’t fall into IBM’s then-current set of offerings around hardware, software or services. Adler couldn’t just send a sales team out to the client site to attack a problem with a tried-and-true solution; the issues went far beyond the scope of new hardware or some piece of data management software.

Adler noticed these complex issues seemed to have a common theme: how to get the most use out of a company’s data assets, also known as “data governance.”

Our friends at Wikipedia define data governance as a practice that encompasses the people, processes and procedures required to create a consistent, enterprise view of an organization’s data in order to:
• Increase consistency & confidence in decision making
• Decrease the risk of regulatory fines
• Improve data security
• Maximize the income generation potential of data

As you can imagine, data governance strikes at the heart of everything a company does. There is no process that doesn’t involve the input, use or production of data. But, trying to get your arms around everything can be a pretty daunting task.

Adler invited a handful of IBM’s top customers to a three-day executive meeting so they could discuss the challenges of data governance. Even though there were competing companies in the room (e.g., several financial institutions), the attendees saw the benefit in discussing the issues and turning to each other for help and advice. Since all attendees saw value from attending this meeting, Adler formalized the group and launched the IBM Data Governance Council.

Over the years, the council continued to meet and add new members. There are presently more than 50 council members comprised of 35 IBM customers, 17 business partners/vendors, and 3 representatives from academia.

Their discussions have led to something unique in the IT industry. They call it the Data Governance Maturity Model, and it outlines the five levels of maturity a company goes through in 11 categories, including data quality, data risk management, organizational awareness and other areas.

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