The ancient proverb "May you live in interesting times" was really underscored during 2010. In fact, the year offered some great examples of the increasing need for data governance in both the public and private sectors.

Federal Computer Week reported, "Poor information security in an obscure State Department computer system made it possible for an inside source to turn over a massive cache of secret and sensitive diplomatic cables to WikiLeaks."

According to The Associated Press, the Federal Aviation Administration is missing key information on who owns 119,000 private and commercial aircraft in the U.S. - a gap the agency fears could be exploited by terrorists and drug traffickers.

Bloomberg BusinessWeek reported that TUI Travel, Europe's largest tour operator, said it accepted the resignation of its CFO after having to restate its 2009 results. The tour operator said it wrote off $185 million after faults in integrating computer systems following a 2007 merger.

And finally, in an interesting paper by Louise Francis and Virginia Prevosto, published by the Casualty Actuarial Society Forum, the authors stated, "Since 2007 a global financial crisis has been unfolding. The crisis was initially caused by defaults on subprime loans, aided and abetted by pools of asset-backed securities and credit derivatives, but corporate defaults, such as that of Lehman Brothers, and outright fraud have also contributed to the crisis. ... In this paper we show that data quality played a significant role in the mispricing and business intelligence errors that caused the crisis. ... We use the Madoff fraud and the mortgage meltdown as data quality case studies. We conclude that data quality issues have made a significant contribution to the global financial crisis."

While your company may not have data governance horror stories on this scale, undoubtedly there are plenty of cases of bad decisions, missed revenue opportunities and increased costs due to silos that have sprung up between business units. These may include:

  • Inefficient processes due to bad quality data.
  • Systemic problems due to improperly integrated systems passing bad information to one another.
  • Poorly trained data entry personnel unwittingly entering incomplete data.
  • Problems complying with government regulations brought on by fragmented or inconsistent information.

Problems like this are remarkably common across the corporate landscape. Data governance maturity models generally have five levels and are modeled on Software Engineering Institute's Capability Maturity Model. A recent survey sponsored by Kalido found that only 10 percent of organizations have been able to move their data governance programs beyond the lowest two levels of data governance maturity. This shows most companies haven't gotten very far in their data governance efforts or are just getting started.

So if your company is in this position - increasingly aware of the need for data governance but not very far along the data governance maturity scale - how do you make tangible progress in a short time?

Suggestions For Getting Started

  1. Avoid "analysis paralysis." Since many organizations don't know where to start, they stall and won't do anything.
  2. Make sure everyone realizes that although technology is important, it will at best be an enabler. It's not a silver bullet. You can't just implement data governance technology. You're going to need a comprehensive program involving organizational changes, process redesign and intensive work on information quality (and yes, some technology).
  3. Use one of the many data governance maturity models to baseline your current situation and to describe your desired future state. This will provide a framework for planning your activities and managing everyone's expectations. Define: a) where you are now, b) where you want to be, and c) how fast you want to get there.
  4. Start to look at project dimensions for master data management, data integration, data quality and data governance over that time period. This should lead to a realistic data governance roadmap.
  5. Don't start before you receive executive sponsorship and funding for your data governance organization. If you begin without high-level support, you'll end up with a failed effort that must be explained later.

Spend Time on the Design Before Launching

Each company is different, so your data governance program should be tailored to how your company is structured and aligned with your corporate culture. There are a lot of moving parts in a data governance initiative; working from an understood design is a lot easier than making it up as you go along.

Make sure you plan for (and address in your program) all of the important aspects that come together in a data governance initiative:

Organizational design, including the organizational model, leadership and staffing. Where will the data governance function live and who pays for it?

  • Executive sponsorship, corporate culture and organizational change management.
  • Communications strategy. Create an internal campaign to support the data governance program.
  • Data governance lifecycle, including the design and implementation of new data governance processes.
  • Information quality. Build or incorporate a data quality function into the data governance program.
  • Technology. Implement the various types of supporting technologies required to enable a robust data governance program.
  • This should give you the sense for what is involved in starting a data governance program in a large corporation.

Don't let challenges dissuade you - in the long run, not tackling data governance is far worse, since your enterprise is at risk of being a 2011 horror story. So dig in and start the hard work of governing your enterprise data. Don't forget, if it's done properly, data governance can produce new revenue, cost savings and more efficient processes for your company.

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