Do you want to know personal data quality fear? Contact Experian (, Trans Union ( and Equifax ( and obtain a copy of your credit report. I recently had concerns that I could be a victim of identity theft, so I obtained copies of my credit reports from these agencies. The results were eye-opening.

While the overall accuracy of the reports was better than the last time I reviewed them in the late '80s, there were still enough inaccuracies to give me pause. Not only were there wildly inaccurate address and employment entries, some of the information in these reports is set to remain accessible for more than 10 years.

If you've never seen your credit report, I encourage you to obtain a copy and review it carefully. The amount of detail about you, your address, employment and financial history may surprise you.

Thinking about letting that credit card payment slide for another 30 days in these tough times? Think again. My records are still carrying the red-letter sin of a single late payment on a credit card in 1995.

You've probably made an error or missed a payment somewhere in your financial life. If so, it has been captured, stored and made easily accessible for credit analysis purposes. It is also likely that there are gross inaccuracies in your credit reports. Considering how often credit histories are used to make housing, credit and employment decisions, you should ensure that the data being presented about you is accurate.

Inaccurate data can easily be intertwined with your identity in a most intimate way. Dirty data can prevent you from achieving job opportunities, obtaining credit or buying a home. Invalid or incorrect data can literally shape the course of your life and alter your chances of success.

As you know, dirty data can also determine the success of your business intelligence (BI) project. With this much importance and the potential impact of dirty data, why isn't data quality more ingrained in what we do in BI?

At a recent vendor briefing from a data quality software vendor, I was disappointed to learn that data quality is still not a high priority for most BI teams. The vendor has found that their best customers are teams that have released a system with flawed data and suffered the backlash from the business community. Only after feeling the heat of rejection, low utilization and the threat of budget elimination do most teams seek out a data quality solution.

This news was distressing, considering that my peers and I have been preaching the importance of data quality for more than ten years. It clearly shows that we have not yet found a way to communicate the importance of this issue, or the impacts of not addressing it, in a way that resonates with, and is relevant to, our audiences.

I left the briefing deeply troubled. In my experience, projects that don't include a proactive data quality component have a very low success rate. How could we have missed this issue so completely as an industry?

As I contemplated this, I was reminded of a meeting I had with Larry English, the godfather of information quality in our industry. At the meeting, Larry's upside passion was mixed with an equal part of heartfelt frustration about a very large project that my company was executing.

We both saw that there were inherent information quality challenges that the client was unwilling to address. Larry believed that the project should be stopped or abandoned unless the client was willing to adopt a pervasive cultural change related to information quality. From his point of view and in his experience, unless this deep cultural and process shift occurred, their efforts would be severely degraded and probably mortally wounded.

While agreeing with him in principle, I knew that there was zero probability that this client was going to successfully implement any level of significant cultural change, successfully implement significant process change or stop a massive project with more than 120 people on the team and make information quality the centerpiece of the initiative.

In the end, I think our project was a microcosm of what has happened with data quality in our industry. While information quality is a recognized prerequisite of success at the theoretical level, BI teams, rightly or wrongly, perceive it as something that requires an organization-wide cultural and business-process change and costs well into the triple digits to implement. Consequently, teams have viewed data quality as an all-or-nothing, change-the-business-culture component and have put it on the back burners of their projects.

The lesson I hope teams can learn, and learn quickly, is that data quality can be done incrementally. It can be done with low-cost tools. It can be done successfully with limited resources, and it definitely remains a pragmatic prerequisite for success.

For your personal protection, eliminate data quality fear by ensuring the accuracy of your credit reports. For your team and project's protection, eliminate data quality fear by repositioning the challenge as accomplishable, incremental and an absolute prerequisite for success.

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