Data quality management: Follow the doctor's orders

Preventing data quality problems may seem like a "no-brainer," but you'd be surprised at how many data warehouse and business intelligence (BI) project teams don't plan for data quality issues. When you ask them why, the conventional response is that data quality management is the problem of the source systems. Although on one level that may be true, it's an awfully caviler response from a team that's responsible for providing accurate data to business users.

Data quality management recommendations

So where does data quality management fit in your data warehouse and BI project plans? You need to make it an integral part

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of every phase of your project – from requirements through design and production. A few specific recommendations:

Data quality shouldn't be an afterthought in your data warehouse and BI projects. But if you aren't following these recommendations already -- at least you're not alone. Many companies struggle with data quality management. That doesn't excuse you, but it means you have a lot of company. The best course of action is to take steps to prevent data quality problems up front, so you can avoid the bitter medicine of dealing with them later.

About the author
Rick Sherman has more than 18 years of business intelligence and data warehousing experience, having worked on more than 50 implementations as an independent consultant and as a director/practice leader at a Big Five accounting firm. He founded Athena IT Solutions, a Stow, Mass.-based business intelligence consulting firm. Send Rick an email.

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