Be Prepared to Duel with Data Quality

Plenty of business intelligence (BI) or data warehouse projects have been blindsided by complications related to data quality. Sometimes these issues aren't apparent until business users start testing the systems just before going live with the projects. What causes BI project teams to get caught off guard by data quality issues? Why do these problems surface so late in the projects?

There are two common pitfalls: defining data quality too narrowly and assuming data quality is the responsibility of the source systems.

People often assume that data quality simply means eliminating bad data - data that is missing, inaccurate or incorrect. Bad data is certainly a problem, but it isn't the only problem. Good data quality programs also ensure that data is comprehensive, consistent, relevant and timely.

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