Data quality tools do not solve data quality problems

October 5, 2010
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Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Data quality tools do not solve data quality problems—People solve data quality problems.”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“Data quality tools do not solve data quality problems—People solve data quality problems.”

This DQ-Tip came from by the DataFlux IDEAS 2010 Assessing Data Quality Maturity workshop conducted by David Loshin, whose new book The Practitioner’s Guide to Data Quality Improvement will be released next month.

Just like all technology, data quality tools are enablers.  Data quality tools provide people with the capability for solving data quality problems, for which there are no fast and easy solutions.  Although incredible advancements in technology continue, there are no Magic Beans for data quality.

And there never will be.

An organization’s data quality initiative can only be successful when people take on the challenge united by collaboration, guided by an effective methodology, and of course, enabled by powerful technology.

By far the most important variable in implementing successful and sustainable data quality improvements is acknowledging David’s sage advice:  people—not tools—solve data quality problems.

 

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