Data Quality Metrics: Are You Adopting The Dilbert Approach?

Dilbert on Data Quality Metrics Dilbert on Data Quality Metrics

A lot of companies struggle to determine what their data quality metrics and targets should be. The problem is that because you’ve typically never managed data quality proactively before, the whole planning and measurement aspects can appear confusing; this leads to numerous problems.

Problem #1: Which Data Quality Measures to Select?

It’s obviously wise to leverage data quality technology and the inbuilt metrics and reporting they provide but at some point you’ll need to demonstrate to the business what kind of impact your data quality efforts are having. Measuring the effectiveness of Data Quality is a populr

  • How is data quality improvement impacting Customer Satisfaction levels for example? (Assuming they’re measured and relevant to your area of improvement.
  • How efficient are your knowledge workers since data quality management was introduced? Are you winning the war against the Data Quality Time Traps?
  • How many data entry issues are being reported since you employed your data entry improvements?
  • How has the quality of your finished product or services been reduced?

Managing the right data quality metrics in the first place is obviously critical. Are you relying only on those provided by your data quality tools or are you monitoring measures that the business can get their heads around?

Problem #2: What is a Reasonable Target for Improvement?

This is where we need more debate. I just don’t think there is enough case history or published knowledge available on how organisations should derive targets for their data quality efforts.

The popular Data Quality Books obviously cover how to measure data quality levels using techniques like data quality scorecards but there is a lack of insight into what targets to aim for.

One company (KFR Services) came up with a novel approach in this data quality improvement interview:

“With our data quality scheme we award a bonus if one of our staff reaches a data quality goal. We try and tie their goals to the overall company goal.

So if our corporate goal was a 50% reduction in defects we would make the individuals goal to be cutting their own recorded defects in half.

This motivates the team as no-one wants to see a month where defects occur, it definitely helps the entire team to keep the figures up.”

– Stephanie Fetchen

So what about your organisation? How are you setting targets for data quality measures? It would be great to hear your stories.

About the Author

I am the editor and founder of Data Quality Pro. 20+ years experience of data quality initiatives.

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