Data Quality Scorecards are one of the most vital resources for guiding your team and motivating your project sponsors towards the goal of data quality management.
But how do you go about creating a data quality scorecard? What are some of the key points to consider?
Here are 23 pointers to set you in the right direction.
Have you created a successful data quality scorecard? Want to share your approach or findings?
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Data Quality Scorecards: 23 Tips
- Make your data quality scorecard the centrepiece of your data quality initiative
- Scorecards are key to understanding how well the data supports various reports, analytical and operational processes, and data-driven projects
- Critical for making good decisions about data quality improvement initiatives
- Without a data quality scorecard, all you have are raw materials and no value-added product to justify further investment into data quality management
- Consider the creation of 4 levels to your data quality scorecard:
- Score Summary
- Score Decompositions
- Intermediate Error Reports
- Atomic Level Data Quality Information
- Well-designed aggregate scores are goal driven and allow us to evaluate data fitness for various purposes and indicate quality of various data collection processes
- From the perspective of understanding the data quality and its impact on the business, aggregate scores are the key piece of data quality metadata
- The data quality scorecard is a collection of aggregate scores
- Aggregate scores help make sense out of the numerous error reports produced in the course of data quality assessment and without aggregate scores, error reports often discourage rather than enable data quality improvement.
- Be careful when selecting aggregate scores to measure, scores not tied to a meaningful business objective are useless
- A simple aggregate score for the entire database is usually rather meaningless
- Good aggregate scores are goal driven and allow us to make better decisions and take actions. Poorly designed aggregate scores are just meaningless numbers
- It is possible and desirable to build many different aggregate scores by selecting different groups of target data records. The most valuable scores measure data fitness for various business uses
- Scorecards allow us to estimate the cost of bad data to the business, to evaluate potential ROI of data quality initiatives, and to set correct expectations for data-driven projects
- If you define the objective of a data quality assessment project as calculating the cost of bad data to the business for example, you will have much easier time finding sponsors for your initiative
- It is usually important to know if the data errors are mostly historic or were introduced recently
- Score decompositions show contributions of different components to the data quality
- Score decompositions can be built along many dimensions, including data elements, data quality rules, subject populations, and record subsets
- Level of detail obtained through score decompositions is enough to understand where most data quality problems come from
- For more detailed decomposition produce various reports of individual errors that contribute to the score (or sub-score) tabulation
- Detailed reports can be filtered and sorted in various ways to better understand the causes, nature, and magnitude of the data problems
- At the very bottom of the data quality scorecard pyramid are reports showing the quality of individual records or subjects, these are called atomic level reports and identify records and subjects affected by errors, they could even estimate the probability that each data element is erroneous
- Building and maintaining a dimensional time-dependent data quality scorecard must be one of the first priorities in any data quality management initiative
About Arkady Maydanchik
For more than 20 years, Arkady Maydanchik has been a recognized leader and innovator in the fields of data quality and information integration.
He is the author of the extremely popular publication "Data Quality Assessment".