Where Is Your Data Quality Scorecard?

You have data issues or think you might have data issues.  How do you manage these and how do you present this to the business for highlighting progress in data quality?

Last month I posted about managing data quality and defining your metrics, the data dimensions, and your acceptable thresholds and tolerances. Today I have a few ideas on how to present that to the business.

Your scorecard/ or data quality dashboard will form the key communication piece of your Data Quality work. It will show a baseline measurement of where you started and a target of where you want to get to – remember you do not want to be 100% accurate – see last month’s post!

You do want to be able to measure progress towards your target.  Is data improving, are you having dips in quality, do you know why this is. Ultimately the trend should be that your data is improving.

Scorecards can come in all shapes and sizes from hand written on a whiteboard or flipchart to a system generated version with pie charts and a heatmap.

Depending on the audience, is it virtual, non-data colleagues or people who sit next to you. This will help you determine the type of scorecard you want.

I have used a whiteboard for high physical visibility for people who walked passed my team and for the President to see and support and high-tech solutions using pie & bar charts for sending around to the virtual team and senior mgt.

A few things to consider:

Your scorecard can support decisions on data quality activity, do you need more resource, how would you justify this and where will your quality be if you get the extra resource?

How is the data quality affecting the business performance, how can this be shown on the scorecard?  What value is improved data quality having on the business?

How would you display on your scorecard the number of data issues occurring, how long they are taking to rectify?

Do you need to roll up your measurements into higher level characterizations of compliance with expectations, while allowing for drill-down to isolate the source of specific issues?

Many organizations already have a technology infrastructure to support the definition of operational performance indicators and the supporting measurements that feed a hierarchical view of productivity/accuracy within a business intelligence (BI) framework. Ask IT what your company already has, as data quality metrics are just another feed that can use your existing front-end BI reporting, analytics tools and visualization tool set.  This will most likely include widgets and/or the ability to drill-down into issues to enable the data stewards to act.

Example Scorecards / Dashboards:

A high-level data quality scorecard reflecting three aspects of measurements. Data Quality Score, accumulated from underlying data quality metrics. Data Quality Policy, the degree to which the data governance team has identified business impacts and policies are defined to manage the impact. Data Governance Processes are in place and being monitored.

DQ score3

A view which is more statistical, focusing on multiple areas and the data management aspects within those areas.

DQ score

A scorecard reporting on adherence to specific rules and a few key customer areas.

DQ score2

Resources: 

Data Quality Scorecards: 23 Tips – https://www.dataqualitypro.com/how-to-create-data-quality-scorecards/

https://blog.kissmetrics.com/data-quality/

https://www.whitepapers.em360tech.com/wp-content/files_mf/1407250286DAMAUKDQDimensionsWhitePaperR37.pdf

https://www.domo.com/learn/data-never-sleeps-5?aid=ogsm072517_1&sf100871281=1

https://www.cdc.gov/ncbddd/hearingloss/documents/dataqualityworksheet.pdf

https://www.invensis.net/blog/data-processing/5-best-practices-effective-data-quality-management/

https://www.techopedia.com/definition/28022/data-quality-management-dqm

Image credit: Datamartist

 

 

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One comment

  1. Another great post Liz!

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