When business users complain about the data, that's a good sign! It means they actually want to use it.

In this episode I talk with Uzi Blum (VP Data at Taxfix) about the four layers of data quality.

Key takeaways:

1. Three steps to quickly gain trust with your stakeholders: (1) Show them you understand their problems, (2) deliver results quickly (within weeks, not months), (3) focus on getting the most important metric right first.
2. Weekly active data users (WADU) in the organization is a good proxy metric for the trust people have in data. An aspirational metric might be share of decisions taken that are based on data.
3. Data quality can be measured by the share of incident-free days (reactive), or the share of data assets that are compliant with your quality standards, have monitoring in place, and are covered in the glossary (proactive).
4. To ensure quality on the row layer, we can use unit testing (to cover expected cases) and monitoring (to cover unexpected cases, e.g. with Great Expectations).
5. To discover problems before your stakeholders do, it can be effective to have a data team member on call to check data quality issues in the morning and give a "green light" when it's good to use.
6. Having a glossary with aligned definitions of all metrics can go a long way. Ideally, this is linked to your BI tool, to help users with the right context.
7. Guidelines for creating effective dashboards can also help with providing context (e.g. having clear titles and labels, highly visible filters and consistent color codings).

For more on this, check out these blogs by Uzi and the Taxfix team:

• Four Shades of Data Quality
• Why and how we created the Taxfix glossary of terms and metrics

Find Uzi and Valentin on LinkedIn.