Poor data is expensive. It leads to faulty forecasts, wasted resources, and erodes trust in BI systems. Yet data quality often remains invisible and difficult to measure.

In this session, I show how to escape this “data wonderland” by building a Data Health Dashboard in Power BI that makes data quality transparent and actionable.

We start with the key dimensions of data quality such as completeness, accuracy, consistency, timeliness, and uniqueness, and explore how they can be translated into a scalable data model. You will see how targeted column-level checks can be implemented using DAX measures and how the results can be consolidated into a single monitoring layer.

Finally, we look at visualization techniques that highlight critical issues immediately and allow users to drill down to the underlying data problems.

By the end of the session, you will understand how to make data quality measurable, continuously monitor it, and use it as a foundation for stronger data governance and AI readiness.

Target Audience
BI analysts, Power BI developers, data engineers, and data governance teams

Key Takeaways
- Which data quality dimensions matter most for BI
- How to implement scalable data quality checks with DAX.
- How to design a Data Health Dashboard that makes data issues visible and actionable