Measuring
Data Health with Elementary

Communicating and sharing data health with analysts and consumers is critical because it ensures everyone—from technical teams to business stakeholders—understands the reliability of the data they rely on. Without clear communication, it's difficult to build trust in the data or align teams on where to focus reliability efforts.

In this webinar, we will dive into the unique challenges of measuring data health in dbt, where flexibility can create complexity. We will introduce a framework developed by our community to help measure and improve data quality and show how to implement it in your data workflows.

Agenda

1. The problem - why data teams need to measure data health?
   1. How do we communicate data issues at a high level?
   2. How to find mutual language with stakeholders and consumers?
   3. How do you measure your data quality?
   4. How to uncover coverage gaps
   5. How do you know where to focus your reliability efforts?
   6. How do you know you are making progress over time?
2. Why is it hard for analytics engineers to measure data health?
   1. The Wild Wild West in dbt - freedom created a mess
3. The approach we learned from our community
   1. Quality dimensions framework
   2. A high-level explanation of the framework
4. How we implemented this approach into the Elementary Cloud Platform and how users should use it
5. Q&A