Poor data quality is a pervasive issue, with studies estimating its cost at up to 25% of an organization's operating profit. Despite these staggering numbers, businesses often accept data quality issues as an inevitable reality.

While initial testing efforts during deployment is common, data quality tends to degrade over time and therefore requires sustained attention. This session introduces a practical framework to combat this challenge, leveraging ‘Great Expectations’, an open-source Python library designed for data quality testing.

The talk begins with an accessible discussion on the importance of maintaining high data quality and the foundational principles of the proposed framework. In the second part, we delve into a technical walkthrough of implementing Great Expectations in real-world scenarios. This session is ideal for anyone who uses or works with data, and attendees with a basic understanding of Python will gain the most from the hands-on examples.

Discover how you can turn the tide on data quality and drive better outcomes for your organization