🧑‍🎓 How to Scale Data Quality with Data Observability + Data Testing

Data testing is important, but it’s only the first step toward data quality at scale. Implementing data observability in addition to data quality tests ensures data reliability not just across your smaller pipelines, but across your entire distributed data system.

Hear from Monte Carlo experts, Sales Engineer, Scott O’Leary, and Customer Success Manager, Scott Lerner, as they walk us through why your organization must rely on both data testing and data observability to meet the needs of today’s data teams expectations.

Join us to learn:

• The differences between data testing and data observability, and how to know when to use one vs the other
• Common use cases from leading teams already scaling data quality with data observability and data testing working together
• Tips and best practices for getting the most out of this two-pronged approach to data quality
• And more!

As your data organization scales, data testing alone can’t save you from data downtime. Where testing falls short, data observability fills the gap, providing an additional layer of visibility into your entire data stack. Attend live to chime in with questions and join in on the conversation. See you there!