Keeping the Pulse of Your Data: Why You Need Data Observability to Improve Data Quality
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Watch Julie Skeen and Michael Sisolak from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics, and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
- Data observability – what is it and how it can complement your data quality strategy
- Why now is the time to incorporate data observability into your DataOps strategy
- How data observability helps prevent data issues from impacting downstream analytics
- Examples of how data observability can be used to prevent real-world issues