This episode of Techsplainers introduces data observability, the practice of monitoring data health across an organization. We explore why data observability matters, with 80% of executives distrusting their data and companies like Unity Software losing $110 million due to bad data. The discussion covers the three stages of the DataOps cycle (detection, awareness, and iteration), the five pillars of data observability (freshness, distribution, volume, schema, and lineage), and how data observability differs from data quality and governance. We also examine the hierarchy of data observability and provide a practical roadmap for implementing a data observability framework to ensure reliable, trustworthy data for better business decisions. 

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo