How to choose a Deep Data Observability platform—a comprehensive guide
Bad data is the number one pain troubling data teams today. In response, there has been a proliferation of data quality content, companies and opinions emerging from left and right. There’s a myriad of ways to describe the important but somewhat sprawling set of processes that can be defined as data validation. We see terminologies like data observability, data reliability, data quality monitoring, data validation, data lineage, and more being used interchangeably and inconsistently.
We decided to ask modern data teams what they actually need to comprehensively validate and monitor their data quality in a scalable way. The findings in this report are based on dialogues with +100 data teams globally that we've condensed into one concrete report, forming a framework to use when evaluating Data Observability platforms.
