Bad data is the number one pain troubling data teams today, making it one of the unsolved problems in the context of the modern data stack. In response, there has been a proliferation of data quality content, companies and opinions emerging from left and right. Currently, there’s a myriad of ways to describe the important but somewhat sprawling set of processes that can be defined as data quality validation and monitoring. We see terminologies like data observability, data reliability, data quality monitoring, data validation, data lineage, etc being used interchangeably and inconsistently.
The vast majority of approaches presented today by various actors are limited in scope or effectiveness, and do not provide data teams with concrete guidance on how to select an appropriate data quality platform (DQP).
We decided to ask modern data teams with cloud-native data infrastructure 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.
This report presents a brand new exclusive framework based on a 26-item checklist for the capabilities that a data quality platform should provide in order to comprehensively help data teams obtain high quality data, and ultimately make better decisions, offer better products and gain trust in data.
All in all, this is a guide to selecting a data quality platform that will actually help data teams as their data aspirations grow and mature over time - with an emphasis on cutting through the hype. Regardless of whether you have small or large plans for data; ranging from a move into the real-time domain, or an increase in cross-functional collaboration to make the entire organization more data-driven.