Snowflake has been a game-changer for data-driven companies and data engineering teams of all sizes—near-zero maintenance, reduced silos, and performance under diverse workloads. To fully realize the advantages of Snowflake, your organization must trust the data inside. In this guide, we outline a three-phase approach to improving the quality of your data and how to measure improvement. In this guide, you’ll learn:
The steps you should to take to put a data quality process in place
Three techniques for measuring data quality: operational, logical, and analytical
Key indicators that show when it’s time to invest in a data quality program
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