A guide to better data quality

Learn how to build confidence in the quality of your data

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What's covered

In this white paper, we’ll explore what we mean by ‘data quality’, why it’s fundamental to data projects such as AI and personalization, and how you can collect complete, accurate data.

  • The importance of well-defined schemas

  • How data ownership can influence data quality

  • The roles that validation and testing play in improving data quality

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We encourage you to learn more about Snowplow and how you can leverage our data pipeline to ensure high quality data for your business.

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“Data quality is an urgent issue, the most time consuming, the most painful and it slows us down the most. We find that it’s important to detect and surface data quality issues, make them easier to visualize and more transparent, not fix them.”

Rahul Jain, Principal Engineering Manager,