How to tackle data quality: a three-phase approach


Data quality incidents slow analysis, damage dashboards, break applications, and impact machine learning model performance. But data quality is a big challenge and attempting to tackle it all at once can make it hard to show meaningful results.

Egor Gryaznov, CTO and co-founder of Bigeye, discusses a three-phase approach to addressing data quality, including how to put in place a solid toolchain and process for showing traction at each phase. 

Watch this webinar learn a three-phase approach to addressing data quality, including:

  • An in-depth look at the three phases of data quality: operational quality, logical quality, and application quality.

  • A grasp of the toolchain and process needed to address each phase of data quality

  • A look at how Bigeye can help address operational data quality and more

Hosted by:

Egor Gryaznov
Co-founder and CTO, Bigeye
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