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