How to Tackle Data Quality: A Three-Phase Approach

Anyone who has spent time working with data has seen the negative effects of data quality. Data quality incidents slow analysis to a crawl, damage executive dashboards, break user-facing applications, and impact machine learning model performance. Data quality is a big problem and attempting to tackle it all at once can make it hard to show meaningful results. In this webinar, Egor Gryaznov, CTO and co-founder of Bigeye, will discuss 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. Join 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