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.
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
Gartner® Market Guide for Data Observability Tools
Bigeye is proud to be recognized as a Representative Vendor in the Gartner® Market Guide for Data Observability Tools.
Access the report to explore:
- The critical features and capabilities of data observability
- Understanding the market, including its direction, analysis and vendors
- Recommendations for D&A leaders when evaluating solutions
