Businesses collect more and more data every day to drive processes like decision-making, reporting, and machine learning (ML). Before cleaning and transforming your data, you need to determine whether it’s fit for use. Incorrect, missing, or malformed data can have large impacts on downstream analytics and ML processes. Performing data quality checks helps identify issues earlier in your workflow so you can resolve them faster. Additionally, doing these checks using an event-based architecture helps you reduce manual touchpoints and scale with growing amounts of data.













