Why Data Cleaning Is Failing Your ML Models - And What To Do About It

Precise endeavors must be done to exacting standards in clean environments. Surgeons scrub in, rocket scientists work in clean rooms, and data scientists…well we try our best. We've all heard the platitude, "garbage in, garbage out," so we spend most of our time doing the most tedious part of the job: data cleaning. Unfortunately, no matter how hard we scrub, poor data quality is often too pervasive and invasive for a quick shower. Our research across the data stacks of more than 150 organizations shows an average of 70 impactful data incidents a year for every 1,000 tables in an environment.