6 AI & Data Quality
Implementing data quality rules in data engineering has traditionally been a fragmented, tedious process. You write condition after condition, stitch together SQL CASE statements, loop through rule sets in Python, and maybe bring in a reference file or third-party tool for the messier checks. At its most elegant, it’s still a complex dance of thresholds, logic branches, and duct-taped integrations. In many teams, it doesn’t get done at all, or gets handed off to specialized vendors or expensive platforms that exist solely to clean and validate data.
But AI introduces a new way forward. Instead of building a different rule for every scenario using different tools, you can describe your expectations in a single conversational prompt and use one model to handle a wide variety of data quality challenges. It’s flexible enough to flag missing values, invalid formats, contextual anomalies, all while remaining embedded inside your existing data engineering workflows. That means subject matter experts can contribute directly, encoding their knowledge without needing to write complex code.