Data quality discussions often miss a critical distinction. Yes, there’s plenty of genuinely bad data that needs cleaning and validation. But there’s also a widespread problem of using the wrong data to answer questions it cannot answer - then dismissing entire datasets when that use case fails.
This creates two costly mistakes. First, organizations discard potentially valuable data because one application didn’t work. Second, they fall into the “big data fallacy” - believing more data automatically fixes quality issues, when it can actually amplify existing errors and biases.
The solution isn’t more data or perfect data. It’s understanding that the same dataset can have excellent and terrible use cases depending on the question asked. When you understand the nature and direction of errors across multiple datasets, geography, an…
