Big data systems are growing in size, speed, and complexity — but the trust we place in them often lags behind. While engineers and analysts build pipelines to move petabytes of data, there’s an unspoken assumption: that the data is clean, correct, and complete. Unfortunately, that assumption often breaks in production.
From AI models trained on incorrect labels to business dashboards displaying misleading KPIs, untrustworthy data leads to real-world failures. In healthcare, it can misinform critical alerts. In e-commerce, it skews demand forecasts. And in finance, it triggers incorrect trades or noncompliance issues. That’s why data veracity — the accuracy and reliability of data — is not just a backend concern, but a business-critical one.
