What causes bad data in data warehouses?

Entry errors, migrations, encoding fails, truncations, network glitches—pick your poison in distributed pipelines.

How much does bad data really cost companies?

Gartner says $12.9M average annually; MIT Sloan: 15-25% revenue loss.

Best tools to fix bad data quality?

Great Expectations for open-source rules, Monte Carlo for ML observability, dbt for transformations—start with GE if you're bootstrapping.

☁️ Cloud & Databases

Bad Data's $12.9 Million Sting: Why Fixes Fail and What's Next

A single mangled postcode ripples into millions lost. Here's why bad data isn't just sloppy—it's a silent revenue killer, and the fixes aren't what you think.

Open Source Beat Apr 11, 2026 4 min read

Cracked data records spilling from a massive warehouse server

⚡ Key Takeaways

Bad data costs firms $12.9M yearly on average, eroding 15-25% of revenue. 𝕏
Tools like Great Expectations and Monte Carlo shift from reactive fixes to proactive observability. 𝕏
Repair strategies range from cheap stats to ML or humans—choose by risk tolerance, but fix ETL roots first. 𝕏

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#Great Expectations #Monte Carlo #bad data #bad data costs #data observability #data quality

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Originally reported by Dev.to

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