Can You Rely on Rules?

When it comes to catching data errors, not all approaches are created equal. Traditional rule-based checks can find specific issues—like a needle in a haystack—but they don’t scale. Writing more rules leads to an endless loop of maintenance, missed edge cases, and false confidence.

That’s why we built Anomalo with unsupervised machine learning: to automatically detect unknown unknowns, surface meaningful anomalies, and help teams scale data quality without drowning in rule-writing.

But no approach catches everything. When you do need to find that needle in a haystack, metrics and rules still have a place—especially when they’re easy to configure with no-code tools.

While details change across customer deployments, certain broad issues come up again and again. They can be monitored with a combination of:

1. Data observability
2. Validation rule
3. Key metric
4. Unsupervised machine learning (ML)

Here’s why: you’ll cover a whole lot more issues with all four in use rather than just one or two.

This guide will help you choose the right method for the right problem, so you can stop chasing bad data and start trusting your insights.