Most teams discover data quality issues when dashboards break or decisions are wrong.
When your data lives across Postgres, MySQL, Snowflake, and BigQuery, you need automated data quality monitoring that catches issues before they impact decisions.
The 5 data quality checks that matter
1. Completeness
Completeness checks verify that required fields are populated and there are no missing values where data should exist. They identify gaps in time-series data that could indicate pipeline failures or missing updates.
2. Accuracy
Accuracy checks ensure values make sense and aren’t corrupted during processing. They identify outliers that indicate errors rather than legitimate data points. Most importantly, they verify that calculations match expected results, catching transformation errors.
3. Consistency
Consistency checks verify that values match across systems, ensuring data integrity when the same information exists in multiple places. They detect duplicate records that could skew analysis. They also identify conflicting definitions that need to be resolved.
4. Timeliness
Timeliness checks verify that data is arriving on schedule without delays in data pipelines. They ensure data is fresh enough for the decisions you need to make, alerting when data becomes stale.
5. Validity
Validity checks ensure values match expected formats like email addresses or phone numbers. They detect type mismatches where data doesn’t match the expected schema. Most critically, they identify constraint violations that indicate data integrity problems.
What to build first (week 1)
Start with a simple data quality monitoring system that includes schema validation to check that table structures match expectations. Add row count monitoring that alerts on unexpected drops or spikes that could indicate data loss or duplication. Implement null checks that alert on unexpected nulls in required fields. Add value range checks that alert when values fall outside expected ranges. Finally, add freshness checks that alert when data becomes stale.
Once you have these basics, add cross-system consistency checks to verify data matches across different sources. Implement duplicate detection to identify records that appear multiple times. Add anomaly detection to catch unusual patterns that might indicate problems. Finally, add data lineage tracking to understand how data flows through your systems.
Why most data quality monitoring fails
Most data quality monitoring fails because checks are manual, run once and then forgotten as teams move on to other priorities. Alerts are noisy with too many false positives that cause alert fatigue and make real issues easy to miss. Context is missing when alerts don’t explain what changed or why the issue occurred. Most critically, actionability is low when monitoring shows issues but doesn’t indicate how to fix them.
When you automate data quality monitoring, you can catch issues before they impact decisions by running checks continuously. You can reduce false positives with better context that explains why an alert fired. You can track data quality over time to understand trends and identify systemic issues. Most importantly, you can improve data quality proactively by addressing root causes rather than just symptoms.
The hidden cost of poor data quality
When data quality is poor, decisions are wrong because they’re based on bad data that doesn’t reflect reality. Trust erodes as your team loses confidence in the numbers and starts questioning every metric. Time is wasted debugging bad data instead of focusing on analysis and insights. Most critically, opportunities are missed because you can’t act on insights when you don’t trust the underlying data.
Good data quality means decisions are right because they’re based on reliable data that accurately reflects your business. Trust is high because your team knows they can count on the numbers. Time is saved because you’re not constantly debugging data issues. Most importantly, opportunities are captured because you can confidently act on insights knowing the data is reliable.