Reducing bad data incidents by 90% for a growing SaaS company.

01Challenges

The company had invested heavily in analytics, but trust in the data was eroding. Teams spent more time validating numbers than acting on them, and every dashboard refresh came with uncertainty.

Broken pipelines were discovered by executives. The first sign of a problem was often a Slack message from leadership asking why yesterday's revenue had dropped by 40%. In reality, a pipeline had failed overnight. Data engineers were finding out about issues after the business did.

Nobody knew whether the data was right. Reports loaded successfully, but there was no way to know whether they were complete. Missing records, duplicate events, and schema changes frequently slipped through unnoticed and appeared in dashboards.

Hours spent chasing the root cause. When an issue appeared, engineers manually traced data through ingestion, transformation, and reporting layers. A single investigation could consume half a day and involve multiple teams.

Trust in analytics was falling. Customer success, product, and finance teams increasingly exported raw data into spreadsheets to verify metrics themselves. The company had analytics, but people didn't trust it.

They didn't have a reporting problem. They had a confidence problem.

02What we built

03The outcome

Instead of discovering problems through executive complaints, the team now identifies and resolves issues before they reach dashboards. Analysts spend their time analyzing data rather than validating it, and leadership has confidence in the numbers used to make decisions.