This hits my bottom line directly. Just last month we had to discount a major client delivery by 30% because of data quality issues that made it through to the final report. That’s real money lost, not just technical debt.
From a business perspective, I’ve learned that data quality issues compound exponentially. One bad data point doesn’t just affect one analysis – it cascades through every downstream decision and erodes client trust. I’ve had clients question perfectly good analyses because they lost confidence after one data quality incident.
My approach now is to price data quality into every engagement upfront. We build comprehensive validation suites as part of project scoping, not as an afterthought. This means higher initial costs but dramatically fewer emergency fixes and client escalations.
The harsh reality is that “move fast and break things” doesn’t work when you’re responsible for business-critical decisions. I’d rather deliver a day late with validated data than on-time with garbage. One major data quality failure can cost you a client relationship that took months to build.
We’ve also started offering “data health checks” as a separate service line – essentially auditing other organizations’ data pipelines. It’s become surprisingly profitable because this problem is universal, and most companies don’t realize how bad their data quality actually is until someone systematically measures it.
