When Data Discrepancies Become Dangerous

In a world where businesses run on data, discrepancies aren’t just inconvenient — they’re dangerous.

It starts small. A missing number in a dashboard. An outdated metric in a report. A chart that just doesn’t add up. But these seemingly minor issues can quickly escalate into something much larger: a complete erosion of trust.

When customers spot incorrect or inconsistent data, their confidence in the system falters. And when that happens early in their journey — during onboarding or the first few weeks of usage — it sets a tone that’s hard to reverse. No matter how slick the interface is or how powerful the analytics engine might be, if the data is wrong, nothing else matters.

In fact, data discrepancies have become one of the leading indicators of churn. Teams don’t always vocalize it, but when they stop trusting the numbers, they stop using the product. They return to spreadsheets, ping the data team for one-off exports, and quietly abandon the tools they were excited about just weeks before.

Fixing this isn’t just about building more robust pipelines. It’s about shifting the mindset: treat data consistency as part of the user experience. That means proactively detecting issues before customers do. It means making debugging tools visible and usable. And it means aligning product, engineering, sales and customer success around a shared goal: don’t let bad data make it to the surface.

At TigerEye, we believe clean data starts with well-built software. That’s why we design for accuracy from the start — and also give customers real-time data health reports to flag issues early, whether they come from upstream systems or human error. Trust begins with transparency, and that means showing our work, not hiding behind it.

Because when data is wrong, it’s not just a bug. It’s an erosion of trust. And in a market full of options, trust is everything.

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