What would you do differently if you could actually trust your data?

What would you do differently if you could actually trust your data?

At some point, most HR and TA leaders stopped trusting their reports. Gradually. A report came back with numbers that felt off, so they cross-checked it against a spreadsheet.

Then they started doing that every time. Then the cross-check became the process.

The double-check is the tell. When verifying your own system's output becomes routine before you act on it, you've absorbed manual audit work into every decision your team makes. The question worth sitting with is: what would you do differently if you didn't have to?

If you trusted your time-to-fill data, you'd have different conversations

Time-to-fill is the metric most TA leaders care about most and trust least. The data is usually wrong in one of two ways: incomplete or inconsistent.

Incomplete because roles get filled before they're properly logged, or internal moves don't make it into the system. Inconsistent because stage definitions were set at implementation and the hiring process has changed since.
Decisions get deferred. Strategic initiatives that depend on reliable data get delayed because nobody wants to build on a foundation they can't fully trust.

The HRIS version of the double-check is just quieter. The cost is the same.
Every double-check is a cost you're not counting
The hours your team spends cross-referencing the ATS against a spreadsheet and rebuilding reports the system should generate automatically don't show up as a line item anywhere. They're threaded through everyone's week in small increments.

But they compound. A TA team that's operated with unreliable data for 18 months hasn't just absorbed the manual reconciliation hours. It's made 18 months of hiring and staffing decisions with less information than it should have had. Some of those decisions cost money. Some of them are sitting in the current headcount as the wrong person in the wrong role.

Fixing the data problem is a business investment with a measurable return. Someone needs to calculate the return before deciding whether to make it.

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