In every terminal or warehouse I’ve ever worked, there was some version of a dashboard that told us how well we were doing. It shone with colored post-its, or even digital lines on a big smart screen and presented itself as truth. I watched entire leadership teams plan around those numbers. People debated percentages, built strategies, moved budgets, and (sometimes mercilessly) razzed each other for not hitting their department’s marks. The data almost always came from people’s answers. And people are storytellers first.
Don’t get me wrong, most of it was computer-generated, but if you pulled the thread far back enough, you’d usually find somebody entering numbers that eventually wound through the data analysis engine to the final report.
Everybody pretty much knew those reports were BS. Surveys claimed engagement was up, though morale was visibly low. Strengths assessments became badges for personality, then self-fulfilling prophecies. Everyone had a type, a quadrant, a color. None of it held up under the weight of actual work. We had built a culture of measurement without accuracy.
The human mind is generous with itself, editing, reframing, glazing over rough patches and inflating positives. We all desperately want to look competent in the mirror. Every self-report carries the residue of aspiration and the erasure marks of embarassing negatives once readable. Even anonymous surveys smell faintly of performance, when asked how we behaved, we recall who we think or wish we are.
Over time, these small distortions harden into structure. Metrics built on perception and begin steering real-world decisions. Promotions, policy, hiring, even product design start orbiting numbers that measure opinion. The propensity for AI to hallucinate is very similar in source and effect, interestingly.
I’ve seen companies celebrate record satisfaction scores one quarter before mass resignations. I’ve seen managers quote pulse-survey results to justify keeping a broken process. The mirror told them they looked fine. The reflection was flattering enough to ignore the cracks.
Truth lives in friction
It appears in missed deadlines, quizzical inconsistencies and (eventually) small corrections made without announcement. Observation captures what self-reporting hides and real data doesn’t arrive through a form; it shows up in behavior.
When my team at 10x Velocity conducts an audit for a client, we treat self-reporting as only one leg of the table. Without the other two, the whole thing wobbles.
The first leg is self-reporting, because people inside an organization see nuances outsiders never can. They know the workarounds, the tensions, the bottlenecks invisible to leadership and data culling systems alike. Their perspective matters, but it’s steeped in bias and personal history. It’s not their fault and I’m guilty of same…we all are.
The second leg is outside perspective. You can’t read the label from inside the bottle. External review cuts through the familiarity that dulls awareness. It sees what insiders have stopped noticing. This is why mixing departments during discovery can be powerful as well. That’s us in this scenario.
The third leg is task mining, the data that doesn’t care what anyone believes. It’s the unimpeachable record of what actually happens. How time moves. How systems behave when no one’s looking. That kind of truth isn’t emotional, but it keeps the other two honest.
Each leg alone can mislead when trying to assemble the whole truth. Together they stabilize the picture. You get what people feel, what an outsider observes, and what the work itself records. Anything less is a mirror pretending to measure.