A while ago I was asked to collect metrics on developer throughput. I built the dashboard, pulled the numbers, and stared at the indicators.
Some were up. Some were down.
I remember thinking, "Does this even align with what we're actually being asked to improve? Or did we choose a metric that already pointed to the outcome we expected?"
Nothing was settled.
We had selected a measure, interpreted it quickly, and almost moved forward as if it had told us something definitive.
All that had really happened was this: a number changed, and we were tempted to treat it like a conclusion.
The shift you don't notice
Most of us don't become "data-driven" because we love spreadsheets. We do it because we want to be fair.
Metrics feel cleaner than opinion. They protect you from favoritism. They give you language in executive meetings that doesn't rely on gut instinct. When you're responsible for promotions, budgets, hiring plans, that clarity matters.
I've built KPI frameworks. I've argued for them. I still think they're necessary.
But there's a shift that happens slowly. It starts with, "What does the data suggest?" And ends with, "The data proves it."
That change is small enough that you don't notice it happening.
You start telling yourself the high performer was always going to rise. The struggling team was clearly trending toward failure. The strategy that worked was obviously the right call from the beginning.
It begins to feel inevitable.
And inevitability is comfortable.
What the dashboard doesn't show
The problem isn't that metrics are wrong. It's that they are selective.
They measure what we chose to measure. Velocity. Conversion. Revenue. Throughput. Retention. Those things matter. But they are not the whole story.
The engineer who quietly prevents outages rarely tops a velocity chart.
The team experimenting with a risky idea often looks inefficient before it looks brilliant.
A new product direction can look like a mistake in its first quarter and like foresight in its third year.
When you spend enough time leading through dashboards, you can start to believe that if something isn't visible there, it isn't real.
That's where creeping determinism sets in.
You begin to treat the measured outcome as destiny rather than one slice of context.
Hindsight feels smarter than it was
After something works, the data looks cleaner than the moment ever did.
After something fails, the warning signs seem obvious. It's easy to forget how uncertain it felt in real time.
The graph doesn't show the late-night debate that changed direction. It doesn't show the personal crisis someone was dealing with. It doesn't show the mentor who intervened at exactly the right time.
When we narrate the past purely through metrics, we smooth out the uncertainty that was actually there.
And when leaders start believing that the past was predictable, they start assuming the future will be too.
That's the determinism part.
What it does to decision-making
Creeping determinism doesn't turn leaders into villains. It turns them into optimizers.
You fund what already looks strong. You promote the person whose chart trends cleanly upward. You shut down ideas that wobble early.
On paper, it looks disciplined. Over time, it narrows the range of bets you're willing to take. You become very good at improving what already works.
You become less comfortable backing what doesn't yet show up as a tidy line moving in the right direction.
The organization becomes more predictable.
Predictability has value. It keeps payroll steady. It makes quarterly planning easier. It lowers the emotional temperature in leadership meetings. It means fewer surprise Slack messages at 9 p.m.
But it also quietly teaches you to avoid ambiguity. And most meaningful shifts start out ambiguous.
A small correction
I don't think the solution is to loosen standards or ignore data. That swings too far the other way.
What I've had to remind myself is simpler than that. Data informs. It does not decide.
A drop in engagement might mean the feature failed. It might mean the wrong users saw it first. It might mean the market shifted. The chart can't tell you which.
A steady performance trend might signal consistency. It might also hide burnout.
I once had a team whose delivery metrics flattened for two quarters. On paper, it looked like stagnation. The instinct was to intervene. New targets, new pressure, maybe new leadership.
I was close to doing exactly that.
What the dashboard didn't show was that they had spent those months untangling brittle infrastructure that had been slowing everyone else down. Their visible output dipped. The organization's resilience increased.
If I had optimized for the chart in that moment, I would have penalized the very work that made future velocity possible.
That's when it clicked for me. The risk wasn't the metric itself, it was how quickly I was ready to let it stand in for my own judgment.
Metrics narrow the field of interpretation. Leadership means widening it again before you act.
If you stop at the dashboard, you become a very efficient reader of outcomes.
If you go beyond it, you're still required to use judgment. That part never goes away. I still refresh dashboards. I still care about the arrows. I just try not to treat them like fate.
Because the moment you start believing the numbers make the decision for you, you're no longer leading.
You're just confirming what already happened.