Reliability over Performance
Unreliable code scales mistakes. Reliability scales value.
The dashboards looked great. Clean, fast, exactly what leadership asked for.
And somewhere beneath that polished surface, a silent flaw was already scaling.
It happened in the middle of a high-stakes transformation—an enterprise data platform rebuild with global visibility. The goal was ambitious: consolidate systems, modernize reporting, and get critical metrics into the hands of leaders faster than ever before.
I was leading the BI effort and feeling pretty confident. I’d pulled off complex migrations before, survived version mismatches, untested assumptions, surprise schema changes—all of it. This was just the next evolution.
We rebuilt the logic for margin calculation across regions. It was efficient, lean, and ran 60% faster than the previous solution. Everyone loved it—until the calls started coming in.
First from a regional manager who noticed the numbers didn’t match the ledger. Then another. Different regions. Different currencies. Same issue.
The logic had been written to favor performance: fast lookups, cross-joins cached in memory, pre-aggregated values designed for speed. But the business rules had shifted. A rebate program was added mid-quarter, and the new model didn’t account for it. The pipeline ran fine. The results were wrong.
By the time we traced it, we’d overstated profitability by several percentage points. No one had caught it sooner because everything looked right. But it wasn’t. And the cost wasn’t just financial—it shook trust.
That night, I stayed late and audited the entire stack. I wrote a script to test assumptions, one layer at a time. Nine hours later, I had a list: logic that silently failed when rates changed, metrics that drifted under new business rules, and jobs that hadn’t triggered alerts because they hadn’t technically failed.
From that moment on, my philosophy changed.
I stopped building for “fast enough” and started building for resilient clarity.
Later, when I led a migration to a new cloud platform, we didn’t just port logic—we rewrote it with validation checkpoints, test coverage, and SLAs on every output. When a stakeholder asked if we could shave off a few seconds by skipping a validation step, I said:
“Only if you’re okay betting trust on it.”
Because I’ve seen what happens when performance wins the battle but loses the war.
Now I mentor engineers with this simple truth:
Reliability scales trust.
Performance without trust is theater.
We don’t build dashboards to impress.
We build them to be believed.