The Root Cause of Data Friction
And the Tax You're Paying: Shitty Data
Let’s call it what it is: shitty data.
Not “data quality issues.” Not “data inconsistencies.” Not “legacy system challenges.”
Shitty data.
The kind that makes your CFO question the numbers. The kind that makes your engineers waste entire sprints fixing pipelines instead of building features. The kind that makes your CEO decide based on gut feeling because the dashboards can’t be trusted.
We’ve been dancing around this problem for years. We’ve given it polite names. We’ve blamed the tools, the team, the budget.
But the root cause is simpler — and more uncomfortable — than most companies want to admit.
What "Shitty Data" Actually Means
Shitty data isn’t just “wrong” data. It’s data that:
Lives in silos — so no one has the full picture.
Has no single definition — so “Active User” means three different things to three different teams.
Is manually verified — because nobody trusts the automated numbers.
Arrives too late — so decisions are made on last month’s reality, not today’s.
Breaks under scale — so what worked at 20 people collapses at 200.
It’s not a technical problem. It’s an alignment problem. And it’s the raw material of Data Friction.
How Shitty Data Becomes Data Friction
When your data is shitty, every layer above it suffers.
Strategy gets built on assumptions that don’t match reality. Execution gets built on requirements that were never properly translated. Decision-making slows down because no one agrees on the facts. And team morale drops because everyone is busy fixing symptoms, not solving causes.
Shitty data doesn’t stay in the database. It leaks into every meeting, every sprint, every strategy session.
And that leak has a cost — one we’ve explored before under a different name.
The Friction Tax, Revisited
If you’ve read the previous article in this series, you know about the Friction Tax: the hidden cost of misalignment between business strategy and technical execution.
Shitty data is what makes that tax so expensive — and so persistent.
The visible cost is easy to estimate. Add up the hours your leadership team spends reconciling numbers, debating definitions, or manually verifying dashboards. Multiply by their loaded salary. The number is rarely small.
The invisible cost is harder to measure — but far more damaging. Features that ship late. Campaigns optimized against the wrong metrics. Customers who leave before anyone notices the warning signs.
If you want the full breakdown of how to calculate it, I covered it in detail here.
Why Companies Tolerate It
If shitty data is so expensive, why does it persist?
Because it hides.
It looks like “normal operational noise.” It looks like “that’s just how it is.” It looks like “we’ll fix it when we scale.”
But every month you wait, the tax compounds. The pipelines get more tangled. The definitions drift further apart. The trust erodes.
And the worst part? The longer you tolerate it, the more expensive it becomes to fix.
What to Look For (Without a Full Audit)
You don’t need a three-week engagement to know if shitty data is a problem in your company. Start with these three signals:
1. The reconciliation ritual.
If your team spends the first days of every month manually verifying numbers, that’s not diligence. That’s a symptom.
2. The definition drift.
Ask three different teams what “active user” means. If you get three different answers, your data isn’t aligned. Your company isn’t either.
3. The late signal.
If your customer success team finds out about churn from a cancellation email rather than from usage data, your data is arriving too late to be useful.
These aren’t technical failures. They’re structural ones. And they’re fixable — but only once they’re named.
The Bottom Line
Shitty data isn’t a technical problem. It’s a business problem.
It’s the root cause of Data Friction. It’s what makes the Friction Tax so expensive. And it’s the reason so many scale-ups stall just when they should be accelerating.
The first step is naming it. The second is measuring it. The third is deciding what to do about it.
In the next article in this series, I’ll share the framework I use to map where the data breaks — and why.
#DataFriction #GrowthGap #ScaleUp #BusinessStrategy #TechExecution #MMTrufinStratEdge
