FTE Debt™ Calculator
Your Best People Spend Half Their Week on Data Housekeeping. Here's What That Actually Costs.
I've sat in enough boardrooms to know one thing. Nobody hired a VP of Marketing to reconcile spreadsheets on a Sunday night. Nobody hired a CFO to spend three hours "sanity-checking" a dashboard. It keeps happening anyway. I call it FTE Debt™. Once you see it, you can't unsee it.
Prefer to be walked through it? Take the Reality Check instead. Four minutes, anonymous, no email needed. Already collected hours from your team leads? Use the Data Hours Worksheet.
Define the Friction
Step 1 of 2Who in your organisation keeps getting pulled into data cleanup, report-fixing, or "just quickly checking the numbers"? Add them below. You'll probably be surprised how quickly it adds up.
Pick the seniority level most affected
How many at this level deal with data friction?
Monthly hours on cleanup, validation, or workarounds
Used for financial impact. A rough average is fine.
Pick the seniority level most affected
How many at this level deal with data friction?
Monthly hours on cleanup, validation, or workarounds
Used for financial impact. A rough average is fine.
Your Results
Step 2 of 2Your Annual Friction Tax
€118kDollar Debt Impact per year
Weighted hours lost per year
People's worth of time trapped in friction
Raw unweighted hours per month
Here's where it gets expensive. Your managers and directors are stuck doing data plumbing instead of actually leading. That roadmap? It's gathering dust.
42% of your weighted friction sits with senior leaders (Director and above). That's your most expensive capacity doing the lowest-value work.
"This isn't a number on a page. Behind every hour here, there's a product feature that didn't ship, a market signal that got missed, or a team lead who went home wrecked from work that wasn't even their job."
Where the Hours Go
It's Not a Skills Problem. It's a Structural One.
In my experience, every hour you calculated above falls into one of three patterns. Data teams measure what they ship. I measure what the rest of the organisation suffers through.
Shadow Data Work
The Pipeline Gap
Someone in marketing built a Google Sheet because the 'real' pipeline was six sprints away. That was two years ago. The sheet is still running. Sound familiar?
Validation Overhead
The Trust Deficit
Your team double-checks every number before putting it in a deck. Last quarter, a dashboard was wrong and nobody caught it until the board meeting. Once trust breaks, you pay for every insight twice.
The Last Mile Gap
The "So What?" Problem
The dashboard exists. The data is technically there. But your regional manager still exports to Excel and builds a pivot table to answer the actual business question. Data delivered ≠ decision made.
How I Count It
Why 10 Hours Matters, and Why Seniority Changes Everything
Not every hour of data work is "debt." If someone spends 30 minutes a day on data tasks, that's just… work. It becomes a problem when it crosses 10 hours a month. That's when it stops being incidental and starts being structural. Call it the noise floor.
The other piece people miss: an hour of a VP's time is not the same as an hour of an analyst's time. Not because one matters more as a person, but because the opportunity cost is wildly different. When your Director of Product is reconciling data, nobody is doing the Director-of-Product job. That's why I apply a Role Multiplier (1.0× for ICs up to 5.0× for executives).
The equation behind the numbers:
FTE Debt = Σ (Monthly Friction Hours × Seniority Weight) ÷ 160
The result tells you how many full-time people you think you hired versus how many are actually available to do the work you hired them for.
Want the role-by-role breakdown emailed to you?
First name, work email, company. I'll send the table above with the weighting behind each row, so you can put it in front of the people who own the budget.
This Doesn't Fix Itself.
You're looking at roughly €118k a year in capacity that's going to data housekeeping instead of the work that actually moves the needle. That's an estimate from your own inputs. The next step is measuring it.
The way in
The Data Cost Baseline
Three weeks, €12,000 fixed. The total annual cost of running data across infrastructure, absorbed hours and everything already built. Cost per product for your top 20, a ranked keep / kill list with the recovery attached to each candidate, and the template so your team can rebuild the number next quarter without me.
Start the Baseline