Most CEOs calculate data team cost as salaries plus infrastructure plus tools. That number is wrong by 25–40%. The real cost includes all the invisible work your business teams do to compensate for data that doesn't quite fit, hours that never show up on any invoice, never land in any budget conversation, and compound silently every month.
Here is how to find that number and what to do with it.
The accounting trick that hides the real cost
When your CFO looks at the data budget, they see a clean set of line items: headcount, cloud infrastructure, tooling licenses. What they do not see is the parallel system your business teams have quietly built to make the official one usable.
Your marketing team has a campaign dashboard. They also have a spreadsheet. The spreadsheet exists because the dashboard has a data quality problem that has been "on the backlog" for six months. Someone in marketing spends time every week maintaining that spreadsheet, validating numbers before they share them externally, reconciling two versions of the truth.
That time is paid for. It just is not booked anywhere. The salary absorbs it silently.
This is what I call FTE Debt, the accumulated organizational cost of data friction on the business side. Not the cost of the data team. The cost the rest of the company pays because the data products they rely on do not quite fit how they actually work.
A concrete example
I had a conversation recently with a founder who was proud of his data setup. Modern stack, good team, proper tooling. When I asked him to walk me through how his marketing team actually used the data, a different picture emerged.
Data from Facebook and Google flows into the warehouse. It gets combined with website events. Then it gets shared with the marketing team, who immediately pull it into a spreadsheet to quality check it before doing anything with it.
Fifteen hours a week. One team. Just to validate data before they can use it.
That is not a data team problem. That is an FTE Debt problem. The marketing team is paying a tax on every decision because the data product was built correctly from a technical standpoint, but not from the perspective of how the business actually works.
Multiply that across every team that touches data. Finance, operations, product, sales. Each one has its version of the spreadsheet, the workaround, the shadow system. None of it shows up on the invoice.
The formula
FTE Debt is intentionally simple to calculate:
FTE Debt = People × Friction% × Average fully-loaded cost
- People: anyone who regularly builds with, requests, or waits on data, not just the data team.
- Friction %: the share of their time burned on workarounds, reconciliation, validation, and waiting. In most growth-stage companies this sits between 25% and 45%.
- Average cost: fully-loaded annual cost per person, salary, benefits, tools, overhead.
Run the numbers for your own organization. Take 30 people, 35% friction, €85k average cost. That is just under €900k a year, the current price tag on capacity that is producing nothing. No decisions. No growth. Just maintenance of a workaround.
That number is your FTE Debt for the period.
Why this always surprises CEOs
Three reasons the size of FTE Debt shocks the room when I first calculate it with a leadership team.
It compounds invisibly. Each new tool adds a reconciliation tax. Each new dashboard adds a maintenance tax. Each new team adds a translation tax. None of these show up on an invoice. They show up in the calendar.
It hides inside "we need to hire." The instinctive response to data slowness is to add analysts. But adding people to a high-friction system multiplies the friction. You buy more capacity, waste a larger fraction of it, and the dashboard count grows faster than the decision count.
It is socially expensive to name. FTE Debt is not anyone's fault. It is a systemic property of a portfolio that grew faster than its decision architecture. Naming it feels like blaming the data team, which is why most organizations let it compound in silence.
What FTE Debt is not
It is not "your data team is slow." It is not a talent problem. It is not technical debt by another name, technical debt is in the code, FTE Debt is in the calendar.
It is also not the same as low utilization. A team can be 95% utilized and still have 40% in FTE Debt. Utilization measures whether they are busy. FTE Debt measures whether the busy work is moving a decision.
What to do with the number
FTE Debt is a diagnostic, not a target. Once you have the number, the question is not "how do we reduce this metric." The question is: for each chunk of friction, which decision was this serving?
Most of the time the answer is "no decision in particular." That is where the recovery starts. You stop funding the dashboards nobody uses to make decisions nobody is ready to make.
For the friction that is serving real decisions, the question becomes: what would it take to halve it? The answers are almost never glamorous. Cleaner naming. Fewer dashboards. A documented decision protocol. Stronger ownership of each data product. None of it requires a tool purchase.
The CFO conversation changes the moment this number is on the table. FTE Debt is denominated in euros. It is auditable. It compares cleanly to any other capacity investment. And it answers the question every CFO eventually asks of a data budget: what would happen if we did not spend this?
When FTE Debt is named, the answer is honest: most of the spend is currently funding friction, not decisions. That is when the real conversation starts.
The next step
If you want to run this calculation for your own organization, the FTE Debt Calculator on this site lets you model it with your own numbers in under two minutes.
If the number you land on is large enough to warrant a conversation, book a 30-minute call. No slides. We map where capacity is leaking and what it would take to recover it.