For the CFO
The business case, with the arithmetic
You should not be reading the business two weeks after it happened, and you should not have to take a vendor's word for what it saves. Here is the whole case in numbers you can check.
Last reviewed 5 August 2026
The case in four lines
It is a substitution, not a new line item.
What you spend today on producing information is mostly payroll, so it never gets scrutinised the way a license renewal does. Total it once and the comparison becomes simple.
Then three things change on your desk: you see the business every morning rather than two weeks after close, variance arrives with its cause already computed, and every figure traces to its source and reconciles to the ledger to the cent. SQOR sits beside your audited books and never writes to them.
The substitution
Both sides, same basis.
Replace our headcount assumption with yours. The structure of the comparison is what matters, not our number.
| Today | With SQOR | |
|---|---|---|
| Data and reporting people, six at a fully-loaded US median | $937,800 | the production work moves to the platform |
| Of which, data preparation and cleaning | about $420,000, recurring | included |
| Platforms: extraction, warehouse, transformation, quality, orchestration, BI | five or six contracts | one, warehouse included |
| Platform cost, 25 users, all-in | $48,960 | |
| One-time onboarding | Nominal, covers the proof of concept | |
| Time to first findings | months | days |
People figures use the US Bureau of Labor Statistics median annual wage for data scientists of $120,230, May 2025, at a 1.3 fully-loaded multiplier. The preparation share uses Anaconda's State of Data Science, which has measured 37 to 45 percent of time going to data preparation four years running.
Payback
Two numbers your investment committee will ask for.
Set-up is recovered in the first weeks, not the first year
Against the cheapest fully-loaded stack on our comparison pages, the annual difference is more than $175,000 a year. Set-up is a nominal fee that covers the proof of concept, so it is recovered in the first weeks. That is arithmetic on figures published per competitor on those pages, not a projection.
The recurring saving is the preparation line, not the license line
The license difference is visible and modest. The larger figure is the recurring cost of getting data ready, which does not appear in any software budget and repeats every month.
The upside is separate and should be tracked separately
Recovered margin, closed leaks and faster decisions are real and they are not in the substitution math above. Name one, put a number on it, and check it afterwards. A return you cannot point at is not a return.
The risk is bounded and short
Read-only connection, nothing written to any source system, and no migration to reverse. If the first use case does not deliver you stop, and there is no infrastructure left behind and no team to unwind.
What changes on your desk
Four things stop being someone's week.
01
The pack assembles itself
Figures are computed continuously and reconciled before they serve, so the monthly pack becomes a view rather than a project.
02
Variance arrives with its cause
The drivers behind a movement are computed and ranked rather than narrated afterwards, so the explanation does not change depending on who writes it.
03
Leaks surface in days
Recognition gaps, discounting outside policy and cost drift are found by looking continuously across every product and location, which is the only way they are found at all.
04
The forecast updates itself
Rolling rather than quarterly, computed from the same drivers that explain the number rather than a trend line somebody extended.
What to put in front of a board
Four slides, in this order.
One, the current fully-loaded cost of producing information, including the payroll portion, as a single number. Most boards have never seen it as one number and that alone changes the conversation.
Two, the substitution, both sides on the same basis, with the payback period stated. Three, one specific leak or decision you expect to change, with a figure attached and a date to report back on. Four, the boundary: read-only, sits beside the audited books, nothing written, and what happens if you stop.
Then report back on slide three whether it worked. That last step is what separates a business case from a purchase, and it is the reason most analytics spend has never been able to demonstrate a return.
Questions
The CFO case, asked plainly.
What analytics does a CFO actually need?
Three things, in order. A daily view of the business rather than a monthly pack read two weeks late. Variance that arrives already explained, with drivers computed and ranked rather than narrated afterwards. And traceability, meaning every figure ties to its source and reconciles to the ledger, because a CFO cannot use a number they cannot stand behind. Dashboards deliver the first if somebody maintains them, rarely the second, and almost never the third.
How do I build the business case for an analytics platform?
As a substitution. Total what producing information costs you today, including the payroll portion that never gets scrutinised, then put the platform cost against it and state the payback period. Then name one specific leak or decision you expect to change, attach a number, and commit to reporting whether it changed. The last step is what makes it a business case rather than a purchase.
What is the payback period?
The subscription is $48,960 a year for 25 users all-in, and set-up is a nominal fee that covers the proof of concept. Against the cheapest fully-loaded conventional stack itemised on our comparison pages, the annual difference is more than $175,000 a year, so the set-up fee is recovered in the first weeks. That is arithmetic on published per-competitor figures rather than a projection.
How do I close the books faster?
Most of the close is finding and reconciling rather than accounting. When the questions of which accounts moved and which entries look wrong are answered continuously, the close becomes confirmation instead of investigation. SQOR sits beside the books and never writes to them, so it shortens the finding without touching the record.
Can I trust an AI number in financial reporting?
Only if a language model is not producing it. Machine learning computes each figure and reconciles it to your source, the language model explains it and never holds it, so the same question returns the same number and no figure is ever generated. When a figure cannot be reconciled the platform withholds it and says so. That property is the one to insist on from any vendor, and most cannot describe it.
What should go in a board pack?
What changed, why it changed, what it means for the forecast, and what you are doing about it, with every number traceable. The common failure is volume: forty pages of charts and no causation, which invites the board to do the analysis in the room. Lead with the three numbers that moved and the computed driver behind each one.
Bring the number you least want to defend.
Read-only, on your own ledger and subledgers. We will show you what it is, what moved it, and where it traces to.