The real dollar return
Data Foundation ROI Estimator
Enter a handful of numbers from the client’s business to estimate the impact of a data & analytics foundation — cash released, recurring EBITDA improvement, faster month-end close, and the enterprise value it creates. Every assumption is editable and cited, so the output stays defensible in the room.
The reframe
Priced like infrastructure — pays back like four things
A data foundation gets priced as one number on one date. Its return is spread across four places nobody adds up in the same sentence — the CFO’s working capital, the ops team’s recovered hours, the board’s confidence in the numbers, and next year’s AI spend. Because no single P&L line says “data foundation savings,” the whole thing defaults to the cost column.
The money already leaking
A cost they pay today, blind. The build redirects money that already bleeds out — an invisible cost, not a new one.
The direct operating return
Working capital released, labor recovered, close accelerated. The cleanest, most defensible math.
The amplifier
Durable EBITDA improvements are worth the exit multiple, not their face value. The PE lever.
The option value
Whether AI spend returns depends on the data beneath it — most enterprise pilots have yet to show measurable return. The foundation changes the odds.
Client inputs
Ask the client for these seven. Rough figures are fine.
▸ Assumptions (editable & cited)
Lever 4 · Option value
AI readiness — the multiplier on spend you’re already committing
You’ll spend on AI regardless. Whether that spend returns depends heavily on the data beneath it — most enterprise AI pilots have yet to show a measurable return. This is option value on money already committed, so it sits apart from the year-one total above.
Summary — copy for email
Run these in the room
The formulas, in one place
The exact math behind each output above, with the benchmark each figure leans on. Illustrative inputs show only the shape of the return — replace with the prospect’s real numbers in discovery.
| Lever | Formula | Illustrative | Source |
|---|---|---|---|
| Working capital release | (Revenue ÷ 365) × DSO days cut | $100M, 10 days → ~$2.74M one-time | Standard working-capital math |
| Labor recovered | hrs/wk × rate × 52 | 30 hrs × $60 × 52 → ~$94K/yr | AFP/APQC |
| EBITDA → enterprise value | annual EBITDA improvement × multiple | $500K × 8× → $4M EV | Houlihan Lokey; CT Acquisitions |
| Close acceleration | current close − target (floor 5 days) | 10 → 4.8 cal. days (25th pct) | APQC |
| AI spend at risk (option value) | AI spend × 60% abandonment risk | $250K × 60% → $150K exposed | Gartner (2025) |
For verification / linking
Sources & citations
Every benchmark behind this estimator, with its origin. Cite the source when you use a number, flag enterprise-scale and practitioner figures as directional, and never attach a benchmark to a named client as its actual result.
Estimate only. Figures are modeled from the inputs and editable assumptions above, grounded in the published benchmarks cited (Gartner, APQC, Nucleus Research, MIT, Billtrust, and standard working-capital math). They are directional and intended to frame a conversation — not a guarantee, forecast, or a specific client’s actual result. Replace assumptions with the client’s real numbers before presenting as a projection. Enterprise value created assumes the annual EBITDA improvement is durable and valued at the entered multiple.