DDDomino Data Health Financial Model
Enterprise/Security Startup Financials (Free Excel Download)
Enterprise MLOps platform - the open "system of record" for data science teams, unifying infrastructure, reproducibility, model ops, and governance.
professionals from Deloitte
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About this model
Domino Data Health is an enterprise MLOps platform that acts as a system of record for data-science work. It brings infrastructure, reproducibility, model operations, and governance together so organisations can develop and deploy models with more control.
The business targets large data-science organisations that need to make machine-learning workflows repeatable across teams and environments. Its enterprise motion creates a land-and-expand opportunity as an initial platform deployment is adopted by additional users, projects, and business units.
The model uses an ACV-led ARR build, with new enterprise logos, deployment timing, expansion, and retention as the core revenue drivers. It also reflects the material infrastructure and support costs of running an MLOps platform, alongside sales capacity, R&D, and cash runway.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Domino Data Health
dominodx.com
How to build a detailed financial model for Domino Data Health
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Domino Data Health model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Platform positioning: "Center of the enterprise ML ecosystem" - acts as the integration layer and system of record across the full data science workflow.
- Core capabilities (from platform diagram, sl.6): Infrastructure orchestration; reproducibility & collaboration; Model Ops (host, publish, monitor); governance & management.
- Open architecture: integrates with any data source, IDE, programming language, algorithm package, compute framework, and cloud/on-prem infrastructure.
- Problem solved: AI/ML spend is fragmented (~$60B/yr on AI/ML excluding salaries) with disorganized people, tools, and infrastructure. No dominant system of record existed - Domino claims this greenfield.
Market
- Today's addressable market: ~2M data scientists globally → $12B market.
- 5-year user growth scenario: ~6M potential users → $32B market.
- Extended TAM (new products / use cases): $65B market.
- Growth rate: Data science jobs growing at 36% y/y.
- Enterprise AI/ML total spend: $60B/yr (excluding salaries).
- No SAM/SOM breakdown provided beyond the above concentric circles. No methodology cited for the $12B/$32B/$65B figures.
Revenue model
- Model: Annual Contract Value (ACV) subscription per enterprise customer.
- Pricing unit: Per-customer enterprise license; pricing scales with number of data scientists / business units. Exact per-seat or per-compute pricing not disclosed.
- Land small, expand wide: Initial deals as low as ~$500K ACV, growing to $2.4M ACV for the same customer over 2–3 years. Health insurance case study at $1.2M ACV.
- Expansion mechanism: Organic spread across business units (BU-by-BU adoption), then firm-wide platform contracts.
- No consumption / usage-based component described; no professional services revenue line mentioned.
Traction & metrics
- Customer penetration:
- 20% of the Fortune 100
- 8 of the largest global banks and financial services organizations
- 4 of top 10 pharma companies
- 6 of the largest global insurance companies
- 3 of top 5 ratings agencies
- 2 of top 5 Healthcare companies
- 5 F500 US manufacturers
- ARR trajectory: Bar chart shows strong growth FY2018 → FY2019 → FY2020 → FY2021E. Shape shows roughly 2–2.5× step-up per year but no axis labels - exact ARR figures are redacted in this version of the deck.
- Land & expand examples: One customer: $500K ACV initial land (2016) → $2.4M ACV today (4.8× expansion over ~4 years).
- Q4 largest quarter: "$XM Enterprise Lands" and "$YM Enterprise Expansion" - dollar amounts redacted.
- Budgeted initiative wins: Growing from ~12% → ~30% → ~48% of wins originating from budgeted initiatives across three recent periods (x-axis labels not shown).
- New Lands ASP: Upward trend across four periods (axis labels redacted).
Unit economics
- Gross churn FY20: Redacted ("x%").
- Net retention FY20: Redacted ("xxx%"); slide title explicitly states "High net retention and LTV driven by customer satisfaction" - implied >100%.
- NPS: Redacted ("+XX"); Gartner cited: "reference customer score for support and service was among the highest of all vendors evaluated."
- Cohort expansion: Bar chart shows net retention rising from Year 1 → Year 2 → Year 3 post-land - shape confirms strong negative gross churn / high expansion dynamic.
Competition / moat
- Positioning: Gartner Magic Quadrant Visionary (2020) and Forrester Strong Performer (Notebook-based Predictive Analytics & ML Solutions).
- Named competitors visible in Forrester chart: Oracle, Anaconda, Cloudera, Databricks, Civis Analytics, OpenText, H2O.ai, Google.
- Claimed moats:
- Open architecture (not locked to any cloud/tool/language) - vs. closed ecosystems of Databricks / Google
- Reproducibility & governance - mission-critical for regulated industries (pharma, finance, insurance)
- Customer satisfaction / support quality (Gartner reference score cited as top-tier)
- Deeply embedded in enterprise workflows - 1,000+ researchers on a single instance (health insurance case study)
- No patent or proprietary data moat described.
Team & funding ask / use of funds
- Leadership team (all hired/in-seat as of FY2020):
- Nick Elprin - CEO & Co-Founder (Bridgewater)
- Chris Yang - CTO & Co-Founder (Bridgewater)
- Natalie McCullough - President & COO (Microsoft, ServiceSource, McKinsey)
- Joseph Rozenfeld - SVP Engineering (Amazon, Captora, Tibco)
- Jason McClelland - CMO (Heroku/Salesforce, Adobe)
- Dennis Sevilla - CFO (Sunrun, DocuSign, Salesforce)
- Billy Waldman - VP Product (Samsara, Cisco/Meraki)
- Dave Cole - Chief Customer Officer (Acxiom, MicroStrategy)
Recommended financial model
- Archetype + why: Enterprise SaaS ARR model with cohort-level land-and-expand mechanics. Domino is a pure subscription business with a pronounced land-small / expand-large motion, redacted-but-confirmable NPS >100% net retention shape, and a multi-year cohort expansion curve visible in the deck. A standard ARR waterfall (new ARR, expansion ARR, churned ARR, net new ARR → ending ARR) with ACV cohorts by landing year is the right engine.
- Forecast horizon & granularity: FY2021E–FY2025 (5-year), annual. Quarterly optionality for FY2021–FY2022 if management cadence data surfaces.
- Key drivers & assumptions:
- New logos landed per year - Q4 FY20 was "largest quarter ever"; ~30–50 new enterprise logos/yr at model start, growing 20–30% y/y
- Average initial ACV (land ASP) - examples: $500K–$1.2M; base case ~$600K average new land ACV, growing ~10% y/y per maturity trend on slide 14
- Expansion rate by cohort year - cohort chart shows net retention rising materially from Yr1→Yr3; Yr1 net retention ~110%, Yr2 ~125%, Yr3+ ~130–140% until customer maxes penetration
- Gross churn rate - labeled "x%" (redacted); ~5–8% gross churn given enterprise stickiness and NPS quality signal
- Net revenue retention (NRR) - labeled "xxx%", implied >100%; ~120–130% blended NRR for model calibration
- New logo sales cycle / capacity - ~6–9 month avg sales cycle; 1 AE closes ~4–6 logos/yr; headcount growth drives new-logo capacity
- S&M as % of revenue - ~40–50% at current scale, declining to ~30% at maturity; typical enterprise SaaS benchmark
- R&D as % of revenue - ~25–30%; platform company with ongoing ML/infra investment
- G&A as % of revenue - ~15% near-term, declining to ~10%
- Gross margin - ~70–75%; SaaS platform with some hosting/infrastructure COGS; no margin data in deck
- Market penetration ceiling - 20% of F100 already; ~500–1,000 addressable enterprise accounts globally
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: NRR ~125%, new logos +20% y/y, land ASP growing ~10% y/y, gross churn ~7%
- Bull: NRR ~135–140%, new logos +35% y/y (larger sales force post-raise), ASP uplift from enterprise bundling; gross margin expands to 78%+
- Bear: COVID-related budget freeze slows new lands (deck already flags COVID), NRR compresses to ~110%, churn ticks to 10%, new logo growth flat
- Required sheets / outputs:
- Assumptions & toggles (scenario switch, all key drivers in one place)
- ARR waterfall (new ARR, expansion ARR, churned ARR → ending ARR by period)
- Cohort model (each landing year as a cohort; track ACV × retention curve)
- P&L (revenue → gross profit → operating expenses → EBITDA/operating loss)
- Headcount plan (AEs, CSMs, R&D, G&A - drives S&M and R&D opex)
- Cash flow / runway (burn rate, cash position, implied months of runway)
- Summary dashboard (ARR, NRR, new logos, gross margin, EBITDA margin - annual)
Frequently asked
Is the Domino Data Health financial model free?+
Yes. The Domino Data Health model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Domino Data Health's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
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