# Domino Data Health Financial Model Enterprise MLOps platform - the open "system of record" for data science teams, unifying infrastructure, reproducibility, model ops, and governance. - Canonical: https://finamodel.com/startups/domino-data-health - Excel download: https://finamodel.com/startup-models/domino-data-health.xlsx - Category: Enterprise/Security - Model type: SaaS ARR / Valuation - Funding round: Series E - Funding: $43M - Founded: 2020 - Geography: Global (US-headquartered; customers include global banks, pharma, insurers across multiple geographies). - Customer: B2B ## About the company 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. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## 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**: 1. Assumptions & toggles (scenario switch, all key drivers in one place) 2. ARR waterfall (new ARR, expansion ARR, churned ARR → ending ARR by period) 3. Cohort model (each landing year as a cohort; track ACV × retention curve) 4. P&L (revenue → gross profit → operating expenses → EBITDA/operating loss) 5. Headcount plan (AEs, CSMs, R&D, G&A - drives S&M and R&D opex) 6. Cash flow / runway (burn rate, cash position, implied months of runway) 7. Summary dashboard (ARR, NRR, new logos, gross margin, EBITDA margin - annual) ## Frequently asked questions ### Is the Domino Data Health financial model free? Yes. The Domino Data Health model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.