What is the primary value proposition of Domino Data Lab according to the deck?

Domino positions itself as the 'system of record' for enterprise AI and machine learning. Rather than being just another tool, it acts as a central orchestration layer that integrates with existing data sources (like Snowflake and S3), programming languages (Python, R), and compute frameworks (Spark, Kubernetes). This allows large organizations to manage the 'mess' of disorganized people and infrastructure while ensuring reproducibility and governance.

How does Domino demonstrate its 'land and expand' capability?

The deck uses a specific, anonymized case study on Slide 9 to show a 2016 initial deal of $500k ACV for a 'Data Science Center of Excellence' that grew into a $2.4M firm-wide platform by 2018. This is supported by Slide 13, which provides a bar chart of 12 major customers, showing that in many cases, the 'expansion' revenue significantly outweighs the 'initial land' revenue.

What evidence of market validation does the deck provide?

Domino relies heavily on third-party analyst validation. Slide 8 features a Forrester Wave chart placing Domino in the 'Leaders' category and a quote from Gartner's Magic Quadrant identifying them as a 'Visionary.' Additionally, Slide 12 lists impressive market penetration statistics, such as serving 8 of the largest global banks and 6 of the largest global insurance companies.

What does the deck reveal about the maturity of the AI/ML market in 2020?

Slide 15 argues that the market is maturing rapidly. It shows two key trends: first, that a much higher percentage of sales wins are now coming from pre-budgeted enterprise initiatives rather than ad-hoc departmental spends. Second, the Average Selling Price (ASP) for 'New Lands' is increasing, suggesting that enterprises are committing larger sums of money upfront for data science platforms.

What critical fundraising information is missing from this deck?

As a late-stage (Series E) deck, it is surprisingly light on specific financial figures. While it shows growth trends for ARR and ASP, it uses 'X%' and 'XXX%' placeholders for gross churn and net retention on Slide 14. It also lacks a specific 'Ask' slide detailing how the $43M would be spent, and it does not provide a detailed breakdown of unit economics like Customer Acquisition Cost (CAC).

Domino Data Health Pitch Deck (2020): 19-Slide Breakdown

See all 19 slides of the Domino Data Health pitch deck — a 2020 deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Domino Data Lab’s 2020 pitch deck is a masterclass in enterprise positioning, moving beyond simple tool utility to claim the status of a 'system of record.' At the Series E stage, the narrative shifts from product features to market maturation and financial predictability. The deck highlights a massive $60B enterprise spend on AI/ML and utilizes high-profile third-party validation from Gartner and Forrester to cement its leadership. Key to the successful $43M raise was the demonstration of a robust 'land and expand' model, showing initial deals of $500k ACV growing to $2.4M. While it lacks gr…

Key takeaways

Enterprises are projected to spend $60B on AI/ML annually, excluding salaries, creating a massive greenfield opportunity (Slide 2, 4).
Domino positions itself as the 'open system of record' for enterprise AI/ML, acting as a central gateway for various tools and infrastructures (Slide 5, 7).
The company holds a 'Leader' position in Forrester's Notebook-based Predictive Analytics and ML Solutions wave (Slide 8).
A specific case study shows an initial $500k ACV deal expanding to $2.4M ACV over two years (Slide 9).
Domino has captured 20% of the Fortune 100, including 4 of the top 10 pharma companies and 3 of the top 5 ratings agencies (Slide 12).
The market is maturing, with the percentage of wins originating from budgeted initiatives nearly quadrupling over the tracked period (Slide 15).
Net retention shows a clear upward trend, increasing significantly between year 1 and year 3 after the initial land (Slide 14).
The leadership team features executives with backgrounds from Bridgewater, Microsoft, Salesforce, and Adobe (Slide 16).

The Narrative: From Tool to Infrastructure

Domino Data Lab’s 2020 pitch deck is designed for a late-stage audience. At Series E, investors are less interested in the 'how' of the technology and more interested in the 'where' of the market and the 'how much' of the growth. The deck follows a classic enterprise software narrative: identify a massive, chaotic spend; position the product as the only solution for order; and prove that the world’s largest companies are already paying for it.

Slides 1-5: The Problem and the Opportunity

The deck opens with a bold market claim on Slide 2 : "Enterprises will spend $60B on AI/ML this year, excluding salaries." This immediately establishes the scale of the opportunity. Slide 3 uses a visual metaphor of children playing soccer to describe the current state of enterprise AI as a "mess of disorganized people, tools, and infrastructure."

Slide 4 and Slide 5 introduce the core thesis: this chaos creates a "greenfield system-of-record opportunity," and Domino is the "only open system of record for enterprise AI/ML." By using the term "system of record," Domino is positioning itself alongside foundational enterprise categories like CRM (Salesforce) or ERP (SAP), which implies high stickiness and long-term value.

Slides 6-8: Product and Validation

Slide 7 provides the technical architecture of this "system of record." It shows Domino at the center, orchestrating data from sources like Snowflake and S3, supporting languages like Python and R, and running on infrastructure like AWS and Kubernetes. This slide is crucial for showing that Domino is an "open" platform that doesn't require customers to rip and replace their existing stacks.

Slide 8 shifts to external validation. It features the Forrester Wave for Notebook-based Predictive Analytics, where Domino is circled as a Leader. It also includes a quote from Gartner praising Domino’s support and service scores. For a Series E investor, this third-party proof reduces the perceived risk of the technology and the company's ability to serve large clients.

Slides 9-10: The Enterprise Case Studies

The deck uses two specific case studies to prove its value proposition. Slide 9 details a customer that started with a $500k ACV deal in 2016 and expanded to a $2.4M ACV firm-wide platform by 2018. It attributes this growth to Domino’s flexibility and infrastructure orchestration. Slide 10 focuses on a major health insurance company with a $1.2M ACV , highlighting the need for "workforce orchestration" for over 1,000 researchers and the elimination of "shadow IT."

Slides 11-13: Market Size and Penetration

Slide 11 breaks down the market into three tiers: the current $12B market (2M users), a 5-year projection of $30B (6M users), and a future $65B market driven by new products and pricing. Slide 12 is perhaps the most impressive slide in the deck, stating that Domino has captured 20% of the Fortune 100 . It lists specific penetrations: 4 of the top 10 pharma companies, 8 of the largest global banks, and 3 of the top 5 ratings agencies.

Slide 13 visualizes the "Land vs Expand" model across 12 major customers (C1 through C12). The chart shows that for many of these top-tier clients, the expansion revenue (light blue) is significantly larger than the initial land (dark blue), proving the company's ability to grow within its existing footprint.

Slides 14-15: Retention and Maturation

Slide 14 discusses retention and LTV. While it uses placeholders like "x%" for Gross Churn and "xxx%" for Net Retention, the accompanying bar chart shows that Net Retention increases every year a customer stays with the platform, peaking at Year 3. This suggests that the longer a customer uses Domino, the more valuable they become.

Slide 15 argues that the market is maturing. It shows two charts: one showing that the percentage of wins from budgeted initiatives is growing, and another showing that New Lands ASP (Average Selling Price) is on an upward trajectory. This is a signal to investors that the sales cycle is becoming more professionalized and lucrative.

Slides 16-19: Team and Growth

Slide 16 introduces the leadership team, highlighting experience from Bridgewater, Microsoft, Salesforce, Adobe, and Amazon . This is a "scale-up" team, not a "startup" team. Slide 17 claims that "Q4 was our largest yet," listing a flurry of new enterprise lands and expansions across F500 and G2K companies.

The deck concludes its data presentation on Slide 18 with an ARR growth chart. While the Y-axis lacks specific dollar amounts, the bars show consistent year-over-year growth from FY 2018 through a projected FY 2021E , with the largest jump occurring in the most recent periods.

What Works in This Deck

The "System of Record" Positioning: By claiming this specific category, Domino moves away from being a "nice-to-have" tool and becomes a "must-have" infrastructure component. · Heavy Use of Logos and Verticals: Instead of just saying they have customers, they break them down by industry (Pharma, Banking, Insurance), which proves they have solved the specific regulatory and security needs of those sectors. · Land and Expand Visualization: Slide 13 is a very effective way to show the long-term value of an enterprise customer without needing to show a full P&L. · Market Maturation Argument: Showing that more deals are coming from budgets (Slide 15) is a sophisticated way to prove that the industry has moved past the "experimental" phase.

What Is Missing

Specific Financials: For a Series E round, the use of "X%" and "XXX%" for churn and retention is unusual. Investors at this stage usually require hard numbers in the main deck. · The Ask: There is no slide detailing how much money is being raised or how it will be allocated (e.g., R&D vs. Sales expansion). · Competitor Comparison: While the Forrester Wave shows competitors, the deck doesn't explicitly explain why Domino wins against specific rivals like DataRobot or Databricks in a head-to-head evaluation. · Unit Economics: There is no mention of LTV/CAC ratios or payback periods, which are standard metrics for late-stage SaaS companies.

What Other Founders Should Copy

The Problem Visual: Using a simple, relatable image (the soccer kids) to explain a complex technical problem (disorganized ML infrastructure) is highly effective. · The "Gateway" Diagram: Slide 7 is a perfect example of how to show your product as a central hub that adds value to an existing ecosystem rather than competing with it. · The Maturation Metric: Tracking whether your sales come from "budgeted initiatives" is a great way to prove market timing and product-market fit to late-stage investors. · Third-Party Validation: If you are a leader in a Gartner or Forrester report, make it a centerpiece of your deck. It carries more weight than any internal metric.

Frequently asked questions

What is the primary value proposition of Domino Data Lab according to the deck?: Domino positions itself as the 'system of record' for enterprise AI and machine learning. Rather than being just another tool, it acts as a central orchestration layer that integrates with existing data sources (like Snowflake and S3), programming languages (Python, R), and compute frameworks (Spark, Kubernetes). This allows large organizations to manage the 'mess' of disorganized people and infrastructure while ensuring reproducibility and governance.
How does Domino demonstrate its 'land and expand' capability?: The deck uses a specific, anonymized case study on Slide 9 to show a 2016 initial deal of $500k ACV for a 'Data Science Center of Excellence' that grew into a $2.4M firm-wide platform by 2018. This is supported by Slide 13, which provides a bar chart of 12 major customers, showing that in many cases, the 'expansion' revenue significantly outweighs the 'initial land' revenue.
What evidence of market validation does the deck provide?: Domino relies heavily on third-party analyst validation. Slide 8 features a Forrester Wave chart placing Domino in the 'Leaders' category and a quote from Gartner's Magic Quadrant identifying them as a 'Visionary.' Additionally, Slide 12 lists impressive market penetration statistics, such as serving 8 of the largest global banks and 6 of the largest global insurance companies.
What does the deck reveal about the maturity of the AI/ML market in 2020?: Slide 15 argues that the market is maturing rapidly. It shows two key trends: first, that a much higher percentage of sales wins are now coming from pre-budgeted enterprise initiatives rather than ad-hoc departmental spends. Second, the Average Selling Price (ASP) for 'New Lands' is increasing, suggesting that enterprises are committing larger sums of money upfront for data science platforms.
What critical fundraising information is missing from this deck?: As a late-stage (Series E) deck, it is surprisingly light on specific financial figures. While it shows growth trends for ARR and ASP, it uses 'X%' and 'XXX%' placeholders for gross churn and net retention on Slide 14. It also lacks a specific 'Ask' slide detailing how the $43M would be spent, and it does not provide a detailed breakdown of unit economics like Customer Acquisition Cost (CAC).

Cover slide of the Domino Data Health pitch deck — Series E 2020 — Domino Data Health pitch deck, slide 1 (2020)

Domino Data Health pitch deck: the facts

Company: Domino Data Health
Year: 2020
Stage: Series E
Slides: 19
Sector: Software

Domino Data Health pitch deck PDF

The full Domino Data Health deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Domino Data Lab (deck labeled "Domino Data Health") pitch deck was used for

This deck is a **2020 Series E fundraising presentation** for Domino Data Lab (branded in the library as Domino Data Health), an enterprise data science platform provider. It was used to raise a **$43M Series E round** to expand its position as the system of record for AI/ML and to launch products like Domino Model Monitor. The deck targets enterprise buyers, emphasizing Domino’s role as the central platform for large data science teams and its ability to orchestrate infrastructure and monitor models in production. It showcases market validation from analyst firms like Gartner and Forrester and highlights a land-and-expand sales motion into large enterprises (e.g., growth from $500k ACV to multi‑million ACV).

Business model: Enterprise **data science and MLOps platform** providing a centralized environment for data scientists to build, deploy, monitor, and manage machine learning models at scale.

Round: Series E
Year: 2020
Raised: $43M Series E equity financing announced June 10, 2020.
Lead investor: Highland Capital Partners
Investors: Highland Capital Partners (lead), Highland Europe, Dell Technologies Capital, Sequoia Capital, Coatue Management
Headquarters: San Francisco, California, United States.
Industry: Software – enterprise data science / MLOps platform.

Total funding: $128M total funding as of the June 2020 Series E announcement.

Use of funds as presented: Support the expansion of Domino’s enterprise data science platform and the launch and scaling of Domino Model Monitor, helping enterprises monitor production ML models and manage data science at scale.

What the Domino Data Lab (deck labeled "Domino Data Health") deck got right

Strong third‑party validation from Gartner and Forrester. Slide 7 positions Domino as a **leading data science platform**, referencing inclusion in **Gartner** and **Forrester** evaluations and highlighting high reference customer scores for support and service. This third‑party validation reinforces credibility and reduces perceived risk for enterprise buyers and investors.
Compelling system‑of‑record positioning and open architecture. The deck emphasizes Domino as the **center of the enterprise ML ecosystem**, with an open architecture that consolidates data science tools and workloads into a single platform. This system‑of‑record positioning is strategically compelling and aligns with enterprise needs for governance, consistency, and efficiency in AI/ML.
Demonstrated enterprise land‑and‑expand motion. Slide 8 presents a clear **land‑and‑expand narrative**, showing a customer journey from a 2016 initial deal for a Data Science Center of Excellence to 2017 organic expansion across business units and a 2018 firm‑wide platform deployment on Domino, with ACV growing from **$500k to multi‑million levels**. This illustrates strong expansion potential and platform stickiness.
Clear articulation of differentiated product capabilities. The customer story on slide 8 attributes the win to **flexibility for diverse analyses**, **unique reproducibility and collaboration**, and **powerful infrastructure orchestration** that accelerates research and boosts data scientist productivity. These capabilities map directly to known pain points in enterprise data science, making the value proposition concrete.
Strong macro narrative around a maturing data science platform market. Slide 14 frames a **rapidly maturing market**, with increasing ASPs and a larger share of wins originating from budgeted initiatives as IT leadership drives purchasing. This communicates that Domino is selling into a more structured, top‑down budget environment, which can support larger, more durable contracts.
Funding tied to concrete product expansion (Domino Model Monitor). The Series E round is contemporaneous with the launch of **Domino Model Monitor**, giving the deck a tangible innovation story that extends Domino from experimentation to production monitoring. This expands the addressable problem space and reinforces Domino’s role across the entire ML lifecycle.

What could have been stronger

No explicit “Ask” / use‑of‑funds slide. The deck, as reconstructed in secondary analyses, reportedly **lacks a dedicated “Ask” slide** explicitly stating the amount being raised, valuation expectations, and a detailed use‑of‑proceeds breakdown (e.g., split across R&D, go‑to‑market, international expansion). This omission makes it harder for investors to quickly see precisely how the $43M will be deployed and which milestones it is intended to fund.
Limited visibility into unit economics. Analyses of the deck note that it does **not present detailed unit economics**, such as customer acquisition cost (CAC), payback periods, or cohort‑level profitability. Given the enterprise focus and land‑and‑expand motion, investors might want more granular economics to understand scalability and efficiency.
Heavy reliance on a single expansion case study. While slide 8 showcases a dramatic ACV expansion story (from **$500k to $2.4M+ ACV**), it focuses on a single flagship customer. Without a portfolio‑level view (e.g., percentage of customers expanding, median expansion rate, churn), investors may question how representative this case is of the broader customer base.
Market narrative more directional than quantitative. Slide 14 indicates that the **market is maturing** and ASPs are increasing as more buyers budget for data science platforms, but the OCR text shows only partial charts and limited quantitative detail. The deck appears to lean on directional trends rather than concrete market‑sizing numbers (TAM/SAM/SOM) and quantified ASP progression, leaving some macro context underdeveloped.

How an investor would read this deck

What draws attention

Evidence of **strong analyst validation**, including inclusion in Gartner and Forrester evaluations for data science and ML platforms, which supports the claim of being a leading enterprise platform.
Clear **enterprise land‑and‑expand motion**, with one highlighted customer growing from a **$500k ACV initial deal to around $2.4M ACV** (labeled as $2.4M or $24M in different OCR/analyst interpretations), demonstrating the potential for significant account expansion.
Positioning as the **system of record for AI/ML** with an open architecture that consolidates diverse tools and workloads, matching enterprise trends toward centralized MLOps and governance.
Macro thesis that the **data science platform market is maturing**, with increasing ASPs and a higher share of deals coming from budgeted initiatives, implying expanding budgets and stronger top‑down buy‑in.
Use of funds in conjunction with the launch of **Domino Model Monitor**, indicating a strategy that integrates experimentation, deployment, and monitoring into a single enterprise platform.

Risks that stand out

Heavy emphasis on a **platform** sale into IT and data leadership may lengthen sales cycles and concentrate revenue in a smaller number of large enterprise accounts, increasing exposure to a few key customers.
The market for **data science/ML platforms is competitive**, with other vendors recognized by Gartner/Forrester and cloud hyperscalers offering overlapping MLOps capabilities, which may pressure pricing and differentiation.
The deck’s limited disclosure of detailed **financial and unit‑economic metrics** (e.g., CAC, net retention, profitability) in public analyses makes it harder to assess scalability and capital efficiency.
Reliance on being the “system of record” for AI/ML assumes that enterprises will converge on a single platform rather than a multi‑tool stack, an assumption that may not hold in all segments.

Questions this deck invites

How many enterprise customers exhibit **similar ACV expansion** to the case on slide 8 (from $500k to multi‑million ACV), and what is the distribution of expansion across the base?
What are Domino’s **unit economics** (CAC, payback period, gross margin, net revenue retention) for its enterprise customers, and how do these vary by segment or region?
How sticky is the platform in practice (e.g., **logo churn**, seat churn, workload churn), and what switching patterns have been observed among large customers using multiple ML tools?
How does Domino’s positioning in **Gartner and Forrester** reports translate into win rates versus specific competitors, and what are the most common competitive displacements or losses?
What portion of pipeline now originates from **budgeted platform initiatives** versus bottom‑up team adoption, and how is that trend evolving over time as the market matures?

What founders can take from the Domino Data Lab (deck labeled "Domino Data Health") deck

Showing **third‑party analyst validation** (e.g., Gartner and Forrester) can credibly support claims of market leadership in enterprise software and reduce perceived vendor risk.
A well‑constructed **land‑and‑expand case study**, like the customer growing from $500k to multi‑million ACV, can vividly demonstrate expansion potential and the value of becoming a core platform.
Framing the product as the **system of record** for a critical function (AI/ML) helps communicate strategic importance and justify higher ASPs in enterprise sales.
Linking a funding round to a concrete **product launch** (e.g., Domino Model Monitor) can show investors that new capital is tied to specific innovation and market opportunities.
Articulating that the **market is maturing**, with increasing ASPs and more budgeted deals, helps explain why timing is favorable for scaling a platform business.
At later stages (Series E), investors may still expect a clear **Ask and use‑of‑funds slide**; the criticism of this deck’s omission underscores how important it is to state explicitly how much is being raised and why.

Domino Data Lab (deck labeled "Domino Data Health") pitch deck: common questions

What does Domino Data Lab / Domino Data Health do?

Domino Data Lab (sometimes referred to as Domino Data Health in pitch‑deck libraries) is an **enterprise data science and machine learning operations (MLOps) platform** that helps large organizations manage the end‑to‑end lifecycle of models, from experimentation to deployment and monitoring. Its platform centralizes tools, infrastructure, and governance for data science teams and is positioned as a system of record for AI/ML in enterprises.

How much did Domino Data Lab raise in its Series E round and when?

In **June 2020**, Domino Data Lab announced a **$43M Series E funding round**. This round brought the company’s total funding to approximately **$128M**.

Who invested in Domino Data Lab’s Series E round?

The **$43M Series E** was **led by Highland Capital Partners**. The round also included **Highland Europe**, **Dell Technologies Capital**, and existing investors **Sequoia Capital** and **Coatue Management**. Some secondary data providers additionally list Dell Technologies Capital and Highland Capital Partners as key or lead investors in the round.

What was the purpose of Domino’s $43M Series E raise?

According to the company’s June 10, 2020 press release, the **$43M Series E** funding was used to support the launch of **Domino Model Monitor (DMM)** and to expand Domino’s enterprise data science platform. The announcement notes that Domino’s platform is trusted by about **20% of the Fortune 100**, highlighting a focus on large enterprises and suggesting continued scaling of go‑to‑market and product capabilities.

What are key messages in the Domino Series E pitch deck?

The **2020 Series E deck** emphasizes that Domino is a **leading data science platform**, citing recognition by **Forrester** and **Gartner** in categories like data science and machine learning platforms. It also highlights an enterprise land‑and‑expand motion, including a customer whose **initial $500k ACV deal grew to $2.4M+ ACV** over several years, illustrating scale‑up within a large financial or enterprise client.

Sources

Funding and outcome facts on this page were researched on 2026-08-21 from the pages below.

Domino Data Health pitch deck slides

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What each slide of the Domino Data Health pitch deck says

Slide 2

SUE HR ih ea ee _ 7 = 3 == ig ZZ o — Enterprises will spend $60B on Al/ML this year, excluding salaries PR pe = — NCO ay

Slide 3

SR Pe a FA of Co 21d . But it's a mess of disorganized people, tools, and infrastructure

Slide 4

That creates the only large, greenfield system-of-record opportunity in enterprise software

Slide 5

Domino is the only open system of record for enterprise Al/ML

Slide 6

Domino is “The center of the enterprise ML ecosystem” The gateway to ML infrastructure and the system of record for work Your favorite tools and IDEs Use data fi wher Sand e dal en e " \ = Q studio rd | 7 Integrated with your workflow Xx wilake an » = TN rr / Bs om gsa. Bi Che bat Or Rite Ot i +obleay a ouhon GR sas [FRSA sii oa fon @databricks 4A MATLAB julia b ol OPyTorch H,0 Use any programming language vi OQ ANACONDA WF TensorFlow = NM Sa opts Teno With any algorithm package Running on any infrastructure @nvioia WS @) kubernetes (a) B® Microsoft Azure

Slide 7

The leading data science platform FORRESTER Gartner Notebook-based Predictive Analytics and ML Solutions Magic Quadrant Visionary on Data Science and ML Platforms Strang Chatkengens Canmenders Pertormen Laaders. v = (44 : M Domino successfully enables enterprises int ind al-strength deployments. Reference customer scores for support and service was among the highest of all vendors evaluated. As the center of the enterprise ML ecosystem, Domino's open architecture helps consolidate all data scienc S and workloads within a single platform an . pawe e [Rsp—

Slide 8

With Domino, [COMPANY] dramatically increased rate of seed testing 2016: Initial deal for Data Science Center of Excellence 2017: Organic expansion across multiple separate business units 2018: Firm-wide platform on Domino Why Domino: « Flexibility to support diverse types of analysis « Unique reproducibility & collaboration capabilities « Most powerful infrastructure orchestration, accelerates research and data scientists' productivity Initial Deal: 500k ACV Today: 24M ACV

Slide 14

Market is maturing rapidly ASP increasing as companies budget for data science platforms and IT leadership drives purchasing % of wins that originated from budgeted initiatives New Lands ASP 50% 40% 0% 20% - IO

Slide text above is read directly from the Domino Data Health deck PDF embedded on this page.