Venture capital has shifted focus. From 2024 to 2026, VCs moved away from prioritizing rapid growth to evaluating companies based on their ability to generate and protect proprietary data. Here’s why this matters for your $1M ARR business:
- Data is the new moat. Investors now value companies with exclusive, hard-to-replicate data that grows in usefulness over time.
- Valuations depend on defensibility. SaaS companies with proprietary data are commanding 8–12x revenue multiples, while those relying on third-party AI models see just 2–3x.
- AI tools alone aren’t enough. Without a strong data strategy, "AI wrappers" are losing investor interest.
Key Takeaway: If you’re a founder, focus on building a data advantage that compounds with user interactions. Investors are looking for businesses that can prove their data is exclusive, improves over time, and creates barriers for competitors.
Steps to Stay Competitive:
- Identify what data your product generates that others can’t easily copy.
- Ensure your product gets smarter with each user interaction.
- Build structural barriers, like regulatory or workflow integrations, to make replication difficult.
- Explore ways to monetize your data beyond your core product.
This shift in VC priorities highlights the need for founders to rethink their strategies. Companies that can clearly articulate their data moat are securing higher valuations and stronger investor interest.
The 4 Dimensions Investors Use to Evaluate Data Moats
The 4 Dimensions of Data Defensibility: How VCs Evaluate Data Moats
As venture capitalists shift their priorities from just chasing growth to identifying robust data strategies, four key dimensions have emerged as the foundation of a strong data moat. These dimensions help investors determine whether your data gives you a lasting edge or just a short-term advantage. Understanding them is crucial to positioning your business effectively – and avoiding being dismissed as just another feature.
Let’s break down these dimensions, starting with the uniqueness of your data.
Uniqueness: Can Competitors Access Your Data?
The first thing investors want to know is whether your data is truly exclusive. If a competitor can easily access or replicate it, you don’t have a moat – you might have a temporary advantage, but nothing long-lasting.
Data becomes unique when it’s generated through your operations, like customer interactions or workflows, and isn’t readily available elsewhere. For example, Incode’s biometric authentication platform collected proprietary training data with every use, improving its model’s accuracy over time. Competitors couldn’t replicate this dataset without matching Incode’s scale and customer base.
A simple test: if a competitor could recreate your data by hiring a team to label publicly available information, your data isn’t unique. As Emerald from Future Cognitive Capital puts it, “If the advantage can be bought back with capital, it is not a moat. It is a head start.”
Compounding: Does Your Data Improve Over Time?
The second dimension looks at whether your data becomes more valuable as it grows. This is often referred to as a “self-reinforcing data loop,” where each interaction improves the product, attracts more users, and generates even better data.
Take Brigit, for example. Every new user didn’t just add revenue – they contributed data that made the product better for everyone. The secret lies in designing systems where every user action – like edits or confirmations – feeds back into improving your models. The focus should be on capturing high-quality, hard-to-replicate data from the start.
Structural Barriers: What Makes Your Data Hard to Copy?
Even if your data is unique and improves over time, investors want to see barriers that prevent competitors from replicating it. These barriers could include regulatory protections, specialized hardware requirements, or deep integration into workflows.
Treefera is a great example. In 2025, they raised $30 million in Series B funding by building a climate AI dataset that combined satellite and drone data from supply chains. The structural barrier wasn’t just the data – it was the specialized drone-captured information that would be incredibly difficult and expensive for a competitor to replicate.
Another example is S&P Global. While stock price data is public, they generate $1 billion annually by licensing the S&P 500 Index. Their edge lies in intellectual property rights and their status as a trusted benchmark – advantages that money alone can’t easily overcome.
Monetizability: Can Your Data Make Money?
Finally, investors assess whether your data can directly generate revenue. This shows that your moat isn’t just supporting your main business but could also open up new income streams.
There are two main ways to monetize data. The first is direct licensing – selling access to your dataset or insights via APIs. The second is through clearinghouse effects, where fragmented data is consolidated into something more valuable that customers will pay for.
Ask yourself: could you package your data and sell it, even to customers who don’t use your main product? If the answer is yes, you’ve created a data asset that stands on its own. While this often becomes clearer as companies scale, planning for it early – by structuring and documenting your data – can unlock future revenue opportunities.
Summary Table
| Dimension | Defensibility Source | Example |
|---|---|---|
| Uniqueness | Operational access / Decision traces | Incode (Biometrics) |
| Compounding | Feedback loops / Usage gravity | Brigit (Financial predictions) |
| Structural | Regulatory / Physical / Energy | Treefera (Drone-captured climate data) |
| Monetizability | Licensing / Clearinghouse effects | S&P Global (Index licensing) |
These four dimensions form the foundation of how investors evaluate the strength of a data moat. By excelling in these areas, your business can demonstrate a lasting competitive advantage that goes beyond surface-level metrics.
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Cyberphysical Data: The Most Defensible Type
What Is Cyberphysical Data?
Cyberphysical data emerges when the physical world meets the digital. Picture IoT sensors tracking industrial machines, biotech devices analyzing medical samples, or drones gathering insights from supply chains. This type of data can’t be scraped or duplicated by simply hiring engineers – it requires a unique combination of hardware and software working together.
The defining feature of cyberphysical data is this tight integration. The software depends on the hardware, and the hardware generates data that competitors can only access by setting up their own physical systems. A great example is Treefera, a London-based company that raised $30 million in Series B funding in October 2025. They aggregated satellite and drone data from the "first mile" of supply chains by deploying drones over physical locations. No amount of web scraping or API usage could replicate that effort.
Why Cyberphysical Data Is Hard to Replicate
What makes cyberphysical data so defensible? Structural barriers that go beyond just spending money. For instance, Anduril, a defense technology company valued at $14 billion with $1 billion in 2024 revenue, relied on custom-built silicon and secure regulatory access. Their edge came from operating in environments with limited GPS signals and leveraging security clearances – advantages that couldn’t be bought off the shelf.
Physical presence adds a layer of friction that protects your data. A competitor would need to do much more than just spend money – they’d have to deploy hardware, navigate regulatory hurdles, build relationships on the ground, and then wait months or even years to collect comparable data. As of 2026, over half of end users (52%) reported that their physical security systems already integrate cloud solutions, but the physical layer remains a bottleneck. You can’t simply download sensor readings from a factory floor or gather biometric data from patient interactions. Being physically embedded in workflows is essential, and this hurdle makes cyberphysical data incredibly hard to duplicate. Even companies without their own hardware must find ways to anchor themselves within these workflows to ensure their data remains protected.
How SaaS Founders Can Build Defensible Data Without Hardware
Even if you’re running a pure SaaS company, you can still create defensible data by owning critical workflow processes. Becoming the go-to system of record for a specific workflow allows you to capture highly detailed information that fragmented tools miss, consolidating it into a single, indispensable source.
Take Zenniz, for example. This European tennis coaching startup saw 3.5x revenue growth by January 2026 by combining hardware and software for deep performance analysis. But you don’t need hardware to achieve similar results. Instead, focus on workflows where decision-making details matter. For instance, tracking why a sales rep offered a discount or why a clinician overrode a recommendation creates a dataset that becomes more valuable with every interaction. This type of data isn’t just proprietary – it’s tied to institutional knowledge that competitors would struggle to replicate.
Whether you’re leveraging hardware or sticking to SaaS, the ability to generate and safeguard unique data is the foundation of achieving premium valuations.
How to Audit Your Data Moat
Many founders hitting $1M ARR struggle to clearly explain the unique data their product generates. These days, VCs are just as interested in your product’s data as they are in your revenue numbers. If you can’t articulate your data’s value, it becomes obvious in investor discussions. Before your next pitch, take a moment to answer these four questions. They’ll help you figure out if you’re building a business that stands out – or just a feature that could easily be copied.
What Data Do You Generate That No Competitor Has?
Start by identifying whether your data is truly exclusive – something competitors can’t easily recreate. If they can achieve similar results using other datasets, you don’t have a moat. You’ve got a head start, but head starts can disappear quickly with enough funding.
The strongest data advantage comes from decision traces – those subtle details like exceptions, overrides, and reasoning that often live in Slack messages or employees’ heads rather than your CRM. Capturing this decision-making process creates a unique knowledge base that grows stronger with every interaction. This isn’t something competitors can scrape from APIs or replicate by hiring engineers. It’s deeply tied to your workflow and how decisions are made in real time.
Ask yourself: can a well-funded competitor replicate my data by pulling public information, using standard APIs, or throwing money at the problem? If the answer is yes, your data strategy is weak, and investors will notice. How are you using AI to build your data moat? Subscribe to our AI Acceleration Newsletter for weekly strategies on creating defensible AI systems.
Next, think about whether your product improves every time someone interacts with it.
Does Your Product Get Smarter With Each Interaction?
A strong data moat relies on how your data evolves over time. It’s not just about collecting data; it’s about structuring it in a way that compounds – growing more valuable as it incorporates user behavior, context, and feedback.
Every feature in your product should not only deliver value to the user but also generate data that strengthens the experience for everyone. Each interaction should feed into a system that gets smarter, creating a self-reinforcing loop of improvement.
A study from MIT in 2025 revealed that 95% of AI pilots fail to deliver measurable financial results. The few that succeed share a common trait: their products improve automatically as users engage with them, without needing extra infrastructure. This is what separates a fleeting feature from a lasting moat.
Now, think about the obstacles a competitor would face to replicate your dataset.
What Would It Take for a Competitor to Replicate Your Dataset?
This question forces you to adopt a competitor’s mindset. What barriers – like time, costs, regulations, or exclusive partnerships – would stop someone from recreating your data? Even the difficulty of moving data between systems (data viscosity) can work in your favor by reducing churn, but don’t rely on that alone. AI tools are making data migration easier, which lowers traditional switching costs.
Your strongest defenses are structural. Between 2023 and 2024, $560 billion was poured into AI infrastructure, yet it only generated $35 billion in revenue – a 16:1 investment-to-revenue ratio. Why? Most AI companies lack structural moats. They rely on third-party models, which drives their margins down. In contrast, companies with proprietary data and models maintain margins as high as 90% because competitors can’t simply buy their way to parity.
Think about whether your data requires lived history – patterns of user behavior captured over years. Competitors can buy volume, but they can’t buy time. This type of long-term learning, often called cognitive compounding, is impossible to shortcut.
Finally, ask yourself if your data has value beyond your core product.
Can Your Data Be Monetized or Become Its Own Product?
The ultimate test of your data’s value is whether it can stand alone. Can it create new revenue streams, like benchmarks, industry reports, or additional services? Data that serves multiple purposes – improving your product while also offering monetization opportunities – commands higher valuations.
Look for catalyst data – information that becomes more powerful when paired with other datasets. This type of data doesn’t just improve your product; it creates network effects that extend beyond your immediate user base. If your data can become the go-to standard for performance, compliance, or outcomes in your industry, you’ve built something that’s exceptionally hard to copy.
Answer these four questions thoroughly. If you’re not happy with your answers, you’re not alone – but now you know where to focus your efforts. Founders who clearly understand and communicate their data strategy often secure much higher valuations – sometimes 2-3x higher – than those who can’t. Take the time to refine your approach before your next investor meeting.
How to Build and Communicate Data Defensibility
Understanding the concept of a data moat is one thing, but building it and explaining its value to investors is an entirely different challenge. Many founders at the $1M ARR stage manage to create defensible data but often struggle to capture or communicate its importance effectively. The gap between a 6x and a 15x valuation often boils down to how well you demonstrate the compounding nature of your data and the intentionality behind your product’s design to generate it. Which AI systems are you leveraging to create these compounding data loops? Subscribe to our AI Acceleration Newsletter for weekly tips on crafting defensible AI products. Let’s break down how to build your data moat and communicate its value to investors.
Designing Products to Generate Defensible Data
Every feature you develop should serve two purposes: deliver immediate value to users and generate data that strengthens your product. This requires embedding data capture into your product’s core workflow. The strongest data moats often come from capturing "decision traces" – those critical exceptions, overrides, and rationales that are typically buried in Slack threads or team members’ heads. By documenting why users make specific choices or how they handle edge cases, you create a layer of defensibility that competitors can’t replicate by simply scraping APIs or hiring more engineers.
Another key is to design for deep system integrations. When your product becomes so embedded in your users’ operations that extracting data or replicating integrations becomes prohibitively expensive, you’ve created a powerful advantage. This isn’t about trapping users – it’s about becoming the essential system for critical decisions. Focus on "catalyst data", which activates or amplifies the value of other datasets. Examples include user intent data that improves search results, compliance annotations that streamline regulatory filings, or quality scores that optimize supply chains. These datasets not only enhance your product but also generate feedback loops that grow in value over time.
The most resilient companies during market downturns shared one important trait: their products improved automatically as users engaged with them, without requiring constant infrastructure investments. By leveraging real-world data – like audio, video, sensor readings, or proprietary evaluations – founders can position their companies upstream, where generic AI models struggle to compete. In industries like healthcare or legal, incorporating expert post-training and domain-specific adjustments can further refine models, especially in high-stakes scenarios where mistakes are costly.
Documenting and Protecting Data Ownership
In today’s investment climate, data provenance has become a critical part of due diligence. It’s not enough to generate valuable data – you need to clearly document its origins, ownership, and protection measures. This level of transparency can significantly enhance your company’s valuation. Take S&P Global as an example: they generate around $1 billion annually by licensing the S&P 500 Index. Their edge lies not in having unique data, but in owning the exclusive intellectual property rights to the index’s methodology and brand.
Securing exclusive contracts with primary data sources can also give you a head start. While these agreements may be temporary, they provide crucial time to build other layers of defensibility. In regulated industries, compliance itself can act as a moat. Adhering to standards like HIPAA, ENERGY STAR, or government security clearances creates switching costs that competitors can’t easily overcome. For instance, the identity verification market is projected to hit $150 billion within two decades, largely driven by the need for robust compliance to combat AI-generated fraud.
"Act like you’re going to be exiting tomorrow because you don’t know when the buyer or investor is coming. They just show up." – Gabriela Smith, LatAmplify Ventures
Operate as if an investor or buyer could knock on your door at any moment. Keep meticulous records of your data assets, including what you generate versus what you license, proprietary algorithms, and regulatory approvals. This not only strengthens your valuation but also signals to investors that your market position is secure.
How to Position Your Data Story to Investors
Once your data is systematically captured and protected, you need to clearly communicate its competitive edge. The investor narrative has shifted. As Steve Schlenker, Managing Partner at DN Capital, explains:
"Ultimately none of us are investing in AI companies. We’re investing in great companies that just use AI to be even better."
Your pitch should center on the problem you solve and how your proprietary data makes your solution uniquely hard to replicate – not just on your AI capabilities.
Demonstrate how user interactions create a self-reinforcing feedback loop that enhances your model and deepens customer retention. Use metrics like retention cohorts and consumption growth to show that customers are embedding your product into workflows that would be costly and disruptive to replace. Explain how your gross margins will improve as your data moat scales. Proprietary models can push margins toward 90%, while relying on third-party inference chips away at profitability.
It’s not about the sheer volume of data but rather its configuration – how it’s sequenced, how frequently it’s updated, and how edge cases are handled. This configuration reflects the lived history of user interactions and isn’t something competitors can replicate with more funding. A 2025 study found that 95% of AI pilots failed to deliver measurable P&L impact. The 5% that succeeded consistently improved through automated data feedback. Show how your customer cohorts contribute to product improvements that benefit all future users. This is the kind of compelling data narrative that attracts premium valuations.
Conclusion: Data Defensibility as Your Competitive Edge
The world of fundraising has shifted dramatically. Venture capitalists are now asking, "What unique data do you generate, and how does it compound?" – often before diving into metrics like MRR. This change is redefining how companies are valued. Businesses raising at 8–12x revenue multiples, compared to those at 2–3x, aren’t necessarily growing faster. Instead, they’ve built strong data defensibility, creating switching costs that even large amounts of capital can’t overcome. Want to stay ahead? Join our AI Acceleration Newsletter for weekly insights on using data defensibility to strengthen your fundraising strategy.
The 4 Dimensions of Data Defensibility
A strong data moat relies on four key dimensions:
- Uniqueness: Is your data something no one else can access?
- Compounding: Does your product improve with every interaction?
- Structural Barriers: Are there significant obstacles that prevent others from replicating your data?
- Monetizability: Can your data generate its own revenue stream?
Take Incode Technologies as an example. They grew from $6 million to $170 million in revenue by excelling in these areas. Their biometric authentication system improves with every verification, creating an accuracy gap that competitors find nearly impossible to bridge.
Next Steps for Founders
Start by auditing your current data position. Ask yourself:
- What data do you generate that no one else can?
- Does your product naturally improve as customers use it?
- How easy – or difficult – would it be for a competitor to replicate your dataset? Would they need months of engineering or years of trust-building and regulatory approvals?
Document your findings in financial terms and make this the centerpiece of your investor conversations. When asked about your competitive edge, lead with how your data compounds and creates switching costs. If you’re leveraging AI systems to build compounding data loops, highlight that as well. For actionable frameworks on creating defensible AI products, sign up for our AI Acceleration Newsletter. By doing a thorough data audit, you’ll not only sharpen your competitive edge but also align with strategies that have proven successful for companies like M Studio.
Learn How M Studio Helps Founders Build Defensible Ventures
At M Studio, we specialize in helping founders with $500K–$3M ARR identify, capture, and communicate their data defensibility. Our proven framework helps you:
- Design product features that generate valuable, exclusive data
- Document ownership and provenance for due diligence
- Craft a compelling data narrative that attracts premium valuations
We’ve partnered with over 500 founders to develop AI-powered systems that automate workflows while creating compounding data advantages. These strategies have helped founders secure valuations 2–3 times higher than their peers. Your first step? A clear, actionable data audit. RSVP for an intro call to learn how we can help you turn everyday product usage into a long-lasting competitive moat. Founders who embrace this shift are pulling ahead – and the gap is growing with each passing quarter.
FAQs
What counts as “proprietary data” for a SaaS business?
Proprietary data in a SaaS business refers to unique information created through the company’s own operations, workflows, or customer interactions. This type of data stands out because it isn’t something that can be easily duplicated or found in public datasets. Examples include insights generated from exclusive systems of record, data collected via IoT sensors, or information tied to highly specialized workflows.
Defensible proprietary data becomes more valuable as the business grows, often benefiting from structural barriers like regulatory access or industry-specific restrictions. Reproducing this data would require significant effort, specialized tools, or exclusive resources, making it a powerful asset for the company.
How do I prove my product’s data loop is actually compounding?
To demonstrate that your product’s data loop grows stronger over time, focus on how every customer interaction adds value to your system. Showcase the specific data signals your product collects and explain how these contribute to measurable outcomes – like sharper accuracy, lower operational costs, or improved efficiency.
Emphasize how your data scales effectively with increased usage and why it’s difficult for competitors to replicate. If your data is exclusive and not easily accessible, it gives your product a significant advantage, making your loop harder to compete with and more powerful as it grows.
What should I show investors to validate data defensibility?
To prove your data’s strength, focus on showing that it’s distinct, scalable, and hard to duplicate. Emphasize how scaling enhances its value, how it’s safeguarded by barriers like regulatory restrictions or exclusive partnerships, and how it’s embedded in your operations. Make sure your data is both auditable and traceable, so investors can confidently verify its insights. If your data is easily copied or widely available, it weakens your competitive edge.
