Our CEO’s Perspective: Why Proprietary Data Alone Doesn’t Justify a Higher Valuation
By Mike Ryan, CEO of BPN AI
I recently had a great conversation with a founder Boardy introduced me to. His customer activity and revenues are growing nicely, and he's collecting proprietary data at every turn.
That's a great starting point. But is it enough to justify a higher valuation?
We talked about what actually makes proprietary data valuable to private equity, venture capital investors, and strategic buyers. And it comes down to something many founders overlook: it's not just about how much data you collect, but what you can do with it.
Proprietary Data Is Valuable. Actionable Intelligence Is Worth More.
Strong data collection matters. And as AI-enabled tools make publicly available information easier to access and analyse, genuinely proprietary data can become an even more important competitive advantage.
But a large dataset doesn't automatically create a valuable business.
The real question is whether your analytics can uncover something your customers or competitors couldn't see before.
Can they identify new opportunities, improve decisions, reduce costs, or help customers understand their businesses better?
That's the difference between collecting data and building an intelligence layer. And it's where the valuation conversation starts to change.
How Proprietary Data Impacts Company Valuation
When investors assess a data-rich company, there are two very different valuation conversations.
The first is a business with a lot of data, but limited differentiated insight.
In this case, private equity and growth equity investors will typically focus on the fundamentals: revenue growth, EBITDA, customer retention, competitive positioning, and the size of the total addressable market (TAM).
The data may be useful, but it doesn't necessarily justify a premium valuation multiple.
The second is a business that turns proprietary data into a competitive advantage.
If the company can demonstrate that its analytics produce unique, reliable, and commercially valuable insights, that's a different story.
More customers can generate better data. Better data can improve the analytics. And better analytics can strengthen the product, increase customer value, and make the business harder to replicate.
That's the potential for compounding intelligence.
But there's an important distinction: more data doesn't automatically mean more value. The analytics need to deliver something customers will actually use and pay for.
What Should PE and VC Investors Look for During Due Diligence?
For investment teams evaluating AI-driven or data-rich businesses, the question isn't simply whether the company owns a proprietary dataset. It's whether that dataset creates a measurable and defensible advantage.
I'd focus on four things:
Uniqueness: Is the data genuinely proprietary, or could competitors collect something similar?
Quality: Is the data structured, reliable, and sufficiently consistent to support meaningful analysis?
Analytical value: Does the company uncover insights that wouldn't otherwise be available?
Commercial impact: Can those insights be linked to revenue growth, customer retention, cost savings, or better decisions?
That last point matters most. A sophisticated analytics platform may sound impressive, but investors ultimately need proof that it creates economic value. For a broader checklist, see our guide to AI due diligence for PE and VC.
The Same Principle Applies to AI for Investment Analysis
Interestingly, this is exactly the challenge we see with AI in private equity and venture capital.
Investment teams already have access to enormous amounts of information: financial statements, market research, operating data, investment models, and company presentations.
And with tools like ChatGPT and Claude, analysing that information has become much faster.
But faster analysis isn't necessarily better analysis.
If the numbers are wrong, assumptions drift between documents, or conclusions can't be traced back to reliable sources, the output isn't something an investment team can confidently take to its investment committee.
That's why we built BPN.
BPN is an AI-native investment analysis platform for private equity, venture capital, and growth equity teams, designed to turn research, financial models, and investment assumptions into structured, evidence-backed analysis with Evidence Mapper, Case Builder and Memo Writer.
The goal isn't simply to generate more content. It's to help investment teams produce work they can trust, challenge, and refine, without spending hours manually reconciling every output.
The principle is the same whether you're evaluating a company's proprietary dataset or using AI to analyse an investment opportunity: the value lies in the quality of the intelligence, not the volume of information.
Build the Proof Before Due Diligence Begins
One thing I emphasised in that founder conversation was the importance of demonstrating the value of the analytics layer well before a financing round or sale process begins.
A few slides describing your proprietary data won't be enough.
Investors want to see the intelligence layer working. That means real products, dashboards, models, and workflows that translate data into better decisions.
Even better, show the outcomes.
How much time does your product save? What does it help customers do better? Can you demonstrate stronger retention, higher revenues, or measurable cost reductions?
The more clearly you can connect your analytics to economic value, the stronger your valuation argument becomes.
The Bottom Line
Proprietary data can absolutely drive a higher company valuation. But owning the data is only the beginning.
What matters is whether you can turn it into something genuinely useful, differentiated, and difficult to replicate.
For founders, that means building and proving the analytics layer long before investors start asking questions.
For private equity and venture capital teams, it means looking beyond the size of a dataset and understanding whether the intelligence behind it creates a real competitive advantage.
At BPN, we apply the same thinking to AI-powered investment analysis. Information is everywhere. What matters is turning it into analysis you can trust and decisions you can defend.
So if you're building a data-rich company, here's the question I'd ask:
What can your analytics help a customer do that the raw data alone never could?
Proprietary Data and Valuation: FAQ
Does proprietary data increase a company's valuation?
It can, but only when the company shows that the data produces unique insights customers use and pay for. Without that proof, investors value the business on its fundamentals: growth, EBITDA, retention and market size.
How do investors assess proprietary data in due diligence?
They test four things: how unique the data is, its quality and consistency, the analytical value it creates, and its measurable commercial impact on revenue, retention or costs.
Related reading: Why Investment Teams Need an AI Trust Layer and Investment Memo Example for PE and VC.
