What actually makes a tech company's data valuable

Every tech company believes its data is valuable, but "we have a lot of data" and "we have a valuable data asset" are not the same statement and this is where a great many valuations fall apart.

The market has never made the stakes clearer. OpenAI's five-year licensing deal with News Corp was reported at more than $250 million. Reddit disclosed data licensing contracts worth an aggregate $203 million around its IPO. Amazon's first AI-focused deal with the New York Times was reported at $20 million to $25 million a year. The global market for AI training data, worth roughly $3.6 billion in 2025, is forecast by one estimate to reach $22.6 billion by 2034. Data that was once exhaust is now a line item on the biggest balance sheets in technology.

However, in the same market, a single clean code repository changes hands for around $5,000 and a full company's operational data often starts in the low six figures. Two datasets, both "proprietary," separated by four or five orders of magnitude in value. So the interesting question for any tech company is what puts a dataset at one end of that range rather than the other.

Why size is the wrong thing to brag about

The instinct is to reach for volume: rows, records, users, terabytes. It is the easiest thing to measure, which is exactly why it is a poor proxy for value. A vast dataset of generic, easily reproduced information is worth far less than a smaller one that is very hard to recreate.

Analysis of AI training transactions shows a scarcity premium on domain-specific data running at roughly four to seven times the price of commodity datasets. Commodity text is cheap because the supply is effectively infinite. Scarce, specialised, hard-to-assemble data commands real money because it is the bottleneck, and buyers know it. Scale amplifies value, but only once the data is scarce enough to be worth amplifying in the first place.

What actually moves the number

Look across the disclosed deals and the same value drivers keep surfacing, none of them raw size on its own.

Uniqueness is the big factor. The question every buyer really asks is whether they could get this somewhere else or build it themselves more cheaply. If they can, the price falls toward the cost of rebuilding it. If they can't, you have pricing power.

History matters more than people expect. A snapshot is a commodity; a decade of consistent, time-series data is not, because elapsed time can't be bought at any price. A dataset showing how something changed over years has a scarcity a competitor starting today simply cannot replicate for years.

Context multiplies value. Data that connects behaviour to outcomes or usage to results, is worth more than the same fields sitting in isolation, because it answers questions that siloed data can't. It is why a connected, multi-system company record commands six figures while a loose export does not.

Data tied to demonstrable results, model performance, clinical outcomes, financial benchmarks, is worth more than data whose usefulness is merely claimed. Sageworks was acquired for a reported $800 million largely on the strength of a proprietary benchmarking dataset others couldn't match.

And then there is rights cleanliness, the one tech companies most often forget. It cuts both ways, hard. Documented, training-specific permission raises value; murky provenance pushes a dataset toward worthless, because the legal risk transfers to the buyer. After two years of AI copyright litigation, buyers now price that risk explicitly.

The trap: data you hold but don't own

This is where tech valuations most often come undone. A platform can route enormous volumes of data without owning any of it. If the data belongs to your customers, or your users' consent doesn't cover the use a valuation assumes, the headline number is built on sand. The volume is real; the asset isn't.

A buyer's diligence will work this out in an afternoon. A credible valuation works it out first, by examining consent, contracts and the genuine legal basis for use, and prices the asset on what can actually be exploited rather than what merely passes through the servers. The difference between those two figures has ended more than one deal.

Why it pays to know early

For tech companies, data is rarely a side asset. It is frequently the core of enterprise value, the thing investors are really buying and acquirers are really paying for. That makes the valuation question unusually consequential. Get it right and you can evidence value in a raise, defend it in a sale and borrow against it. Get it wrong, or leave it unexamined and you hand that ground to whoever is on the other side of the table.

The firms that come out ahead are not the ones with the most data. They are the ones that know which of their data is scarce and tied to outcomes, and can prove it to someone paid to be sceptical. In a market where comparable datasets can differ in value by a factor of ten thousand, knowing exactly where yours sits is no longer optional.

Data Valuation Partners produces independent, standards-based valuations of data as an asset.