29. How to price your data when selling or licensing it

Determining a fair price for data is one of the most practically complex challenges a business can face. Unlike physical goods, data does not wear out, can be replicated infinitely, and its value depends heavily on who is using it and for what purpose. Yet companies are increasingly finding themselves in situations where they need to put a precise number on a dataset — whether they are entering a licensing agreement, selling a data asset as part of an acquisition, or simply exploring new revenue streams. Getting this right requires a thoughtful approach that goes beyond guesswork.

The starting point is understanding what makes your data uniquely valuable to a potential buyer or licensee. Factors such as exclusivity, accuracy, freshness, and coverage all influence price. A dataset that covers a segment of the market that no competitor can access commands a premium. Historical depth matters too — ten years of transaction data is worth considerably more than two. Once you have assessed these qualities, it is worth exploring comparable transactions in your industry. If similar datasets have been licensed at a certain rate per record or per time period, that benchmark offers a useful reference point and adds credibility to your pricing discussions.

From there, the pricing model itself must be chosen carefully. Flat-fee licensing suits buyers who want certainty; usage-based pricing works better when downstream applications vary widely in scale. Some companies adopt a tiered structure, offering different price points depending on the scope of use, the geography covered, or the level of exclusivity granted. Revenue sharing arrangements, where the data seller takes a percentage of income generated using the data, can align incentives and reward sellers when the data proves especially valuable. Whichever model is selected, it should be backed by a formal valuation rather than intuition alone.

The most credible prices are those that can be defended with evidence. A structured data valuation exercise, drawing on cost, market, and income-based methodologies, gives sellers a clear justification for their asking price and significantly strengthens their negotiating position. It also builds trust with buyers, who are increasingly sophisticated about data quality and wary of overpaying for assets that underdeliver. Companies that invest in understanding and documenting their data's value before entering commercial conversations consistently achieve better terms — and begin longer, more productive data-sharing relationships as a result.