Data monetization means earning a financial return from data. The return is indirect when data improves your own operations or products, and direct when an outside party pays for data, insights, access or a license. Licensing records to AI companies is the newest direct route. Most companies begin with an internal use, then test whether outsiders would pay.
- Monetization is broader than selling data: improving operations and improving products count too.
- Direct routes bring in revenue from outside the company; indirect routes show up as savings or better products.
- Platforms range from marketplaces and data exchanges to data-as-a-service APIs and AI licensing programs.
- Licensing a prepared copy of records to AI developers needs no data product or subscriber base.
What data monetization means
Data monetization is turning data into a financial return. MIT CISR researchers describe three ways to turn data into corporate profits: improving, wrapping and selling (MIT Sloan). The return does not have to be a sale. According to the same research, it can come as cost savings, more sales, more donations or a budget increase.
For a definition with examples tied to business-to-business companies, and the research figures on which route earns most, read B2B data monetization.
Direct and indirect data monetization
The simplest test is who pays. If no new party pays and the gain shows up as lower costs or better products, it is indirect. If an outside party pays for data, insights or access, it is direct.
| Indirect | Direct | |
|---|---|---|
| Who pays | No new payer; the business gains | A customer, partner, marketplace buyer or licensee |
| MIT CISR terms | Improving: using data assets so operations run more smoothly, faster and at lower cost (MIT Sloan) | Selling: not only data, but also insights that solve problems |
| Wrapping | Usually counted here: data makes existing products more appealing | Some definitions count it as direct; see direct vs indirect examples |
| Typical effort | Internal analysis and process change | Packaging, rights review, contracts and, for products, ongoing delivery |
Five data monetization strategies
Most companies use more than one. The first two are indirect; the last three bring in outside revenue.
- Improve operations. Use records to cut waste, repeat work or delays. It needs no buyer and carries the least risk.
- Wrap data into what you already sell. Add estimates, benchmarks or recommendations to an existing product. MIT CISR calls this wrapping: building data into an offering so customers value it more (MIT Sloan).
- Sell data products or data as a service. Package insights, feeds or APIs and charge for access. IBM notes that data-as-a-service lets organizations create a scalable revenue stream from their data assets (IBM).
- List data on a marketplace or exchange. Reach buyers who search for datasets, at a price you set. Works best for data that is already clean and standard.
- License records to AI developers. Give a developer defined rights to a cleaned copy of records the business already holds, as a one-off or with periodic refreshes. See AI licensing.
Choosing a data monetization strategy
Pick the strategy from the data you have, not from the market you wish you had. Three questions narrow it quickly: Is the data distinctive, or could a buyer get it elsewhere? Can you license it, given your contracts and privacy duties? Do you want ongoing revenue that needs a product team, or a one-off or periodic license that does not?
For a step-by-step plan for a business-to-business company, read the data monetization strategy section of the B2B guide. For public cases of companies earning from data, see data monetization examples.
Kinds of data monetization platform
"Data monetization platform" covers several different things. A data marketplace, in IBM's definition, is an online platform where data providers and consumers can list, purchase and trade data, and providers set their own prices (IBM). IBM also notes internal marketplaces or data exchanges. Platform names change fast, so this guide describes the kinds rather than listing vendors.
| Kind of platform | What it does | Suits |
|---|---|---|
| Public data marketplace | Lists datasets that buyers search, compare and buy at the provider's price | Clean, standard datasets with many potential buyers |
| Data exchange or clean room | Lets partners share or match data under agreed rules without handing over raw records | Partner deals and data sharing for better terms |
| Data-as-a-service API or feed | Delivers data or insights on subscription | Companies willing to run a data product |
| Analytics and insight services | Turns your data into reports sold to customers | Firms with a clear customer problem to solve |
| AI data licensing programs and data partners | Match company records with AI developers and handle preparation and terms | Companies with years of distinctive records and no data product |
Licensing data to AI companies: the newest route
AI developers need data that was never on the public web, and they now pay for it. Reported licenses range widely: Quartz reports that rates vary by orders of magnitude depending on the content, the publisher and the buyer (Quartz). One social platform disclosed data licensing contracts with an aggregate value of $203 million and terms of two to three years.
What sets this route apart is that no data product is needed. You license a defined copy for a defined use, and you keep the records. Read AI licensing for how these deals are structured, and the deals tracker for named, sourced deals.
How to start monetizing data
- List your records: categories, systems, years of history and who owns each. The data inventory template has a column for each.
- Pick one internal use and measure the gain. It shows which records carry value.
- Check rights before any outside route: customer contracts, employee data, third-party content and privacy law.
- Estimate value with the three data valuation methods before you talk to a buyer.
- Get an outside view before sharing any files: check fit with a short note on what you hold.
Common questions
What does data monetization mean?
Turning data into a financial return. MIT CISR describes three ways: improving operations, wrapping data into products and selling data or insights (MIT Sloan). The return can be savings or new revenue.
Which data monetization strategy should a company try first?
Usually an internal one: pick one process your records could improve and measure the gain. It needs no buyer and shows which records carry value. Outside routes come next, once contracts and privacy duties have been checked.
What are the main data monetization strategies?
Five are common: improve operations, wrap data into existing products, sell data products or data as a service, list data on a marketplace or exchange, and license records to AI developers.
What is a data monetization platform?
Software or a service that helps a company earn from data. The main kinds are data marketplaces, data exchanges and clean rooms, data-as-a-service APIs, analytics services and AI data licensing programs. IBM defines a data marketplace as an online platform to list, purchase and trade data (IBM).
What are data monetization services?
Services that do part of the work for you: preparing and de-identifying data, finding buyers, setting terms or running a data product. Origin Data Partners is an independent data partner that helps companies get their data bought; a buyer may pay Origin when a deal closes.
Can a small company monetize its data?
Yes. The usual first move is internal, such as cutting repeat work with records the company already holds. For outside revenue, a license of a prepared copy to an AI developer needs no product team, but buyers look for years of history, linked records and clear rights.
Is data monetization legal?
It can be, within your contracts and privacy law. Personal data is the main limit: de-identify it or keep it out, and review customer and employee agreements before any outside route.
Sources and further reading
Origin Data Partners is independent and not affiliated with any buyer named on this page.