If your company has been running for a few years, it holds data that someone would pay for, and you may not have thought of it as an asset. Data monetization is the business term for turning that data into revenue, and until recently it meant building dashboards, selling reports, or packaging a data product. AI labs changed that. They now pay for the ordinary operating records of ordinary companies (support tickets, sales histories, project tools, code, documents), and they pay well. Read on to find out the four ways companies monetize data, the public examples and prices behind each one, and how to work out which route fits you.
What data monetization means
Data monetization is any way a company earns money from the data it already holds. Some of that money is indirect (you use your data to sell more or spend less), and some is direct (someone pays you for the data itself or for something built on it). This guide is about the direct kind, because that's where the money has moved in the last two years. Our guide to data licensing as a business model covers the licensing route on its own, and this page puts it next to the other three so you can compare.
The four ways companies monetize data
Every data monetization example fits one of four routes, and each one asks something different of you.
| Route | What you sell | Who pays | What it takes | Example |
|---|---|---|---|---|
| Data-driven products and services | A better version of what you already sell (pricing, recommendations, benchmarks) | Your existing customers | Analytics work inside your product | A software company charges more for a plan with benchmarks built from customer data |
| Insights and reports | Aggregated findings from your data, with no raw records | Companies in your industry, analysts, investors | Cleaning, aggregation, a report or dashboard to sell | A payments company sells spending trend reports built from transaction volume |
| Data as a product | Raw or enriched data delivered by feed, API or file | Other businesses that need the data itself | Rights checks, packaging, delivery, sales | A weather or maps company licenses its feed to apps |
| AI training licensing | A copy of your operating records, with personal details replaced by pseudonyms, for training AI models | AI labs and AI startups | A short scoping call, a few hours on exports, a marketplace that handles the rest | A 60-person SaaS company licenses eight years of tickets, deals and code |
The first two routes are where most data monetization advice stops, and they suit companies with a data team and a product to build into. The third is a real business for companies whose data is the product. The fourth is the newest and the most open, because it's the only route where you don't need to build anything. You license what already exists.
Data monetization examples from the AI licensing market
The clearest examples of direct data monetization today come from AI training deals, because the prices are public. Here's what the record shows:
- News Corp licensed its news archive to OpenAI for a reported 250 million dollars over five years.
- Reddit licenses its conversation data to Google for about 60 million dollars a year, and to OpenAI in a separate deal, so the same data earns twice.
- Shutterstock earned 104 million dollars from AI licensing in 2023 across five major buyers, and 138 million dollars in 2024.
- Amazon pays The New York Times a reported 20 to 25 million dollars a year.
- Google bid 10 million dollars in bankruptcy court for Spirit Airlines' operating records. Our breakdown of the Spirit Airlines data deal explains why an airline's internal archive drew a bid at all.
- Startups that shut down have sold their code and workspace archives for roughly 10,000 to 100,000 dollars per deal through closure platforms, and a single repository has fetched about 5,000 dollars.
The first four are famous catalogs owned by famous companies, and you'd expect them to command famous prices. The last two are the ones that matter for a company like yours. Spirit was a bankrupt airline, and the startups were closing. Their data earned money because it showed how a real operation ran, and that record doesn't exist on the public internet. You can find the full set of public figures on our AI training data statistics page, and our guide to how AI data licensing deals work covers what gets negotiated in each one.
Why AI labs opened data monetization to ordinary companies
AI labs are training models to do work, and a model that will handle a support ticket, close a deal or fix a bug needs to have seen thousands of real ones. The public web has advice about those jobs and almost no record of them being done. Your systems hold that record. A CRM with six years of deals, a help desk with every escalation, a code repository with every review and every fix, a Drive full of proposals and postmortems. Buyers are paying six figures for full-company datasets like these, and the demand is growing as labs move from chatbots to agents. Our guide to enterprise operational data explains what this data is and why labs want it.
This route also changes who can monetize data. You don't need a data team, a product to build into, or a catalog of your own. You need a company that has been doing real work for a few years, in tools like Slack, HubSpot, Jira, Zendesk, GitHub or Google Drive. Our guide to how tech startups and SaaS companies license their data to AI walks through a typical stack system by system.
What makes your data worth monetizing
Six things decide what your data earns, on any of the four routes, and volume by itself does little. A 40-person company with ten years of specialized records can be worth more than a big company with generic data. In order:
- How unique it is. Records nobody else has price differently from records everyone has.
- How many years of history you hold. Longer histories show more change and more outcomes.
- How connected your systems are. A ticket linked to its Slack thread, its Jira issue and the pull request that closed it is worth more than any of those on its own.
- Whether your records show outcomes. A deal that closed, a bug that got fixed, a runbook that worked.
- How clean the rights are. Data you own outright, with customer contracts that allow it, moves fast.
- What buyers want right now. Demand follows what labs are training, and today that's work agents.
Our guide to how much your company's data is worth to AI labs goes through each factor with the price points behind it.
How to monetize your company's data, step by step
Whichever route you choose, the first three steps are the same, and for AI licensing the whole process runs in six.
- Take inventory. List the systems you run, how many years each one covers, and roughly how much is in it. Don't export anything yet.
- Check the rights. Work out what you own outright, what your customer contracts restrict, and what came from third parties. Anything you can't license cleanly comes out of scope.
- Pick the route. If you have a data team and a product, the first two routes may pay. If your data is the record of how you operate, AI licensing is the route that pays without building anything.
- Scope the license. On a short call, agree which systems are in, which are out, and what you want excluded before anything ships. You can start with one system and add more later.
- Package it. Export the data (a few hours, nothing installed), and let the marketplace replace names, contact details and client identities with pseudonyms to a documented standard before any buyer sees it.
- Get paid. Licensing usually means the same dataset is licensed more than once, so the first sale is the floor, and you're paid on every sale after it.
Our owner's guide to licensing company data for AI covers steps four to six in detail, and our guide to selling data to AI companies covers who's buying and what sells.
Mistakes companies make when they monetize data
Companies that get this wrong tend to make one of four mistakes:
- They sell the data outright when they could license it. A sale pays once. A license pays every time the data is used, and you keep ownership. Our guide to exclusive vs non-exclusive data licenses explains the tradeoff.
- They include customer data they had no right to license, and the deal unwinds in the rights check.
- They clean up the records first. The messy parts (the escalation, the reverted release, the deal that fell through) are what buyers pay for.
- They wait for a data team they'll never hire. AI licensing needs an export and a scoping call, and the marketplace does the rest.
Where to start
The quickest way to find out whether your data is worth monetizing is to take our free data value assessment (ten questions about your systems, history and industry, about five minutes). You get an estimate of what your company's data could license for, and a scoping call turns that into a real number.
Where Troveo fits
Troveo is a data licensing marketplace. We help companies understand what data they hold, protect what matters, and license what's valuable, selectively. We've paid rights holders more than 20 million dollars across video, audio, gaming, robotics and business data, we work with more than 40 active buyers, and we take no fees and no deductions from your payouts. You keep ownership, you decide what's in and what's out, your company is never named publicly, and you're paid on every sale. Start with the data value assessment, or read our business data page for how the process works, or talk to us about what your company holds.