Turning internal data into external revenue: what it actually takes

The external data market is accelerating in double-digits year on year. For organisations sitting on untapped data assets, that represents a significant and largely accessible revenue opportunity. The challenge is knowing how to approach it correctly.

This article is a companion piece to Neudata's webinar, Data for Sale: Creating high margin revenue streams using your existing data assets, now available to watch on demand. It addresses the key themes and insights from that session for those who want a structured written reference alongside the full webinar.

There is rarely a board meeting today that does not, at some point, arrive at the question 'What more can we do with our data?' It is a legitimate and increasingly urgent question, and the good news is that the market has never been more receptive. Buyer budgets are growing, appetite for new datasets is at an all-time high, and the vast majority of data buyers surveyed by Neudata expect spending in 2026 to increase or stay the same.

The opportunity is real and substantial, however for many organisations, the path from 'we have valuable data' to 'we have a profitable data business' is less straightforward than it first appears. It requires strategic thinking, disciplined productisation, and a clear-eyed understanding of what the market actually needs. That is precisely what Neudata's VP of Corporate Solutions, Michael Hejtmanek and Commercial Lead, Stuart Broughton addressed throughout the webinar, and what follows captures the most important thinking from that conversation.

Is your data actually valuable to external buyers?

Understanding what makes your data valuable to an external buyer is the essential first step in any monetisation strategy. While data may be deeply embedded in your operations, its external market value is determined by four measurable factors: uniqueness, frequency, coverage and signal quality. Datasets that score highly across all four typically command average revenues of £1m/yr or more, based on Neudata's 2025 platform data.

As Michael Hejtmanek illustrated in the webinar, two organisations can hold datasets in the same broad category, and yet experience wildly different commercial outcomes. The success of a dataset comes down to:

• Uniqueness: Data that is widely available elsewhere loses market value rapidly. Alpha decay, the erosion of a dataset's signal as more buyers trade on it, is a well-documented phenomenon in quantitative finance. One data acquisition executive at a major market-making hedge fund told Neudata, "I want to be the first to see a dataset. Maybe the second".

• Frequency: Update cadence directly defines your addressable buyer universe. Daily or streaming data opens doors that monthly or quarterly feeds cannot. Quantitative and systematic funds in particular require high-frequency data as a baseline requirement.

• Coverage: Even high-quality data can fail to convert if it does not meet a buyer's geographic or sectoral scope. Neudata has observed trials fail at the final stage due to insufficient coverage against a buyer's index model, not because of any quality issue.

• Signal quality: Ultimately, if the data does not demonstrably improve a decision or a model, it will not sell. Buyers, particularly investors, require evidence of predictive value before committing to a commercial relationship.

Understanding precisely where your data performs, and for which buyer type, is the foundation upon which a sustainable data business is built.

Who buys external data, and what do they each need?

There are four primary buyer segments in the external data market: hedge funds and asset managers, enterprise businesses, data aggregators, and private equity or M&A teams. Each has distinct requirements around data format, compliance and commercial structure. Selling one undifferentiated product to all four is one of the most common and costly mistakes in data monetisation.

This was a point Stuart Broughton and Michael Hejtmanek explored at length during the webinar, drawing on Neudata's insight from both sides of the market:

• Hedge funds and quantitative asset managers prioritise clean, PII-free, signal-rich data with demonstrable predictive value. They expect rigorous due diligence documentation, including a completed DDQ, schema documentation, and ideally a backtesting report from a recognised systems integrator.

• Enterprise buyers are oriented towards operational integration, competitive intelligence and building a richer picture of their customers. They value ease of onboarding and clear use-case alignment.

• Data aggregators evaluate fitness, scalability, resell rights and roadmap. A semi-exclusive wholesale arrangement with one or two aggregators can be a highly effective market-entry strategy for new data providers.

• Private equity and M&A teams typically represent one-off, premium-priced diligence use cases, a different commercial model to the annual recurring revenue business built through hedge fund or enterprise relationships.

• Other buyer segments are also important, with consultancies, corporates and (to a lesser extent) governments all playing an important role for some types of data.

A single dataset may need to be packaged as multiple distinct products, each positioned, priced and documented for a different audience.

How long does it take to generate revenue from selling data?

Most organisations take 9-18 months to reach first revenue from a new data product. The shortest timeline Neudata has observed is 7 months. Internal governance delays, compliance sign-off and stakeholder alignment can extend this to 24 months or beyond.

As discussed in the webinar, the trial process alone, typically unpaid and lasting approximately 3 months, requires patience and dedicated resources. Trials tend to focus on historical data periods representative of relevant market cycles. Therefore, while supporting trials does consume resources, due to its historical coverage and contractual restrictions, the data cannot be traded upon in current periods. Deals most commonly fail not because of data quality issues, but due to:

Pricing misalignment: The leading commercial reason trials do not convert to contracts.

Friction in the sales and onboarding process: Insufficient documentation, slow response times or poor delivery infrastructure

Compliance concerns: Questions about data provenance, consent and resell rights that were not resolved prior to the trial

The businesses that succeed treat data monetisation as what it is: a new product launch with dedicated ownership, proper investment and a clear go-to-market strategy.

"Data monetisation is not really a data exercise. Most of the work in bringing a dataset to market is a business build, it's launching a new product."
Michael Hejtmanek, VP Corporate Solutions, Neudata Consulting

What is the broader strategic case for data monetisation?

Monetising data assets delivers benefits beyond incremental revenue. Organisations that prepare data for external sales report accelerated data maturity, stronger internal governance and improved compliance frameworks across the business, not just within the data sales function.

Beyond the revenue opportunity, there is a compelling secondary benefit that is often overlooked, and one that Michael made a point of raising in the webinar. The discipline required to prepare data for external sale, including governance frameworks, schema documentation and compliance structures, acts as a forcing function that creates lasting internal value. It also establishes market authority: Neudata cites the ADP payroll report as a long-standing example of how data publication reinforces market leadership at scale.

Neudata's 2025 data provider survey found that the two greatest barriers to successful data monetisation were reaching the right contacts within buyer organisations (cited by 50% of respondents) and understanding the specific value their data holds for different buyer types. Both are addressable with the right advisory support and market access.