Why sell data?

Why sell data? This isn't a real question, right?

It's very obvious why a company would want to sell data, and it's the same answer to why a company would want to sell anything - because they can make money!

But the real world is not so simple. Companies have many ways to make money, so how should they think about selling data as it compares to other opportunities?

In this blog post, Michael Hejtmanek from Neudata and Solomon Kahn from Delivery Layer have partnered to cover the market opportunity of selling data alongside the risks and potential downsides.

With these benefits and drawbacks in hand, you'll be far better equipped to make a decision on whether selling data makes sense to you.

Benefits of a data business

Do you have a nagging feeling that your data is worth something? Does it bother you to have it just sitting there when it could be doing something to make your company revenue and improve your market position?

You are not alone.

In the same way you value your data for internal decision-making, others outside your company might also find value in it and will pay to get it.

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"The most forward-thinking companies don’t just use data—they monetize it. A well-structured data business doesn’t just create revenue; it uses data to create competitive leverage." Michael Hejtmanek, Head of Data Consulting at Neudata.

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These types of businesses are often called DaaS businesses (Data as a Service).

Rhymes with SaaS

You would be right to notice that the acronym sound similar to SaaS (Software as a Service).

That similarity is on purpose, because many of the things that make SaaS businesses so compelling and valuable make DaaS businesses equally compelling!

Two of the big ones are high margins and low churn.

When you sell data you already have, it leads to incredibly high gross margins. Once customers integrate your data into their businesses, you become difficult to replace, leading to low churn and high customer lifetime value. These are the reasons that SaaS businesses trade at such high multiples, and they also make DaaS businesses very compelling.

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"Once data becomes embedded in your customers’ workflows, it’s not just a product—it’s an integral part of their decision-making. That’s why DaaS businesses, like SaaS, drive loyalty and long-term value." Michael Hejtmanek, Head of Data Consulting at Neudata

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Covers your team

Another important benefit of a data business - particularly for Chief Data Officers - is that a data business can VERY quickly pay for your entire data team.

The larger data & analytics market is having a crisis of "business impact" for data teams. Data leaders are being asked to justify their existence with real revenue that was driven by the data team.

The beauty of using data as a revenue driver is that if you have a path to increasing company revenue 3-5% -> that generally pays for your entire data team. Even if your data business is just a small part of your overall business, it can be a large part of how you justify the value of your data team.

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"Selling data can very quickly pay for your entire data team." Solomon Kahn, Founder of Delivery Layer.

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Premium positioning

One of the most overlooked benefits of launching a data business is the strategic leverage it can provide. Sharing or selling data can establish your company as a thought leader, influencing industry discussions and shaping market narratives.

A great example of this is ADP, who report payroll data to the financial news every month. When analysts and journalists reference “the ADP payroll numbers,” ADP isn't just sharing insights—it’s reinforcing its position as the authoritative source on employment trends. This visibility enhances its brand recognition and trust among businesses, policymakers and investors.

Beyond thought leadership, having a data product can serve as a unique differentiator for your clients. For instance, companies in B2B industries can bundle access to market intelligence or benchmarking data as a way to set themselves apart from the competition and close large enterprise deals.

These ancillary benefits—brand credibility, industry influence and differentiation in enterprise deals—can make launching a data business far more valuable than just the direct revenue it generates.

Market opportunity

The above all sounds great, and who wouldn't want a high margin business with high recurring revenues that pays for your data team and positions you as a market leader in your space.

But is the market actually there?

In this section we'll go through the market opportunity for data businesses.

Key industries using data

There are two big market categories of data buyers, and they have very different needs and requirements.

The first type of buyer are financial buyers who use the information you share to make better investment decisions. These customers, typically hedge funds and banks, are sophisticated buyers of data who often have teams that specialise in the acquisition of new datasets.

Hedge funds don't need much hand-holding to learn how to use the data, and typically want you to send over a whole bunch of raw data that they will then process and use to make investment decisions. There’s a well established way that hedge funds evaluate and purchase new datasets, and Neudata have a lot of experience in this domain.

The next type of data buyer is a corporate buyer. These buyers are generally far less sophisticated than the hedge fund buyers. They are typically buying data to help power their products or drive some insights into their market. Some data providers cater to corporates with varying levels of data literacy by providing a mix of dashboards, packaged insights, and raw data.

Many of these corporate buyers are looking for interesting charts and insights to include in PowerPoint presentations that drive corporate strategy. They might even want analysts from your company to join their internal meetings and explain the overall market as well as the specific insights for their company based on your data.

Companies are using more and more external data over time, although the market growth is linear vs. exponential.

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"Hedge funds want raw data to fuel investment models. Corporations want insights they can act on. Understanding this difference is key to a successful data business." Michael Hejtmanek, Data Monetization at Neudata.

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Rise of Alternative Data

Over the past twenty years, investors and companies have become increasingly sophisticated in using creative sources of data in unexpected ways. In the industry - these “creative” data sources are typically called "alternative data."

It’s not the best name because “alternative” data was coined to be “alternative to the standard data that financial analysts used 20 years ago.” But it was a cool name so it’s stuck around even though now we’d probably just call it “data.”

An example of an alternative dataset would be satellite photos. You can use satellite data of Walmart parking lots to count the cars and estimate the growth or decline of the Walmart business. Investors can use this data to predict Walmart earnings, but competitors can use the data as well to understand how well or poorly certain stores are doing.

There are companies that scrape Amazon and use that data to build market intelligence tools, analytics products, customer sentiment analysis around reviews, pricing tools and more. The potential datasets and use-cases go on and on…

Regional opportunities

The US market for data is still early. The global market for data has barely gotten started.

For global companies that have interesting datasets in underserved regions such as India or Brazil, there is an emerging desire for insights into those global regions.

As a quick data point, Neudata has found that approximately one-third of data requests are for global datasets. If you have a global dataset, there can be big opportunities.

The rise of AI

And of course, last but not least, there are large opportunities to use data for AI-focused use cases.

"AI leads to more customers that NEED data, and more ways to CREATE that data." Solomon Kahn, Founder of Delivery Layer.

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Whether its publishers or social media sites using massively large sets of data to train foundational AI models, or companies offering interesting datasets on people, places and things that can be used by AI agents... We’re still in the early stages of how AI models will use various datasets to solve problems big and small.

And you can also use AI to GENERATE interesting datasets that would have been impossible without AI. You can take data and ask lots of different questions about that data to an AI model to enrich and add new features to the data.

Risks - Business & legal

There are two categories of risks that companies need to be aware of when they're thinking about commercialising data - business & legal.

Business risks

Like any new business, starting a data business comes with risks.

From our experience, the biggest risks come from not understanding the market and building something people don't want and won't pay for.

The most common ways this happens are:

• Overestimating deal size
• Overestimating market need & size
• Challenging sales process
• Thinking you have unique data when you actually have multiple competitors

One of the most important things you can do to avoid falling victim to these risks is to get out into the market early and talk with people who have experience launching data businesses (both Neudata and Delivery Layer would be happy to speak with you).

Once you have a stronger, market-informed business case, you'll have a better idea about whether or not you've got a real opportunity. It may turn out that starting a data business would be a distraction that couldn't hit your revenue hurdle rate. Better to learn that early.

Legal & regulatory risks

There are common hesitations and misconceptions surrounding the legal and regulatory landscape of data products. While the specifics vary by geography and data type, the guardrails have become increasingly well-defined in recent years, making compliance and navigation more straightforward.

Privacy & sensitive data

It's important to understand how to protect privacy and personally identifiable information (PII) that might be contained in your dataset.

For certain businesses, there might be concerns about material non-public information that could be calculated using the data you're sharing.

Market considerations

There can also be concerns about the customer, supplier and market perception of launching a data product. Will your customers be happy, or will they see it as a violation of their expectations?

And you would be surprised that often, the internal assumptions about what customers will think aren’t true - on both sides! You would expect there to be some cases of customers who are concerned about data products, but often customers are very happy to contribute their data in order to get benchmark and market intelligence information on their industry.

Legal rights

Lastly, there's the question of whether you actually have the right to sell your data. Sometimes you may have data in your systems but not the legal right to turn it into a product.

Obviously there are industry specific legal considerations such as when dealing with healthcare data, financial data and other sensitive data.

Even for data you might think is not sensitive, it's important to have all the correct legal language in your terms & services and customer agreements to ensure you have the legal right to use your data for the specific product you are looking to build. There are frameworks that can be used to address all of these concerns and ensure that whatever data product you bring to market, it is compliant and executed in a proper way. This is a big focus of the work Neudata does with customers as they prepare to bring data products to market.

Is it worth it?

You could build a great business with high margins and low churn that easily pays for your data team and positions you as a market leader.

It could be a disaster with no real market for your data, distracting you from your core business, with legal and regulatory problems to boot.

You're not going to know which it is by staying inside the walls of your company. You need to talk to potential customers, and bring in some market understanding from people who have done this many times.

Neudata is one of a small handful of consulting companies who have extensive experience doing this work so reach out to Michael Hejtmanek if you would like some help navigating this challenge.

And while Delivery Layer is a tool for building data products and doesn't have a consulting offering like Neudata, the company sees a lot of new data products and is always happy to have a chat to share its experiences. You can reach out to Solomon Kahn on LinkedIn.

In the next post of this three-part series, we'll cover how to scope out the actual data product, which is the next step after you've decided that you want to pursue building a data business.

Stay tuned!

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