Data Monetisation

Knowing your use case - The key to data monetisation

We explore the steps in developing a commercially attractive data product

Oct 24, 2023

Knowing your use case - The key to data monetisation

This blog post is the second in a series of three on the data monetisation opportunity. You can read the first one here.

Data monetisation is a huge opportunity waiting to be explored by many businesses.

The global data revolution, fuelled by advances in artificial intelligence (AI), is enhancing corporate performance in multiple ways — and can also boost the bottom line if businesses turn their internal data into a new sellable product.

The growing dependency on data in decision-making processes means demand for reliable and differentiated data has never been higher — almost 2,000 data vendors are listed on the Neudata platform.

So, how to become a data provider? It all comes down to the product.

To develop a commercially attractive product, sellers must understand their data’s potential use cases and associated value, which can be difficult given the secretive nature of some data-buying industries.

It’s not easy, but it is certainly possible — and this article will help companies prepare for that journey.

Read on to learn more about:

  1. Meeting buyer expectations with new products
  2. Understanding the changing alternative data market
  3. Identifying product use cases
  4. Pricing a product correctly
  5. Engaging with Neudata as a partner

Meeting buyer expectations with new products

However complex and convoluted the world of data can be, there are some basic requirements for a successful product. Here are three:

1) It must meet existing market demand — a product, however marvellous in theory, will not generate any revenues without buyers.

2) It should be as scalable and repeatable as possible, ideally purchased “off the shelf” by customers. Bespoke products can be profitable but are often associated with additional work and should be carefully evaluated.

3) It must have a Unique Selling Point. A product without a USP risks being crowded out by existing vendors, who will have more experience of the market — and use cost and marketing pressure against the entrant. A new data product requires a distinctive edge.

Companies should try and get a firm grip on the market they plan to enter by asking: what data products already exist? How are they sold? Are they off the shelf or bespoke? Is there a gap in the market to be exploited with a new product?

Understanding the use cases for hedge funds and other asset managers, who are leading buyers of alternative data, is pivotal and will be explored below.

Understanding the changing alternative data market

The market for both traditional and alternative data has ballooned in size in recent years, fueled by new buyers and expanding data budgets. The nature of the market has also changed:

Demand from the investment world was initially driven by quants, or systematic funds, which were early innovators and remain prolific users of alternative data.

They have long been on the lookout for new datasets to power complex computer algorithms that identify trading opportunities. In the past, however, the buyers proactively solicited data for specific use cases — and the data was sold in raw form, with the sophisticated quants re-fashioning it and putting it to use.

That has changed. Data sellers have become increasingly sophisticated in their packaging and understanding of proprietary data solutions, in some cases even rivalling buyers in their proactive identification of use cases. The data is sometimes still sold in a raw form, but often in a format users can “plug in” to their systems and start using immediately.

This growth trend has been driven by widely adopted technological improvements, a more competitive environment as more sellers enter the market, and an expanding pool of potential data buyers as great numbers of funds migrate to a data-driven strategy and require more data.

As far back as 2020, more than half (53%) of the hedge fund industry was using alternative data in some way, according to a study by the trade group AIMA.

According to Man Group, the world’s largest listed hedge fund manager,

discretionary funds are catching up with their systematic peers and increasingly using quantitative techniques to enhance discretionary portfolios.

Quant techniques are now being used in alpha generation, including the use of alternative data, as well as risk management, portfolio construction and performance analysis, the firm said.

Identifying product use cases

But how are they using the data? Broadly, data assets are leveraged to obtain an investment edge — to create “alpha”, in the industry jargon. However, use cases are usually related to more specific goals or a group of investable assets. For example, data depicting the purchase activity of US consumers is sometimes collected by tracking their debit/credit card spending and can help investors predict companies' top-line revenue before it is officially announced.

Some of the most commonly sourced datasets include:

  • Web-scraped/web-crawling data
  • Transactional data
  • Web- and app-tracking data
  • News and event data
  • Economic and macro data

Case study 1

To give an example of how this data may be used, quant funds might analyse weather data to predict future harvest patterns, allowing them to trade agricultural commodities such as grain more effectively. They may glean insights on crop levels after certain weather types which allows them to put on trades in real-time.

Case study 2

Another example could be web-traffic data, showing how demand for certain products corresponds to certain times of the year, like Black Friday, or events like the Super Bowl. This might translate into trading strategies reliant on certain stocks rising or falling at specific times of the year.

As noted, the investment management industry is typically private and secrecy around use cases can be impenetrable. Here Neudata’s consulting division can help companies in the midst of alternative data product development work out the best angle and use case for their data and who would be interested in buying it.

It is also important for sellers to understand possible challenges that buyers may face, starting with whether they have appropriate infrastructure in place.

Smaller and or less sophisticated buyers may be interested in certain datasets in theory, but in practice may lack the necessary manpower or technical infrastructure to extract value out of raw data. Whilst these buyers may certainly pose a challenge to new data vendors, they should be overlooked. The Neudata Consulting Team will be able to assist future vendors in pivoting their products to best serve such clients, allowing them to capture this important part of the market.  

Pricing a product correctly

Once a company has conducted a thorough product development process it will start thinking about taking it to market.

This is an exciting moment but it can also be confusing — the ecosystem is far from transparent and it’s unknown what datasets buyers are already using, not to mention what they may be paying for them. The better a prospective vendor understands the data landscape, the more effectively they will understand their own value proposition.

The Neudata proposition here can help companies understand the market offering, the products they are competing against, and how they are priced.


How much to charge for a product is the all-important question. As covered above, data only has value if it has a proven use case.

Getting clarity on pricing can be difficult for both sellers and buyers, sometimes creating a distrustful environment. Due to the opacity of the data market, prices can move without the seller or buyer knowing why.

It is a potentially confusing area. A seller may have a truly unique product but could easily undersell due to a lack of confidence — and no one will tell them that.

A seller may be too bullish, not understanding that there are similar solutions on the market, and won’t be able to sell. Buyers will say it’s too expensive without giving context.

There is no rulebook when it comes to pricing alternative data, but Neudata can help new vendors assess the competitive environment and work out their pricing, leveraging truly unrivalled market awareness.

Engaging with Neudata as a partner

The Neudata consulting division helps companies establish whether they have sellable data. The unit can help:

  • Assess the value of data, by providing intelligence on what others are charging/paying
  • Determine which products a company could build with their data
  • Define use cases and rank their market potential
  • Help firms navigate client and reputational risks by advising on an ideal data aggregation level to enter the market
  • Provide guidance on compliance and legal issues Help structure data collection and contracts to be compliant and/or have the required ownership

About Neudata

Neudata is a  research platform for alternative data that specialises in the objective and neutral assessment of data vendors and datasets.

Neudata helps institutional investors, corporations and leading global organisations find the most relevant alternative data sources to use in their internal data ingestion processes.

The Neudata platform is the global authoritative source for unbiased, independent alternative data intelligence. It captures 90% of the global alternative data-solutions supply.

Neudata doesn’t buy or sell data, or require data providers to pay a revenue-share or commission in exchange for recommending their products to data buyers. That means users get unbiased intelligence that’s tailored to their specific research goals and strategies.

It also means Neudata is ideally placed to consult on data monetisation and help companies create new revenue streams in this field to future-proof their business in a tech-driven world.

If you are a data provider and want advice on monetising it, contact to discover how Neudata can help.


Casting the Net: How Hedge Funds are Using Alternative Data, AIMA

The Good New Days: Incorporating Quantitative Techniques into a Discretionary Portfolio, Man Group

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