Data Monetisation

Overcoming internal barriers to data monetisation: balancing risk and reward

After deciding there is likely value in your company’s data, what are the internal barriers to selling proprietary data and how can they be overcome?

Jan 29, 2024

Overcoming internal barriers to data monetisation: balancing risk and reward

This blog post is the last in a series of three on the data monetisation opportunity.

In the first blog post, we discussed the alternative data market broadly, noting how large the market is, what data is used for and who is buying it. In the second blog post, we discussed how a prospective data vendor could effectively understand the value of their data by identifying its use case for investors. 

This edition will focus on the next significant challenge - after deciding there is value in your company’s data, what are the internal barriers to selling proprietary data and how can they be overcome? 

While the high-margin revenue that can be generated by selling data is an attractive premise for businesses across industries, the first reaction for many is to shy away due to perceived risks to existing client/consumer relationships. 

This blog post will focus on better understanding the relationship between data monetisation, compliance and reputational risk. It aims to provide crucial context to businesses who are thinking about exploring monetisation but don’t feel they understand the risk landscape well enough to reach for the rewards. 

Read on to learn about:

  1. Use cases: A refresher
  2. Personal identifiable information (PII) and data usage
  3. Aggregating data: A layer of security for existing clients
  4. Navigating compliance: Legally selling data


Before investing in turning your company’s proprietary data into data products, it is important to understand whether your data has a relevant use case for potential data consumers. A use case is the first layer of validation that a prospective data vendor should look for. 

To best understand the data's relevance in the market and the potential impact selling the data would have on existing relationships, sellers must be specific about what their data can be used for. 

  • How would clients use this data in practice? 
  • Which industry or sub-industry does the data help buyers understand? 
  • What specific companies does it provide insight on?

Summing this up in one sentence is a valuable exercise. Here are a couple of examples:

  • Data product A provides insights into companies that are buying spare parts for machinery. The data helps investors understand which companies want to maintain existing tools and which may be investing in new equipment. 
  • Data product B provides anonymised electronic health records that show what a doctor prescribed a patient. It can be used to track which pharmaceuticals are performing well and are particularly relevant at different times of the year/in different regions.


One of the primary objections Neudata’s consulting unit hears from potential data vendors who are in consumer-facing sectors is that they have (a) misgivings about sharing customer information, (b) concerns around data sales becoming public or (c) the wrong documentation in regards to owning the data and having a right to sell it.

Issue (a)  brings us to a fundamental distinction between monetising data for investors and monetising for other industries. For investors, PII - information revealing the personal information of individuals - is an immediate deal breaker for buying a dataset. This is the case for two reasons: 

  1. Usage: no single consumer’s actions will shift market dynamics or impact the price of investable asset, and - as a result - investors cannot effectively use PII as information. This is in stark contrast to advertisers who are purchasing data to target specific audiences better.
  2. Regulations: the financial services industry is one of the most closely regulated industries in the world, and various regulatory agencies that monitor the operations of institutional investors preclude the use of any PII. 

Issue (b) is a valid concern but can be addressed in many ways - including selling data through an aggregator. Neudata is well-positioned to advise on the avoidance of headline risk. 

Issue (c) can create problems in some cases but is often irrelevant if the prospective data vendor sells data void of personal information. Neudata is also well-positioned to advise on approaching data sales from different compliance positions. 


Prospective data vendors do not only express concerns about revealing personal information; they also have concerns about sharing information on customers/clients and suppliers and thereby damaging their B2B client relationships. To assuage this risk, a company can aggregate their data, selling insights that are slightly less granular than they have available and, therefore, not revealing the activities of their partners. 

This risk - and the associated solution - is best explained by way of example: 

  • The theoretical company: a manufacturer of building materials for commercial real estate, specifically window glass used for windows in high rises. 
  • The potential use case: this company has insights into which firms are winning high-rise contracts by tracking the volume of glass ordered by different companies.
  • The risk: The clients - construction companies - would likely not want their glass buying activities revealed to third parties and, upon learning about data sales, could take their business to another materials manufacturer. 
  • The solution: The prospective data provider could aggregate this data. Rather than supplying which companies are buying glass, their data product could reveal what cities, regions, or neighbourhoods are showing an uptick in glass orders and, thus, are likely seeing more building activity. 
  • In sum: While this aggregated product may be less valuable to an investor than information on company purchasing, it may still provide helpful insight without adding risk to the data-collecting business.  

Of course, this is quite a specific example of how aggregation could assuage business risk. Still, the concept of reducing the granularity of proprietary data before sale can be applied to a diverse range of data use cases. Neudata’s consulting team can help identify how data can be aggregated to maintain its value without risking existing client/consumer relationships. 


Outside of reputational risk related to the sale of personal data and the need to protect existing business relationships, there is an additional barrier to data sales. Put briefly, does your business have the right to sell the data collected during business operations? 

The difficulty with this question is that it changes on a case-by-case basis. In many industries, there are already set ways of constructing terms and conditions to allow the sale of data. In many others, the monetisation of exhaust data is new and standards are only being developed. 

Ultimately, the compliance knowledge related to data sales can take years to accrue, and Neudata can provide guidance, having worked with more than 2,000 sellers - the largest vendor network globally.

Neudata uses this extensive experience to help new vendors align not only their compliance standards with the market but also to direct their product development, identify their use cases, and refine an approach to marketing and sales.

Neudata doesn’t buy or sell data or require data providers to pay us a revenue-share or commission in exchange for recommending their products to buyers. This means users get unbiased intelligence.

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

Blog suggestion

Suggest a topic for the Neudata blog

Suggest a blog topic

Newsletter sign up

Weekly alternative data news, insight and trends straight to your email

Subscribe to Newsletter