How do I know if my business’s data is valuable?

Some of the most frequent questions from prospective data sellers are:

• Who is already selling their data?
• Which companies would find our data valuable? 
• What makes our data valuable to them?
• Are there compliance, regulatory, privacy, or business risks in selling data? 
• How do I get started with an external data monetisation program?

‍Types of data monetisation

There are two major categories of data monetization, internal and external.

‍Internal data monetization

‍Internal monetisation is usually the focus at large companies. Monetisation, in this context, describes a wide range of initiatives, such as analysing past performance metrics to guide strategic decisions or adding insights to a client-facing product to add value. 

The value proposition of internal data initiatives is relatively straightforward to test and monitor:

• How much has company decision-making improved?
• What is client feedback on our new data-reliant feature? 
• Who within our organisation is using these data tools and how often? 
• What is the margin impact of enriching our products with data or optimising operations?

‍External data monetization

‍External monetisation is an entirely different proposition and is one that data teams likely have less experience with. Unlike internal initiatives, success doesn’t depend on the performance of existing business units but on how valuable the company’s data is to other institutions. ‍

‍Who would be interested in using our data? 

Many companies are interested in external data sources, and this interest group is only increasing in variance and size. 

Noted in the latest industry report produced by ResearchAndMarkets, investors, and hedge funds in particular, are the largest existing group of buyers for external data; however, this dynamic is poised to change.

While Neudata’s community of data buyers is made up of investors who were early adopters of alternative data strategies, there is a growing contingent of consumer-facing companies who purchase data to guide their decision-making. Projects range from product development to geographical pricing considerations, supply chain efficiency and risk management. 

‍Who is already selling their data? 

‍A number of companies are already selling their exhaust data, and many more are exploring the opportunity. One well-known example is ADP. Its payroll report—a market-moving publication—is publicly shared for free each month. Other payroll companies sell similar panels privately to investors as data products. 

Another example of public data sharing is Opentable, which stayed in the public eye during the pandemic period by providing a dataset on restaurant reopenings. Similar datasets were sold by other types of companies, like flight aggregator websites, to help generate revenue during Covid.   

‍How do I get started with an external data monetisation program?

‍External monetisation can appear daunting for two primary reasons. Firstly, sharing data externally can feel risky, and secondly, the revenue potential of a data monetisation program can be unclear. 

To get started, it is crucial to understand what data your company has the right to sell. More info on that in our earlier blog post

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Once compliance concerns can be set aside, a series of questions  can help you estimate the value of your data:

• Relevance/value to buyers: Does your data help solve a problem for an external player? What are the use cases? Some use cases are easy to understand. Data on credit card transactions, for example, can help investors understand retail companies’ performance. 
• Accessibility of the data: Can the data be presented in a way that facilitates the contemplated use cases? If a dataset is well suited to analysing company sales, is the data linked to that company’s stock symbol (or ticker)? If the dataset reflects hiring and firing across industries and is being sold to a recruitment company, is it geographically specific? Accessibility will make the data's value proposition much clearer to a wider audience. 
• Quality of the data: Is the data complete and accurate—or at least explainable? For example, if the data shows productivity at manufacturing facilities run by various companies, do the facilities covered focus on one company, or do they make up a representative sample of each company covered? 
• Compliance with legal and ethical standards: Compliance with various regulations, from HIPPA to GDPR, is important. Putting in place appropriate and up-to-date privacy policies and T&Cs helps mitigate this risk. Outside of legal concerns, are there ethical issues that would create reputational risks? 
• Uniqueness of the data: Does your data provide a unique lens into some aspect of the world? How hard is it to obtain? Is it proprietary? The more difficult it is for a buyer to find your data elsewhere, the higher the price it will fetch.

How to answer these questions? 

‍Neudata’s team is well-positioned to help you understand the value of your data. Our process consists of four main steps:

1. Audit  a company’s data assets to identify the valuable aspects of the data
2. Evaluate how these valuable data assets fit within the market landscape of data providers
3. Review the company’s data sales approach from a regulatory and compliance standpoint
4. Introduce the company, now prepared to sell its data at the right price point in a compliance manner, to initial buyers who are looking to beta test a prototype product

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Further reading:

Previous blog post:

• How to understand and navigate compliance concerns when planning to sell your company’s data