A successful market data business is great news for any company. However,

Know it or not, like it or not, everyone is in the data business, as a consumer, and/or as a creator, for example if you are on Facebook then you are contributing to their bottom line (If not necessarily their tax bill).

The growing realisation that data has a value that can be measured in dollars is a misunderstood truth and concept because the new data sources too often think “I have data, so I can make money”. The world does not work that way, just having data does not mean it will magically conjure up data gold. What is needed is to put together the parts making up the data business jigsaw.

Yet few companies seek to turn their own data into a revenue generating business.

This article takes a high-level view of strategies to get going, and the pitfalls lying in wait.


This is a moving target, so flexibility is a requirement. At the same time what the market wants, what is available, and from what is available- what can be developed into commercially viable services that the consumer can themselves use to benefit their businesses by using, rarely coincide. It is down to the 4 basics.

1.Having unique quality datasets, understanding their intrinsic value, and understanding how consumers will make money from it

2.What data is in demand right now? We see Alternative Data, Commercial Data, ESG, and OTC Markets Data (Bonds, Energy, Commodities) as topping the list

3.Attributes: Quality is essential, data must be Accurate, Consistent, and Timely, with access to historical records

4.Accessibility: Technology and partnerships are core, how do clients want to get the data? New channels, new facilitators are changing data workflows.


For data source owners looking to monetise their data to generate revenue, (and for some companies the value of their data is worth more than the underlying business) a coherent approach is required to mitigate the errors that many have already made, for instance:

  • Many new data sources have little to no expertise or experience in creating a data business, often they think just because they have data, there is a market for it. This is a false assumption. Expertise is required.
  • Data Sources must invest in developing a business models, harnessing technologies, building partnerships, and generating sales. There is a cost involved, but it need not be exorbitant or prohibitive, and usually is not. Resources are required
  • Concepts such as Intellectual Property Rights (IPRs) are the beating heart of the data business, but often misunderstood or inadequately addressed. New ways to use data usage always precedes new ways to charge for it and so data’s incredible versatility which makes data so valuable is also an Achilles Heel. Establishing ownership and rights is an absolute requirement


In the data market ‘First Mover Advantage’ is critical to success because data embedded within clients’ workspace environments becomes incredibly sticky. Once ingested it becomes hard to remove or displace, leading directly to stable and continuous revenue flows.

This places an emphasis on the business to be flexible with the ability to develop effective business models, areas putative data sources are notoriously weak in.

Importantly the data supplier must take responsibility to ensure a fair playing field for all clients, through transparency, and non-discriminatory practices.


Trying to do everything yourself is not going to work, so getting the tools for the job is important, including importantly partners:

  • Data is a collaborative game, no company does it all, or on their own. Partners provide reach, knowledge, experience, expertise, technology and tools.
  • At the initial stages, especially creating the data assets, and business models, consultants (Shameless plug-such as DataCompliance) provide expertise and experience, and have already learnt the lessons from their mistakes
  • Data sources are not technology companies, there is often the mistaken view they are the same thing. Wrong! Once this has been realised technology partners and new channels to market become enablers and facilitators that then act as ‘force multipliers’. These do not have to be the large ‘brands’ as there are innovators in publishing, distribution, and accessibility, like Activ Financial, and Arcontech
  • Even the largest existing data sources, such as the big stock exchanges, lack global presence, so partnerships can even extend to sales, again there are ground breakers such as EOSE and USAM
  • A forgotten point of failure is lacking the right tools to administer, report, and invoice for the business, so service offerings from companies like DataBP, MDSL, and TRG Screen are imperatives


Building a data business does not need ‘Rocket Science’, what it needs are 4 simple things:

1.Commitment to establishing and then building the business

2.Creating the data environment from getting to know your data, building the business model around the data,   leveraging technology to get your data to your clients, and selling (licensing) the data

3.Provide the resources to achieve success

Have in place the operational tools to manage the business

Keiren Harris 20 July 2022

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