Data is the gateway to action – but analyse before you act (Pt 2)

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As we outlined in part 1 of this article, the starting point of any data monetization journey is the realization that data is the gateway to action. The value of data comes from its ability to generate insights, which enables management teams to make better decisions, which then leads to the company being able to take more effective actions.

However, before embarking on a data monetization journey, companies need to understand what the return on investment will be. Not every data driven action will have a positive outcome and, sometimes, the monetization of the data simply does not exceed the cost and risks involved.

To better understand all of this, you need to build a business case.

Building the business case for data monetization

Building a data business case enables you to determine the potential economic return from a particular data asset deployed via a particular business model to generate a particular business outcome (called an end use case).

It’s important to note that with large or complex data sets there are likely to be multiple different end use cases (different potential actions > outcomes). By properly building and analyzing each end use case, you can start to work out what is involved in utilizing a particular set of data and crucially, the economic outcomes from that activity.

This then enables you to determine:

  1. The potential returns from a particular data monetization initiative;
  2. The investment required to fund that initiative;
  3. The potential returns versus the cost;
  4. The risks associated with trying to extract value from that data in that particular business context; and ultimately,
  5. The value of your data.

This should familiar because it is: when it comes to monetizing data, management teams should subject their data to the same kind of cost/benefit and return on investment analysis that they would apply to any other business asset.

Take, for example, a situation where your company suddenly discovered it had an abandoned piece of real estate. Prior to any decisions being made about what to with that land, management would first ask some basic questions. What is the nature and quality of this real estate? What can we do with it? For example, do we put our own factory on it? Can we develop it into apartments? Should we lease the land to someone else? Or should we sell it in its entirety?

In this scenario, you’d see the management team look at all its various options.  It would consider what kind of revenue would be generated; the associated costs; the likely risks; and what the return on investment would be for each option. This information would enable the business to know what the best risk adjusted return on investment from the monetization of the land would be.

Driving insights and revenue

Data is absolutely the same. To make the business case for data utilization, management teams and boards need to ask:

  1. What data does the business generate or have access to?
  2. What will the cost be to extract the data?
  3. What different end-use cases can be applied to that data?

Then, for each end-use scenario, a business case should then be developed that estimates:

  1. How much money can be made from using the data in each particular context?
  2. What the costs are associated with doing so?
  3. What are the potential technical, reputational, and legal risks associated with each use case?

From here, management teams are then able to be able to say which (if any) of these end-use cases stack up and to determine which ones to take forward.

This should be obvious but unfortunately most people treat data as if it is a unique resource that doesn’t obey the normal rules of business. In fact, data is like any other business asset and should be treated as such. The fundamental business principles of building business end-use cases still apply and are in fact critical if the end-goals are to not only derive value through data insights, but to monetize data.

Value is in the eye of the beholder

The final point to bear in mind is that data can sometimes be more valuable in a third party’s hands than your own. Take for example a security company that has collected several years of meteorological and crime data that enables insights to be extracted about the impact weather has on crime in a region. It uses this data internally to help mitigate risk by adjusting security measures and staffing levels accordingly. However, in addition to the insights this data provides to the security company it also has the potential to offer valuable insights to other organizations for example local retailers, insurance companies or police. If the security company chose to, it could potentially create high margin revenue by selling or licensing the data to multiple third parties while still being able to access and utilize the data for its own purposes.

They key point to realise here is that there is no marginal cost for the utilization of data. Once the data has been identified and extracted, it can be utilized numerous times for numerous purposes. Therefore, companies need to avoid the pitfall of only analyzing how data can be used for internal purposes, but should also investigate the potential use cases with third parties. Failing to do this can mean that significant revenue opportunities remain unidentified and untapped.

In summary, data can be extremely valuable, but it can also be a major source of cost, distraction, and risk. Merely having data means little: it is the uses to which it is put – the decisions, actions, and outcomes it enables that generates value and that value can sometimes be worth far more to an external party than it can to the original data collector.

Too many companies charge into data monetization initiatives focusing on technical issues with too little attention on exploring and building a robust business case to determine if that data is really gold, or just a pile of dirt.

If you’d like to understand how EverEdge can help you on your data monetization journey, email

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