When it comes to offering data-services, selling data assets, entering into partnerships and data-sharing agreements, agreeing on the value of data is critical. What are the methods to discover the value of data? Which strategic implications?
Like for companies valuation, several methods can be employed, they differ and each reflects a different perspective on value. Similarly to companies valuation, it’s the agreement of a buyer and a seller that sets the value and sometimes methods are used as bargaining tools for setting a price on a difficult to value asset. However, the growth of data as an asset class triggered efforts to harmonize practices and institutionalize the way data value is captured, in accounting for example.
Why is it difficult to assess the value of data
In economics, one stream of theories sees value as the cumulative efforts (human and financial) to produce a good or a service. Another stream of theories defines the value as a function of its utility, whatever the efforts required. The first is based on the offer side of the market (how much does it cost) and the second on the demand side (how much are clients willing to pay). From a particular angle, data makes no exception to these two approaches: it requires some efforts to be produced and accessed (sensors, connectivity, storage, analytics software, …) and it can be assigned a utility: making a decision, automating tasks, …
However, data has specific characteristics which limit sometimes the application of the traditional valuation methods:
- data is non-rivalrous good: consumption of data by one user does not prevent others from using the same data. Data can be used by several entities at the same time.
- data is non-depletable: usage of data does not lead to scarcity
- data value is time-dependent, but not as a linear function: real-time data is valuable as it generates immediate insights. However, accumulating it over a long period of time is also valuable as it provides long-term insights.
- data value increases when combined with other data: combining one type of data with new information can lead to new insights that cannot be extracted from only one source.
- data value increases when it’s transformed: cleaning, preparation and aggregation operations increase the value of a dataset
The 4 approaches to assess the value of data
Given the specificities we just mentioned, it’s clear that no unique standard emerged. Value is agreed upon on a case by case basis and the methods we present capture different costs and benefits approaches used by parties when entering in a trade.
- cost-based: the value comes from the efforts required to create, store, analyse, transport data. It can be measured by computing the real historical costs or estimating the costs to rebuild or acquire data.
- utility-based: the value comes from the cash-flow generated when using data, for example, the discounted future cost reductions associated with lower return ratios in e-commerce.
- market: the value comes from either a market price already accepted (e.g. the price of an email address is now pretty standard) or from a balance between the willingness-to-pay of the buyer and the willingness-to-accept of the seller.
- externalities: the value is based on the externalities created by the use of data. For example, traffic reduction in a city associated with mobility data.
What does it mean for strategy making
As previously said, the question is not about deciding which method surpasses the others, however, the growing topic of data valuation sets interesting strategic questions:
- As the value of data becomes more institutionalized, datasets are becoming valuable assets to manage with the same discipline as other assets class.
- As datasets grow bigger, the opportunities to trade them increase, hence the argumentation on the value becomes as important as it is for merger and acquisitions. Developing capabilities around data valuation sets the ground for competitive advantage.
- Markets are institutions and companies which will (individually or collectively) define a standard approach in their environment will have an opportunity to set the terms at their own interests.
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