Data Valuation and the Ten Common Myths about Intangibles Value and Valuation? – Price and Worth

In 2009 Patrick Sullivan and Alexander Wurzer published a comprehensive list of 10 common myths about the value and the valuation of intangibles in the IAM magazine (if you do not have access to the IAM magazine’s paid content, read Joff Wild’s blog piece about this article). The authors have a strong background in the patent commercialisation domain. For sure, data collections are different from patent portfolios. But both are (the ultimate?) intangible assets, in both domains valuation is a unsolved challenge. So what can we learn for data valuation from this demystification of the value of intangibles? In this blog piece we start with the first two myths – which help to understand the differences between worth and price of a data item or collection in a data economy.

Myth #1 – Value is a well-defined and well understood term

Sullivan and Wurzer argue that value isn’t a widely agreed term and introduce five different business definitions for value which are commonly used – and which we can easily transfer to the concept of data valuation:

  • Worth – this ist the value-in-use of a data item or a data collection to an organisation.
  • Price – this is the amount of money the owner of a data item or collection is asking from a potential buyer.
  • Transacted Price – is the amount of money which has been paid for a data item or data collection in a transfer of ownership.
  • Estimate of Price – is the transaction price expected by the data owner for future transaction.
  • Estimate of Worth – is the valuation of the expected value-in-use of a data item of collection to the current owner.

Worth is defined as the value-in-use of a data item or a data collection. While this is a very useful concept, we want to add a differentiation which was introduced by Glazer in the IBM Systems Journal in 1993. He differentiates between current actual value and potential value of information. While the first is the value-in-use which is already extracted by the data owner, the second is the value-in-use of which the owner is or could be aware, but is not realizing it.

This introduced differentiation between (estimated – actual and potential) worth, (estimated) price and transacted price is very useful, especially for the further analysis of the mechanisms in the data economy. The demystification of the myth#2 reveals the gap between prices and worth in the data economy.

Myth #2 – The value of an intangible is equal to the price someone is willing to pay

One of the common valuation techniques for tangible assets is the usage of transaction prices. For example in a stock market, the price of a share is a very good measure for the current value of the company. This valuation method requires transparent and liquid markets, which usually do not exist for intangibles. One of the reasons for this is, that intangibles are usually not commodities, but highly differentiated goods.

The worth of these differentiated goods depend on the value-in-use for a specific organization. If the expected worth of a data collection or data item is lower than a realizable price, the owner might consider a transaction.

As we will discuss later, in the data economy this equation is more complex than expected. Because the owners do not have to transfer the full ownership of data asset, they are free to license rights on the asset to different customers. Licences can have different scopes, they can be narrow and strict, or very broad and loose. Summarized, the data owners can generate multiple, independent revenue streams from one data asset. For tangible assets, like real-estates, this is not possible. An appartement can be rented only to one tenant.

The bottom line of it is, that given the necessary creativity, a data owner can “sell” data assets to multiple customers and with different prices, without loosing its own value-in-use of the data. This example reveals that price and worth of data collections or data items can be very different in the data economy.

In the next piece of this blog series we investigate the effect whether high production costs of data assets are correlated with high values of these assets.

All posts of this series:

Part 1 – Price and Worth of data assets
Part 2 – Costs and Value of data assets
Part 3 – The art of valuation

Featured Image from Olli Henze under CC licence.