Beyond Economic Data Value

Last week I introduced the concept of The Economic Value of Data as part of a keynote speech at EnterConf.  I had submitted the title of my talk during the early stages of the research being conducted by Dr. Jim Short.

My first task during the keynote was to define the economic value of data as “the maximum amount of money someone would pay me to hand over a specific data set to them”.

Over the course of the last few months, however, Dr. Short had discovered that when it comes to data valuation, the economic value of data is only a subset of a much larger set of use cases where data valuation processes are present.

In other words, data valuation often occurs without the explicit sale of a data asset(s) from one party to another. During my keynote I highlighted a number of use cases that Dr. Short provided as evidence of this fact, and these use cases are depicted below.

During the discussion with the EnterConf crowd I provided a bit of color in terms of the valuation processes for each use case:

• M&A: during a merger there are many assets that come over from the acquired company: people, intellectual property, equipment, and facilities. In the case of LinkedIn’s acquisition of lynda.com, data was one of the primary assets that came along with the acquisition price. 
• Asset Valuation: a bankruptcy proceeding is another example where data valuation can play a significant role. Caesar’s Palace is an example of a bankruptcy where data assets actually make up a huge portion of the overall valuation of Caesar’s assets. In this example the value of data is being calculated without an actual sale being made.
• Data Monetization: some companies use data assets to drive their business processes. For example, 23andMe uses data sets from DNA kits to provide a report to their customers on their genetic makeup. Their partnership with Genentech brought additional revenue into the company. The potential revenue could amount to the same value as if 23andMe had doubled their customer base (yet they will not have to manufacture or ship another DNA kit to realize that revenue). This type of valuation also does not involve the actual sale of the asset.
• Data Sale: Earlier this year Tesco began shopping around dunnhumby, a company that possesses, among other things, a database that contains the buying habits of some 770 million consumers. After several months it was announced that Kroger acquired dunnhumby. This is a straightforward example of the economic value of data.
• Data Insurance: One of the key use cases emerging from the data value research has been the emergence and growth of the data insurance market.  TJX, Sony, and other large companies have suffered cyber-breaches that have resulted in large financial losses. As a result more companies are underwriting data insurance policies. This requires placing a financial value on a data asset outside the context of an actual data sale.

Over the next few posts I will dive into each use case in more detail as a means of uncovering patterns that allow companies to begin Architecting for Value when it comes to the emerging importance of data valuation. Architecting for Value is the goal of Dr. Short’s research; he is looking to advise the industry how to re-architect in two areas: business processes and IT infrastructure.

In an upcoming post I will spend a bit of time exploring the LinkedIn and lynda.com acquisition from a data value standpoint.

Steve

http://stevetodd.typepad.com

Twitter: @SteveTodd

EMC Fellow