Value Shift: From Products to Data

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Earlier this morning I presented at the first ever EnterConf conference in Belfast, Ireland. The event “brings innovators who are building the business technologies of the 21st century together with IT leaders from the world’s largest and most-established companies.”

My keynote focused on the emerging area of enterprise data value. I began with a quote from a recent Big Data report by Capgemini and EMC.

Among our respondents, 63% consider that the monetization of data could eventually become as valuable to their organizations as their existing products and services.

In other words, companies that have long histories of selling products for revenue may well start generating more from data value than product value.

One starting point to getting your hands on data value is by converting your existing product to a “smart” product that generates data.

French tennis racket manufacturer Babolat is a great example. The company makes the Babolat Play racket that generates data as you play. According to Sports Techie magazine:

First introduced at the French Open in 2012, Babolat Play is tennis racket technology that collects a wealth of data about your personal tendencies and style as you compete on the court. Babolat Play rackets are the only rackets on the market that you actually turn on before you play. By flipping the switch on the bottom of the racket, you initiate the process of playing connected, meaning the racket will then monitor everything from shot power to endurance to stroke technique.

By creating a smart product, Babolat took the first step to data value.

Tennis is not the only sport using sensors. Golf, for example, already has a similar use case with products like SwingSmart.

What is less understood is the process by which enterprise companies can associate economic or business value to their data sets. A recent article by the Wall Street Journal highlights this struggle.

The good news is that Dr. Jim Short of the San Diego Supercomputer Center has been surveying the industry and finding data value use cases of interest. I shared Dr. Short’s recommendations (based on his research) with the audience:

  • Make data valuation explicit. Take today’s implicit processes that place an unsystematic value on data (e.g. retention, backup, etc.) and make them explicit.
  • Make data value part of your business strategy. Bring in or develop tools for valuation, develop data policies and services, acquire and/or sell data.
  • Manage your data value. Assess data risk and evaluate data insurance in the same way as other valuable corporate assets.
  • Make data value part of your IT strategy. Instrument your systems to support data valuation for both technical information and business information.
  • Data mine your IT infrastructure. Consider data mining your own data infrastructure to understand and communicate to the business how your data is (actually) organized/aligned with business value added activities and functions.

What’s my role in the research?  While Dr. Short examines the business processes for valuation, I am assessing the impact on IT architectures.  My exploration of data valuation IT frameworks to date can be found on the Information Playground.

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