Platt Perspective on Business and Technology

Mining and repurposing of raw data into new types of knowledge – 5

Posted in business and convergent technologies, macroeconomics by Timothy Platt on March 14, 2012

This is my fifth installment in a series on repurposing accumulated stores of raw data and of processed knowledge, and of the potential – and potential pitfalls of commoditizing and marketing this as information product (see Macroeconomics and Business, postings 50, 53, 55 and 57 for parts 1-4.) So far I have discussed the sale and brokering of access to business information and the issues of risk as faced by the three basic participants that can enter into these transactions: original data sources, access buyers, and access sellers. And I have discussed factors that would determine maximum realized value for this information based on marketplace-based need and willingness to buy, and on marketplace and participant-based risk.

I stated at the end of Part 4 that one industry that would find particular interest and value in having a consistent, standardized framework in place for the valuation of information is the insurance industry and that is a very valid point. Insurance companies use and need valuation and amortization tables for the monetary value of all of the rivalrous goods and property that they cover in their policies. They use this standardized valuation framework both in developing insurance policies and their terms of coverage, and when setting premium rates and determining payouts on claims. Removing ad hoc from the valuation of information would permit them a more standardized system for all of these processes when covering business intelligence and accumulated information value with insurance policies too. And that would reduce risk to the insurance companies themselves as well as offering value to their clients.

I added a twist to this for insurance companies at the end of Part 4 of this series when I raised the possibility that they might seek to tap into the potential marketable value of some of the business intelligence they accumulate, and both about the marketplace itself and as obtained from client and other individual sources.

• The more readily and even ubiquitously available information per se becomes, the more valuable vetted information that can create competitive advantage becomes.
• In this, an information repurposing business, serving as a broker can be seen as a source of competitive advantage to the degree that it sifts and filters, and qualifies more specifically pertinent and useful information from the general flow of what is out there, and as it adds to this from more proprietary sources.
• And an insurance company would be in a position to leverage further marketable value from the information processing and analysis that it conducts anyway in the conduct of its primary business as an insurance company.
• But as I noted earlier, this creates potential conflicts of interest.

An insurance company that trades in business intelligence that is at least in part grounded in data obtained from its customers runs the risk of increasing risk to those same customers, and in ways that they have not agreed to accept.

• First, in accordance with the axiomatic model of risk-based valuation developed in Part 4, this would decrease the maximum attainable value that this information might hold and certainly for the insurance company.
• When the maximum monetary gain even potentially achievable from brokering and selling access to this information drops below a threshold set by their overall business performance and marketplace tolerance for added risk, this additional line of business ceases to add positive value and becomes a liability in fact.

As a matter of basic practice, any business – insurance companies definitely included, that collects customer and other personally identifiable information should firewall off any side business in which they repurpose and sell access to that information. Where possible any such information should be rendered anonymous as to specific source to limit risk to source (and commensurate reduction in information value from that.) And such raw data as would go into this business intelligence pool should always be gathered based on a strict opt-in policy before it can even be considered for mixing into the repurposing data pool and well before any effort is made to sell access to it.

I have written this in terms of insurance companies but note there is a second business model and industry that is already actively brokering access to client, customer and member data and largely without safeguards – and all too frequently without adequate regard to risk to the three participants: social networking and social media sites. All of the points I have been raising in an insurance company context up to now apply with full force when a site such as Facebook or LinkedIn seeks to monetize and sell access to information gathered from member profiles, from their online content, and from the exchanges and interactions their members have on their sites with others.

• Facebook in particular, has come under fire for perceived breaches of confidentiality in the way it manages and manages access to its members’ information.
• A standardized model framework for determining risk and valuation of this information, taking the business processes followed here out of the ad hoc would most likely reduce their risk exposure – as they would have a clearer understanding in advance as to precisely what risk and valuation issues were going to be in play. Quite simply, they would have a clearer understanding as to what to do and what not to even try doing with the information they hold, and why and with that why quantified.

I am going to turn next to a need for standardized safeguards in the collection, mixing, processing and sale of access to information by data repurposing businesses and brokers. Meanwhile, you can find this posting and series at Ubiquitous Computing and Communications – everywhere all the time and at Macroeconomics and Business. I specifically point out as directly applicable background reading Business Intelligence as a Qualitative Distinction – a requirement for effective rules of monetization and my nine part series: Intelligence as a Quantitative Distinction (see Macroeconomics and Business, postings 21 and 23-30.)

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