Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 1: setting out the basic parameters

Posted in macroeconomics by Timothy Platt on December 4, 2013

I have written on an occasional and recurring basis on the valuation of information in this blog, and particularly in its Macroeconomics and Business directory, and in that regard and as background for this posting cite:

Business Intelligence as a Qualitative Distinction – a requirement for effective rules of monetization,
• My nine part series: Business Intelligence as a Quantitative Distinction (see postings 21 and following in the above cited Macroeconomics and Business directory), and
• My two part series: Depreciation of Value in Non-Rivalrous Goods and the Business Intelligence Life Cycle – 1 and its Part 2 continuation.

I have written more on this topic but note these postings as relevant background for this one, where I seek to at least begin a discussion of information valuation as a due diligence and risk limitation and remediation exercise.

• There are circumstances where it is necessary to arrive at more precise valuation of business intelligence held. That certainly applies where this information would be brought to market, or where a legitimate proprietary information owning business seeks legal recourse and recovery in the face of illegal and unauthorized use of their confidential information through, for example, cybercrime.
• But when this information is simply being kept and maintained and used in-house, precise current numerical monetary valuations for it are not as pressingly important.
• Under these circumstances, the key parameters in play for setting an appropriate valuation range would be based upon a combination of factors, including:
• Legitimate and authorized usability and access and the value that can be developed through that,
• Confidentiality protection and limitation of unauthorized access and use, and costs accruing from maintaining that system,
• And the actual loss of value and the direct cost that would come from violation of the access and usage controls in place. The key parameters in all of this this reside in the value that is realizable from holding and using this information in-house and from preserving access and usage control over it, where loss of such control would constitute a loss of competitive strength or advantage.
• And this makes the storage, access and use and ongoing maintenance of this information resource a due diligence and risk management enterprise, and risk management an essential foundation point for the valuation of that information. (I will come back to this to more fully explain it and simply state this point of observation here as an indication of where this overall discussion is headed.)
• Precise numerical valuations are not as immediately important in this as are establishment of effectively and meaningfully defined valuation range parameters, and my goal for this discussion is to expand upon and discuss this point and what it means, too.

And I begin that, and addressing the perspective raised in those two last bullet points by citing what I would argue to be a homologous phenomenon from physics: quantum indeterminacy. There are physical systems where it is not possible to predict in advance of observation, their precise state or configuration. A Gedankenexperiment, or thought experiment as they are also called, known as the Schrödinger’s cat experiment comes immediately to mind, where in that case you cannot even know if the cat is alive or dead until you look – and in a quantum mechanical context the act of observing can directly bring about the outcome that is observed. In a business finance and economic system, it is not going to possible to fully know the true valuation of a package of information until you look, and by credibly offering it in a marketplace so potential buyers can share with you their market-driven evaluations of its worth to them.

Exact finalized valuations and prices agreed to cannot in general be fully deterministically predicted in advance and certainly where bidding or price negotiations are allowed for. But even then it should be possible to establish ranges that any such valuation would fit into at any given time and place and for any given general category at least, of potential buyers. In a fundamental sense, and when this indeterminacy is allowed for, it should be possible to statistically set ranges and likely deviations from a seller-expected or desired median fair market value.

I am going to continue this discussion in a follow-up posting where I will more fully examine this statistically modeled range in due diligence and risk and benefits management terms. Meanwhile, you can find this and related postings at Macroeconomics and Business.


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