Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 9: information determinacy and indeterminacy and the prediction of value 1

Posted in macroeconomics by Timothy Platt on January 25, 2014

This is my ninth installment to a series in which I discuss and analyze the valuation of information in a business context and from a due diligence and risk remediation perspective, and in terms of what in a physical systems context would be called quantum indeterminacy (see Macroeconomics and Business, postings 137 and following for Parts 1-8.)

I have been discussing the valuation of less fully determinant business intelligence as it comes to market as a possible sellable product, from Part 5 of this series on, focusing on closely held proprietary trade secrets as a source of working examples. Then in Part 7 and Part 8 I began discussing how this valuation can be expected to shift in time, from a seller’s or at least a business intelligence relinquisher’s perspective in Part 7 and from a corresponding buyer’s perspective in Part 8. Think of this as shifting from taking a static modeling approach to taking a more dynamic, time dependent one.

I noted in that discussion that I have not made a corresponding shift from static to dynamic in my discussion of the sale and acquisition of more fully deterministic business intelligence such as the sales leads data of my Part 4 example. To round out this overall portion of this series and as a point of comparison to my discussion of how valuation would shift with time for business intelligence that was not fully content-predictable for valuation determination purposes, I pick up from where I left off with Part 4 and explicitly consider how the expected realizable value of a more fully deterministic business intelligence package would be expected to change in time, baring value-perturbing influence of outside factors.

An empirically accurate mathematical function that would describe the change in realizable market value for a closely held proprietary trade secret or other non-determinant business intelligence offering would be fundamentally nonlinear in nature. The rate of change function describing valuation over time for a fully deterministic business intelligence offering, would be expected to follow a linear model, and for a statistically large enough sample of such offerings, one similar in form to the mathematical model used to describe rate of radioactive decay over time when starting at some arbitrary t=0 starting point (with t=time.) Maximum value would occur when this information is freshest and newest and its realizable value would drop off steadily from there.

• This is actually quite important. Indeterminacy in and of itself, of necessity adds in fundamental nonlinearities to the valuation of business intelligence offerings.

With that discussion more fully in place now, I turn to more fully consider a second fundamental example of business intelligence valuation where indeterminacy comes into play, besides that of trade secret offerings that I have been looking into: the sale and purchase of predictive business intelligence. And I start that by more fully explaining precisely what I mean by “predictive” business intelligence here.

As a simple and very specialized case in point for that I could cite businesses that gather business intelligence and analyze if for patterns, both systematically:

• Describing and explaining business and marketplace activity up to now and at an underlying process and mechanism level, and
• As a means for predicting likely upcoming market conditions and market dynamics, and the performance of industry competition.

This insight would then be used as input for developing and refining strategic and operational plans and processes for businesses that purchase these reports. The former more descriptive part of this analysis would be used by the report-buying business to help it to more fully understand where it is now. The later more forward-looking, predictive part of these reports would be used to help plan ahead, and for the upcoming quarter and beyond.

But to put this in perspective, I want to at least begin by considering predictive business intelligence from a wider perspective than just that of this type of business information and analysis report.

• In a fundamental sense all business strategy is predictive
• And all information, however sourced that is brought to bear in developing ongoing strategy, and regardless of length of timeframe considered in that strategy, is predictive business intelligence.

I will in fact more fully discuss as a specific case in point, businesses that develop and sell predictive business intelligence as a product offering in this series. But I do so from the perspective of a very explicit awareness of this larger understanding as to what predictive business intelligence is per se. And for purposes of discussing valuation from a clean and at least somewhat more simple perspective, I allow for inclusion here of any putatively predictive business intelligence that meets some specific criteria:

• It has to be limited as to range of access. This can mean it is exclusively offered to, and if purchased will be available only to one buyer. That would make this product offering a source of unique insight for one participant in their industry and their overall market within it. Or it can be an offering where individual buyers can know in advance of any purchase decision, how widely this specific information offering will be shared in the marketplace, and whether any other potential buyers who could simultaneously purchase this are direct competitors or not. This is important as in an extreme alternative case, if everyone knows it, it cannot offer any particular competitive advantage value, and the more widely it is known among direct competitors, the more depleted its maximum potential value can be, from that.
• It has to be tagged as to how current its underlying evidentiary data is, that any report conclusions are drawn from.
• And buyers should have enough information on where that data was developed from and how it was selected and vetted so as to have a basis for estimating its reliability.
• If any of these criteria of buyer evaluation are essentially unknowable in advance of any purchase: information exclusivity of access, age, or intrinsic reliability, then risk management decisions would most likely render any report generated from this data to be effectively valueless.

I am going to further discuss predictive business intelligence, as a source of monetizable value in my next series installment, and will go on from there as noted above to more fully consider information determinacy and indeterminacy per se. Meanwhile, you can find this and related postings at Macroeconomics and Business.

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