Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 2: setting upper and lower limits as a due diligence exercise 1

Posted in macroeconomics by Timothy Platt on December 11, 2013

This is my second 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 Part 1: setting out the basic parameters.)

In this, business information valuation operates in at least some crucial regards like a black box system where you can observe, measure and track at least some input and output parameters with significant certainty, but where you do not necessarily have the information to quantify the values in all of the intermediate steps between them, at least unless you open the box – where in this context that would mean offering the information in question for sale in an open market, and through a bidding or similar process where price point reached would be set by the marketplace. Here, your starting input is what you have to offer and your calculated in-house valuation of it based on value that you have derived from holding this information and with whatever level of exclusivity that you have held it with, and the ultimate output value is what you can actually get for it in an open market as a received sales price net of sales costs incurred.

I just made a fundamental assumption there, that an information product’s true marketable value would be set entirely on the outside of the owning business, and by what the market on its own would bear for it. And I at least implicitly assumed that a given business information product offering would see a valuation that falls within one price determination range. I stated at the end of Part 1 that my goal for this installment is to at least begin a more detailed discussion of the statistically modeled valuation ranges that arise in this type of transaction context and in risk and benefits management terms. I begin that here by noting that a given potentially marketable body of business intelligence might very well present itself as simultaneously having two or more potential valuation ranges, depending on the observer.

To explicitly bring this back to Part 1 and consideration of how this business activity follows a quantum indeterminacy model, I note here that I cited a specific Gedankenexperiment (or thought experiment): the Schrödinger’s cat experiment in Part 1 as an example of how quantum indeterminacy can empirically arise and even on a directly observably macroscopic scale. Think of information valuation in this context as playing the role of that cat, but where its valuation can take any value determination point along a continuous range, and where in this case more than one observer can independently and simultaneously open the box and arrive at different but justifiable valuations on the same data at the same time. (Note that I have just made another fundamental assumption there, that different observers – e.g. two prospective buyers are in fact actually looking at the same information here when they open this same box. I will look into that a bit farther along in this discussion. But first, let’s consider valuation ranges per se.)

• The less firmly and consistently established a marketplace-acceptable mechanism for determining consistent valuation of a product offering, the more significantly do uncertainty and risk remediation determine what prices are offered and what of them would be accepted for it.
• And this uncertainty leads to wider potential valuation ranges,
• And buyer/seller asymmetries in perceived value lead to divergence in where they would set acceptable valuation range boundaries.

And this brings me to the peculiar seeming notion that two buyers might in fact open that same box but find what amounts to different information in it.

• Information carries meaning and value that is set in large part by the context that it is considered in.

If two potential buyers approach a same possible proprietary information acquisition and one sees this as a routine business intelligence gathering purchase, but the other sees this as a crucial acquisition that will enable them to move ahead on a major initiative that holds promise for significant returns, these two potential buyers will see this seemingly same new information very differently and value it accordingly.

To take at least some of this discussion up to here at least somewhat out of the abstract, consider the valuation of a rivalrous sales offering such as a desktop computer, to the valuation determination of the business-specific information stored in it.

• Standard depreciation tables that track the amortization of precise value in a commodity such as a used desktop computer of a specific make and model and with specific features (e.g. a specific amount of DRAM memory and a specific size hard drive) are well established, and different evaluators would likely reach essentially the same market value determination for goods of this sort when independently performing this type of evaluation and for the same marketplace.
• Two different potential buyer/evaluators might see the information on this same computer very differently. One, in fact might simply want the hardware and seek to buy it with an intention to reformat its hard drive and permanently delete its contents – while the other would purchase that computer strictly so they could access and use its information contents and actually be purchasing its information held, with the hardware only included as its envelope.

This point is important; a marketable commodity such as a computer or a standard software package can be said to hold intrinsic valuation in and of itself, because there are such standardized and consistently replicable mechanisms in place for determining fair market value. Business intelligence, on the other hand, is evaluated on the basis of both intrinsic, and context-derived extrinsically determined valuation factors. And there is much less of a consistent valuation standard or mechanism in place for it when it is brought to market.

I am going to continue this discussion in a next series installment where I will look at circumstances and mechanisms where a more standardized valuation can in effect be carved out from the overall problem of determining fair market prices for business intelligence as a marketable product. Meanwhile, you can find this and related postings at Macroeconomics and Business.

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