Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 4: unique and one-off business intelligence offerings

Posted in macroeconomics by Timothy Platt on December 24, 2013

This is my fourth 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 1-3.)

I focused in Part 3 on the valuation and fair market price determination of standard content and format business intelligence such as sales leads. I turn here to more specifically consider one-off and novel business intelligence offerings, and their valuation. Examples of this can include business intelligence such as:

• Proprietary trade secret information (e.g. secret recipes or formulations that are kept in-house rather than being publically patent protected with the risks that entails),
• Proprietary in-house information as to strategic plans for a business moving forward,
• New product or product update information which can including design details, release date information or any other related information where loss of exclusivity would be expected to lead to loss of competitive position or direct financial loss.
• Confidential information as to proposed, anticipated or pending business expansions, contractions, or shifts in priority, or of impending mergers.

I have, of course, only touched on a few possibilities here out of what could be a much more lengthy list. But my basic point should be clear from these four bullet points. Businesses often and even usually hold significant amounts of business intelligence in-house, that even if of standard forms (e.g. generically identifiable as trade secret information), are still primarily unique and one-off as to content and other details, for any possible marketplace consideration.

• Standard business intelligence is interchangeable, like-for-like, and the more standardized and similar for form two or more packets of business intelligence are, the easier it is to operationally specify what an acceptable like-for-like would mean for any given marketplace context.

Turning back to my auto sales leads example from Part 3, if a leads provider does its due diligence and only offers:

• Exclusive access sales leads to dealerships that it sells them to,
• And leads for potential buyers that it has found to have at least threshold-acceptable credit scores,
• And who have shown interest in purchasing a new car from visiting appropriate web sites that sell this information to them as they assemble their leads,
• And these prospective buyers are from the right geographic region that a dealer would sell to

then it does not matter all that much which specific names and contact information are included in any given lead – only that possible specific leads offer similar-range sales-prospect values to the dealership that buys them.

• The more clearly a standard for like-for-like comparison can be made here, and the more fully the factors and variables included in determining that comparison can be seen to collectively account for realizable value obtainable from that information, the more replicably standardizable a fair market value determination can be made for it.

And this brings me specifically to the challenge of one-off and unique business intelligence and its valuation determination. For truly unique information resources, there are no valid benchmarks that can be turned to for establishing a like-for-like comparison, where historical and even recent, same-market historical data could be cited for comparable-product offering pricing. This, among other things widens out the range of possible valuations reached and by both potential buyers and sellers. This adds in significant valuation uncertainty, and to cite my black box model approach of Part 2, valuation indeterminacy too.

This still leaves valuation criteria that are developed internally to the information owning business and from the impact that their holding this information has had in creating their competitive position in their marketplace. So for example, the fair market value of a closely held trade secret formula or product creating process can be set at least in significant part by analysis of the realized market value actually achieved from holding and using this information. But while this approach might hold for novel information like trade secrets where a valuation can be directly measured through realized financial impact, this would not apply as reliably for closely held information that has not been directly tested against marketplace activity (e.g. information about a planned business restructuring where outcomes might be predicted but with significantly higher levels of overall-outcomes uncertainty.)

I am going to continue this discussion in a next series installment where I will more specifically discuss deterministic and stochastic valuation models and methodologies. Meanwhile, you can find this and related postings at Macroeconomics and Business.


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