Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 8: deterministic and stochastic valuation models and methodologies and their alternatives 4

Posted in macroeconomics by Timothy Platt on January 19, 2014

This is my eighth 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 1-7.)

I focused in Part 7 on the seller (in my specific example, the loser through theft)-side of proprietarily held trade secret business intelligence. And I stated at the end of that series installment that I would turn here to consider the buyer’s side of this next. I will do that, but will at least set the stage for this posting’s main line of discussion with some clarifying notes.

• My goal in discussing buyer and seller sides to the transfer of business intelligence is to more generally discuss valuation from the perspective of information holders who relinquish exclusive ownership of this information, voluntarily or not, and the perspective of the information acquirer.
• When a business plans out and intentionally sells off a proprietary trade secret, this is in many and even most cases going to take place in the context of a carefully executed strategically considered process, whereby the seller takes collateral action to limit any potential downside to themselves from loss of their exclusive ownership.
• And this same operational process will likely at least seek to maximize positive returns on this sale too, and through negotiations of sales terms, but also from redeveloping and remarketing the seller’s still ongoing products and services too, to best maintain competitive strength after it relinquishes sole ownership of this trade secret intelligence.
• This all holds potential to skew realized price asked for and paid, and realized fair market value observed, from that of a simple measure of the value of this trade secret business intelligence as if it were evaluated in a more neutral operational and strategic context.
• So I pursued a more unexpected transfer of business intelligence between parties as a simpler case, and as a perhaps cleaner example in evaluating and determining value.
• Here, I will specifically assume that the recipient is a legitimate buyer, purchasing a recognized value-creating trade secret from a legitimate seller in a marketplace.

I begin my discussion of the buyer side to these transactions by repeating a point that I have noted several times now in this series. A trade secret, like most all business intelligence, is intrinsically non-rivalrous. Specific legally binding contractual agreements can limit or block simultaneous use by separate holding parties, thus restricting at least open use of this information to one of them – a designated legal owner. But in principle, multiple and even unlimited numbers of copies of a set of business intelligence information could all simultaneously be used by an equal number of holding parties. So the realizable value of a business intelligence acquisition is going to be contingent on what the purchasing party can reasonably assume to be its return on investment from making this purchase, where it is assumed they will have exclusive use of it, and where that determination will set a range of possible values depending on precisely what is in the purchase package. But this valuation will also be influenced by and even at times significantly be shaped by risk management factors of exclusivity and of potential impact from functional loss of that.

• What could be realized short-term from the acquisition of a trade secret, to follow up on the type of business intelligence I am discussing in Part 7 and here? Short-term in this context, means before competitors and potential competitors can move in with market share grabbing product offering alternatives.
• What are the anticipated development and build-out costs that would have to be expended in order to remuneratively capitalize on holding this business intelligence? And what are the realistic ranges of possible costs here, where due diligence analysis would at least consider cost overrun scenarios.
• And how would costs and returns be expected to shift in time, and particularly if knock-off or other alternative product offerings were to arise and enter the market? I refer here to the Coca-Cola example that I discussed from an original trade secret holder’s perspective in Part 5 and then again in Part 7. Proliferation of competing product offerings impacts on the realized value of an original product version-defining trade secret, and from both the seller and the buyer perspectives.
• And following parallel reasoning to the timeline argument of Part 7 as presented via a Young Coca-Cola Company and a Mature Coca-Cola Company scenario, the value of a trade secret from a buyer’s perspective would be expected to follow a somewhat similar curve with time, to that of the seller’s expected valuation curve over time – with one crucial exception. The initial valuation that a prospective buyer would face when considering a trade secret acquisition would not in general start out as set at the initial, unknown product offering valuation of the Young Coke scenario unless this trade secret was in fact being bought early and before this trade secret had been effectively brought to market. The initial valuation that a prospective buyer would face in this acquisition would be based on the realized valuation and marketplace strength of this trade secret as a source of business revenue, as developed by the seller.
• This would not, I add mean the value to the seller and the value of this same trade secret to the buyer would be the same – just that the seller’s valuation would be a significant factor in shaping the buyer’s valuation and in determining whether this acquisition would make financial sense.
• Ideally, and from a buyer’s perspective, they would buy cheap on the basis of a due diligence evaluation that would indicate that they could make more effective use of this trade secret then the seller could. This might be because they have better production infrastructure in place, or better and more cost-effective distribution systems. This might be because they hold other proprietary information-based resources that this new acquisition could be developed and exploited in combination with, creating new profit making synergies. There are a lot of potential scenarios here. But the baseline starting point for these analyses and evaluations would be set by the seller’s realized valuation as determined by their business performance from using this trade secret themselves.
• If this were a trade secret that they held but that they had never developed or exploited in any way, this valuation baseline would be minimal. If, and to bring in another valuation confounding factor here, this was a trade secret that led to a product that was losing market strength from market saturation and loss of consumer interest, its valuation might be minimal too – unless this trade secret could be developed and exploited in a new and disruptively novel way by a buyer, in effect giving it new life.

Given the black box nature of trade secret business intelligence offerings, there is going to be a measure of indeterminacy in the valuation of this from both seller and buyer perspectives and for essentially any contingency of sale, acquisition or use that might be explored here.

I was initially planning on outlining here in this installment, how the valuation of business intelligence would shift with time when it follows a more deterministic model, as discussed in Part 5 and Part 6 of this series. My goal was to add that into this general discussion, as a point of comparison for to how valuation shifts in time for business intelligence where its contents are less determinant during any sales process or for due diligence analyses on buyer or seller sides leading up to that. I will, instead discuss that in a next series installment, where I will more fully discuss information determinacy and indeterminacy per se. I will do that at least in part in terms of the sale, purchase and use of predictive business intelligence as a very real-world working example. Meanwhile, you can find this and related postings at Macroeconomics and Business.


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