Platt Perspective on Business and Technology

Business intelligence as a quantitative distinction 2 – the cost cycle and information

Posted in macroeconomics by Timothy Platt on September 10, 2010

I raised two questions in my most recent posting on the monetization of information in Business Intelligence as a Quantitative Distinction 1 – framing some fundamental questions:

1. What shared, enumerable quality do these seemingly non-overlapping and distinct sources of valuation hold in common that underlies their seeming apparent differences? (N.B. here referring to rivalrous and non-rivalrous goods)
2. And beyond that how can this type of enumerable overarching valuation be consistently and unequivocally determined so as to connect information value into the current rivalrous goods and products marketplace?

Cost-effective market price for both has to be arrived at as a minimax solution balancing off two sets of factors. One, to focus initially on information and non-rivalrous goods, is the total cost for developing information resources and for providing them and the other is realizable value that access to this information can bring. When these values converge, an acceptable price point becomes available for this information in the marketplace. Here effective pricing sets to minimize that this information either become a loss leader or loosing proposition to the seller and therefore unavailable in the market (loss), and maximizing the likelihood that a mutually satisfactory price point be reached for it (win).

Note that this does not describe a simple zero-sum game scenario, but rather describes a more general game theory process in which decisions are made in the presence of uncertainty and where all parties could conceivably win or loose.

My focus in this posting is on the cost cycle side of this process and I start out by noting that there are a series of cost and uncertainty generating factors that have to be taken into account and by both sides of any monetized information transaction. Counterpart processes for rivalrous goods also carry levels of uncertainty as well and both can probably best be described as stochastic systems, but markets for rivalrous goods are set up to damp down the apparent uncertainty and price point fluctuations that this type of system would generate. Information and non-rivalrous goods are harder to monetize according to this conceptual model because it is a lot more difficult to do that with consistency across marketplaces for them. It is harder to damp down significant uncertainty across costs for them.

What are the major, readily predictable sources of cost and valuation uncertainty in the cost cycle for information as a commodity? I will focus on that in my next posting in this series.

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