Platt Perspective on Business and Technology

Business intelligence as a quantitative distinction 3 – the cost cycle and information (cont.)

Posted in macroeconomics by Timothy Platt on September 18, 2010

What are the major, readily predictable sources of cost and valuation uncertainty in the cost cycle for information as a commodity? I finished my last posting on the monetization of information with this question, stating at the time that I would pick up on this with the next series posting. A quick answer would be that there are in fact a wide range of potential costs associated with information, each cost point in the cycle carrying with it a measure of uncertainty. And these costs and uncertainties go to both seller and buyer.

• On the seller side, you have to include processes for validating and vetting data and of the remaining levels of uncertainty there, as to current accuracy, and even if the raw data was completely accurate when initially collected.
• Raw data in and of itself can be of limited value. Much of the value of information comes from organizing and developing it into more directly applicable knowledge. It should be noted here that knowledge as processed and filtered data is always focused towards addressing specific questions of importance to the business in meetings its goals. So organized data-based knowledge may hold different levels of value to different people, and to different organizations. Value increases as information becomes increasingly easily and directly applicable to the strategic goals and priorities and the processes of the buyer. This involves buyer-side costs and uncertainties as well as seller-side.
• Processing, storage and retrieval and further processing all add to costs as information management overhead and this applies to both sides.
• And the wild card of uniqueness or commonality of availability of this data and processed knowledge by others comes up too. As a extreme case open standards data and knowledge increase in value as they are more widely disseminated, known and utilized, where any of a wide range of market data might loose value and quickly as more people and organizations know it as its capacity to create unique market value can quickly dissipate from lack of exclusivity.
• As a proposition I would suggest that data and knowledge that gain value from open distribution retain value much longer than do data and knowledge that require exclusivity to be of value. Much of that distinction comes from increasing likelihood of this more exclusive data and knowledge getting out.
• Trade secret information in a way validates this point and even for companies like Coca Cola with their secret beverage formulas that they manage to keep secret from any and all competitors. It can, after all, be expected that others will come up with increasing numbers of competitive products in attempts to capture market share and profits from the leading and first mover trade secret holders. And even with closely guarded proprietary knowledge held secret and without breech of confidentiality, some of these alternative products will compete effectively and take market share.

A rivalrous commodity such as the computer I am writing this on is much easier to value as costs associated with it are much simpler, including adjustments downward of its value with age according to fairly standardized tables and formulas. Perhaps more to the point, the value of the computer I use is going to be more standardized and constant at any given time, independent of who a perspective buyer might be while the value of information on it (if any value for that) would be more buyer-dependent.

By that I mean dependent on factors such as:

• Their capability to convert acquired data and knowledge into validated forms they can directly apply to meet their needs, and
• The time frame in which this data and knowledge will retain competitive value for them.

I have not even mentioned a set of factors that can and does affect both cost and uncertainty considerations – legally mandated confidentiality requirements where it may be legal and of value for a business to hold and use certain information (e.g. customer personal information) but risky and costly for them to provide to it anyone else. And requirements, restrictions and guidelines on this change all the time and can and do vary considerably depending on location and for both buyer and seller, and also for original data sources.

Together, this all sounds like the start of a good argument against being able to readily and uniformly monetize business intelligence. The issues I find myself thinking about here involve finding ways to standardize as many forms of cost and uncertainty that go into valuating information and determining price points for it as possible and for both buyer and seller. Any effective approach to that would in fact lay out the basis for a standardized information economy per se where, for example an audit of the valuation of a business would include a direct and consistently arrived at valuation for its business intelligence too. I will be writing more on that in subsequent posting in my series on Macroeconomics and Business.

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