Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 12: information determinacy and indeterminacy and the prediction of value 4

Posted in macroeconomics by Timothy Platt on February 11, 2014

This is my twelfth 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 1-11.)

I began a discussion of a pharmaceutical retain example of how predictive business intelligence works and its dynamics in Part 11, there focusing on the highly competitive and potentially highly profitable market for severe cold and flu non-prescription medications. I began discussing this from the seller’s perspective as pharmacies seek to keep the right volumes of the right seasonal products in stock at the right time. I then shifted orientation and looked into how this particular business challenge plays out from the non-prescription drug manufacturer’s perspective. And I ended that posting by raising the issue that businesses can and do often face competing sources and types of predictive business intelligence that they could close from and develop their own business strategy and operational execution from. And the competing presence of these information product offerings impact on the marketplace-realizable valuations of each other. My goal for this posting is to at least begin a discussion of that, continuing with my flu season retail pharmaceutical example as I do so. And I begin that with a simple, basic question. What types of predictive business intelligence are available that could arguably offer competitive value for this?

• One possible and I add obvious and widely available answer comes from sources like the United States Center for Disease Control and Prevention (CDC) and more globally from the World Health Organization (WHO) insofar as these organizations track and report incidence of communicable diseases, the flu included. But if every pharmacy and pharmacy chain taps into the same set of generally publically available predictive intelligence sources here, these sources cannot in and of themselves create new avenues to competitive advantage. That result, I add, is at least indirectly one of the core intentions for these information providing organizations, that they provide actionable insight that can be as widely found and used as possible for public health purposes.
• This brings me to consideration of additional sources of information that might not be as widely or as freely available but that would offer business intelligence value. In order to be worthwhile, they would have to offer sources of value not available through standardized CDC, WHO or similar low or no-cost reports. This could mean, among other considerations, their offering earlier and more timely reporting, more geographically localized reporting or more accurate and precise reporting and particularly over business-effective timeframes. The most important detail here is not in the specific additional features offered, but that their value to the purchasing business be such as to offset and exceed any additional expenses incurred from obtaining and using them.
• And I add in this context that some of the potentially most important new sources of business intelligence here are also going to be low or no-cost in nature too, in keeping with a pattern set by much of online information development and distribution. In this context, I have already recently discussed crowd sourced demographics data that is proving remarkably accurate for predicting emerging flu outbreaks as developed through Google, and the big data analysis of key word search patterns for words and expressions related to the flu and its treatment or prevention (see my first example presented in Big Data and the Assembly of Global Insight Out of Small Scale, Local and Micro-Local Data 1: assembling big pictures out of little pieces in three examples.)
• The more information and analysis is readily available free or at low cost and the higher its quality is, the more difficult it becomes for any business intelligence developer to offer a significantly priced for-fee alternative to it, that could be marketed as a source of defining competitive advantage to its clients. The readily achievable margin of additional competitive value that a for-fee information product can offer becomes narrower and more ephemeral.

And this adds in a new source of business intelligence uncertainty to this discussion with the issue of how much work and expense an information providing business has to invest into producing a cost effective information product that client businesses will want to buy, while still offering this at a price point they will pay – and in the face of low cost alternatives. With that competition, a business intelligence seller has to focus in their marketing on the specific issues of their product offering that would most clearly and convincingly create competitive advantage for their products’ buyers, and it would have to focus in that on what they offer that is distinctively unique when compared with alternatively sourced predictive information products.

And with this, I bring business intelligence assemblers and sellers to this discussion, along with pharmaceutical product manufacturer and retail seller business intelligence buyers. For small retail businesses with narrow profit margins from close competition, it is likely that any of this business intelligence gathered and used would come from those free and at most low-cost sources such as the CDC. Larger businesses (e.g. retail pharmacy chain store operations) would be more likely to have the liquidity available to seek out alternative, more directly competitively valuable business intelligence sources to better allocate their distribution of inventory purchased and held across their system of retail outlets and to improve their individual competitive positions in their local markets.

I began a discussion of open source and crowd sourced business intelligence in this posting and will continue my discussion of them in my next series installment. Meanwhile, you can find this and related postings at Macroeconomics and Business.

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