Platt Perspective on Business and Technology

Business intelligence as a qualitative distinction – a requirement for effective rules of monetization

Posted in macroeconomics by Timothy Platt on August 22, 2010

I have been posting on the topic of information as a monetizable asset and source of value in several places in this blog, and particularly in:

Macroeconomics and Business,
Reexamining the Fundamentals, and
Ubiquitous Computing and Communications – everywhere all the time

and have been developing a case for the argument that we need better, more consistent, more comprehensively inclusive ways to determine the monetizable value of business intelligence if “information economy” is to hold more meaning than simple buzz word. I have looked into the non-rivalrous nature of information (see for example Monetizing and Setting Valuations on Information – the crucial question) and have at least briefly outlined some of the consequences of that in determining information value, and have touched upon several other areas of this general topic as well.

I want to pick up on this thread with this posting and will focus on two points:

• Along with being non-rivalrous, information carries value in ways that are context specific.

In that, information is much like rivalrous goods and services. A bottle of fresh water holds a very different value to someone who is parched with thirst and lost in a desert than it would for that same person three days after rescue when they have a big glass of water in their hand already and are not even currently thirsty to begin with. A winning lottery number combination holds different value the day before the drawing than it does three days after that event.

• But not all information is the same and even at a very basic level as to type, and even when only business intelligence information is considered – there are some important categorical distinctions as to origin and range of potential application for business intelligence that need to be taken into account in determining its mode of valuation. And differences as to type of business intelligence (as measured according to these criteria) can and do have impact on how its value changes with context.

There are, of course, a great many ways to parse business intelligence as to type and category so to set a foundation for my approach here, I stipulate that any effective approach has to offer realizable, enumerable value to businesses, and from facilitating planning and strategy, and in setting priorities. More particularly, it has to offer this value not just to individual stand-alone organizations, but also to organizations as they interact, cooperate and compete in a complex changing business ecosystem. This has to make practical sense for businesses as they enter into and work in complex supply chain and value chain systems (see Supply Chains and Value Chains as Drivers of Sustaining Value.)

At least tentatively I would propose three basic categories of business intelligence for consideration in this context:

Customer and client information – This includes personally identifiable information of a type that would be protected under privacy protection law for individuals, and other individual level, non-anonymous data developed for specific people that is not so protected and restricted. This would also hold client and customer relationship data from and specifically relevant to individual business clients. As such this includes any middle ground cases such as the single person consulting firm as a client. This is all data coming in from the surrounding marketplace community concerning specific individuals and businesses – specific entities that make purchasing decisions there as customers and potential customers.
Operational information – This information collectively forms the basis for operational best practices within specific organizations, and as shared (with or without contractual limitations as to allowed usage) with supply and value chain partners.
Product and service information – This is another seeming catchall category insofar as it includes patented and trade secret information, open source and public domain information and all else that specifically and significantly goes into the design and/or production of monetizable goods or services, and that contributes to their valuation for its inclusion.

So I seek to partition business information into three buckets, one focusing on the customer, a second on the business and its operations as it works to meet customer needs, and the third focusing on the products and services that these customers would receive if they decide to enter into business transactions with you.

I would argue that each of these basic categories would include data that is at least in principle directly fungible in its own right and that can be priced in the marketplace as having monetizable value. I would also argue that collectively these three basic categories include most if not all raw business intelligence data and for both individual organizations and for organizations as they function in supply chains and value chains, and in business ecosystems.

My goal in this posting is simply to outline a basic qualitative model of business intelligence. I will elaborate a bit on that in a continuation posting, but in that I will also look into quantitative considerations as to identifiable value and how this might be determined.

One Response

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  1. Santo Home said, on August 22, 2010 at 8:22 pm

    nice articles

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