Data, Information, Knowledge and Wisdom

In the late 1960s a Harvard English professor, James Moffett, wrote a book by the title Teaching the Universe of Discourse. In it he classified four perspectives that one can take to any system. These perspectives were:

Observing: What is happening

Reporting: What happened

Generalizing: What happens

Theorizing: What may happen

The products of the perspectives have been popularly termed:

  1. Data
  2. Information
  3. Knowledge
  4. Wisdom

Understanding these four perspectives and their deliverables give us an understanding of the capabilities that each affords.

Data is the set of variables that represent any state of a system such as a business. Each time something changes in a system its state changes. It is crucial to know what the state changes are and how to measure them. In science, these are known as variables.  In databases these are known as instances.  It can also be termed “polation”.

Information is the collection of data as a system’s states change. This permits the comparison of each state. In science, these are known as the results.  In databases these are represented as tables.  It can also be termed “intrapolation.”

Knowledge is the ability to generalize what a system’s states will be given particular data and information. In science, this is known as hypothesis.  In database parlance these are known as relationships.  It can also be called “interpolation”.

Wisdom is the ability to turn exceptions into advantage—states that are missed by current data, information and knowledge. It is necessary to compare many states of one form of information to many states of another form.  The result is used for projection into the future. In science, this is known as new theory. Database modelers treat this as a many to many relationship and resolve it as an association.  It is also known as “extrapolation”.

I want to distinguish the model for a venture from the model of a business. One of the key obstacles to innovation is communicating a venture model to minds grown comfortable with a business model. First, the people we are trying to convince may not even know there is an exception. Second, the new data, information, knowledge and wisdom we advocate are all untested. We do not know if the data we advocate measures the new states. We do not know if the information will make comparison of all states possible. We do not know if our knowledge will successfully forecast the benefits of the new states. We do not know if our wisdom will be able to handle new exceptions spawned by the venture. No matter how much you analyze, ultimately the experiment has to be performed. As in science, the scale of the experiment dictates the risk.


The Classification of Exceptions

Exceptions in a system are not a one dimensional phenomena, however for the first part of this discussion I am only going to concentrate on a single dimension. This dimension has the following six categories:

  1. Conceptual
  2. Contextual
  3. Logical
  4. Physical
  5. Mechanical
  6. Operational

These categories descend from largest, conceptual, to smallest, operational.

Conceptual exceptions involve the failure of the business model to recognize an achievable objective. Simply put, something new can be done. A contextual exception is the failure of the business model to recognize a consumer context it can support. It can be done and it can be done for the customer base chosen. A logical exception is a failure of the business model to recognize a product or service the customer requires consistently. A physical exception is a failure of the business model to recognize an opportunity to deliver to the customer at a lower cost. A mechanical exception is not seeing how to correct a defect in the manufacture of a batch of product or in the training in the procedures of a service. An operational exception is correcting a defect in a single use of a product or service.

Dealing with exceptions in only the context of product and service is actually in many instances insufficient. There are several more facets to any business and any system. For example

  • Product

  • Service

  • Employee

  • Location

  • Timing

  • Need

When we look at these facets and consider them in combination with the dimension we have already discussed, suddenly a venture becomes increasingly complex. The conceptual category now begs the questions: Is the product possible? Is the service possible? Can we find the employees to make it possible? Is there a location where it is possible? Is there an appropriate time for this venture? Is there a need for the product or service of this venture? And there are still five more categories unexplored.

What we have with these two dimensions are thirty-six different types of exceptions a business model can have.

The Dip Slips

In his new book, The Dip, “marketing guru” Seth Godin attempts to use the Pareto principle to claim that the first 20 percent of the work derives 80 percent of the benefit. I think he has missed the mark entirely.

If we look at the exceptions I have detailed above in the context of the Pareto principle we can see that the first four categories, which comprise eighty percent of the work, provide only twenty percent of the benefit. The first four categories are design categories. The last two categories are development and use. It is the Mechanical and Operational categories that deliver eighty percent of the profit. Put another way, “Measure twice, cut once.” Seth Godin, has logically put the cart before the horse.

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