The road to company wisdom, data-information-knowledge-wisdom

A top-down information centric roadmap as a very condensed executive summary


Terminology: A wise company needs to master two things: let us call them: (1) Science and (2) Engineering. (1): Science is simply the art of predicting the future. Any method with predictive capabilities is science and any method without is not. Whether we predict if a bridge will collapse in the future or if our customers will be happier using our products or if it will rain tomorrow - it is science. Luckily prediction and therefore science has recently become “a solved problem”: Surprisingly, it turns out that science is equivalent to data compression[1]or formal use of occams razor, and there are today well-defined formal methods for creating knowledge and performing true science even with completely automated methods. With this view: Science is also the process of creating knowledge, see below. 2): Engineering (or Design or Optimisation)[2] is the art of creating something of optimal value given constrained resources. Anything requiring a plan towards a defined goal is engineering. Usually this activity is much less studied and left to ad-hoc procedures despite the fact there is a growing body of theoretical knowledge to allow for a more systematic approaches to optimal planning and decision-making. A plan containing decisions is an algorithm which is the highest form for representation of knowledge and wisdom. Optimal decisions require an optimal algorithm/plan for the desired goal. Within a company such algorithms and procedures are usually called processes. Systematic and theoretically founded methods for engineering exist but are too little known.I will tie the rest of this introduction to my version of the Data-Information-Knowledge-Wisdom framework. In this context: Data is any fact (numbers, text, symbols) that is or can be written on paper stemming from events in the world. Another way to say the same thing is that: data is digital symbols produced by analogue observations in the universe. Information is data with a context. Data with attached information on how and when it was collected and its relationship to other data is information. Knowledge is understood information. Understanding information means finding patterns and redundancies in the information that allows for the information to be compressed and represented in a shorter form than the original representation. This shorter form of the information is called knowledge and can be used to predict the future. Producing knowledge is equivalent to science and is done by compressing information. A toy example: The time series “2, 3, 5, 7, 11,13,17,19,23,27,29” is equivalent to “first 11 prime numbers” or “first 11 primes” the latter quote is a shorter representation than the original data series. In other words “2, 3, 5, 7, 11,13,17,19,23,27,29” can be understood as “first 11 primes” and we have created knowledge that can be used for prediction, for instance by calculating the continuation as the “12th prime”. Wisdom is just knowledge tied to value. There is a lot of knowledge without value: I have just calculated the number of leaves on the oak tree outside my window: there are 7236 leaves on the tree. This is knowledge, because it is a condensed symbolic representation of a lot of data from my senses and information in my head. The value of this knowledge is, however, questionable. This knowledge is therefore unlikely to go into my pool of wisdom. The wisdom of the plan to spend time calculating them is also questionable since the only value that came of it is the use as a negative example in a text about wisdom. This could just as easily be achieved with a faked number giving the same value but with less consumption of the constrained resource of time. This would have been a better-engineered plan…In order to know the value of my plan and my knowledge I need to have well-defined and measurable goals and objectives. “A cynic is a man who knows the price of everything but the value of nothing”, according to Oscar Wilde, and we must be careful to set our goals to incorporate all forms of value not just a few easily calculated short-sighted sums of money. The value to be optimised by our overall company algorithm could be the well-being and satisfaction of customers using our products together with the well-being and satisfaction of our employees provided that we can make these values quantifiable and measurable. Process of creating Wisdom is also Wisdom. Wisdom is a self-introspective process required to create a learning organisation. In formal terms: the algorithm of wisdom is recursive and requires application of Wisdom to analysis of our history as past events and decisions. Formally optimal wisdom is achieved thru applications of science to history, provided that history contains data and information on enough events and decision tied to value at each point. Wisdom in this sense is studied within e.g. reinforcement learning, game theory, and the emerging field of general artificial intelligence. An optimal wisdom method predicts the best imminent action based on compression of history including value and produces an optimal algorithmic plan for the future. Tomorrow follows a number of rules to obtain a wise successful company in the information age:



[1] See e.g. http://www.idsia.ch/~juergen/speedprior/
[2] There is really no good cross-disciplinary word with the same connotations and associations, and every label opens up to misunderstandings, but let’s stick to engineering in this document. The theoretical foundation of the concept is, regardless of term, firm.