Information & Decision Making

Decisions are based on information and generate information.

Information and decision-making are intimately connected and interdependent. Information as input to the decision-making does not absolutely determine the decision but allows the decision-maker to exercise their judgment. This is exactly what is required for the decision to carry effective information. What are more correlations between information and decision-making?

A purpose of analytics is to optimize decision making. The analysts optimize decision making by obtaining useful data, more precisely, those data are reliable, can be trusted, and relevant, it applies to the situation at hand. It means to "simplify data" in the sense of dimensionality reduction, and it is, at least, one of the ways to create value and for decision-making in particular. The data is as useful as much relevant information can be derived from it. Reliability, accuracy, integrity -these are attributes/properties of data. And these are the characteristics to shape the quality of data.

Decisions are based on information and generate information. The amount of data required for a decision, and the amount of information generated by a decision, can both be measured in bits & bytes. Data endorses action and knowledge determine and shapes action, information may not affect action in any direct manner, instead, it conditions action which makes it key to decision-making/choice, planning, and action ultimately.

Information and decision-making are intimately connected and interdependent. For decision-making to be effective, the decision-maker must have enough knowledge to make their decisions rich in information and significantly different from the available data. Although information can't always be knowledge; and knowledge doesn't always inform you... While information is an ingredient of knowledge; but in order to interpret information; you need knowledge.

Information is created at the stage of problem-framing. No information can ever be generated without agents. Information has something to do with products of rectification and ramifications by individual agents. The critical phase when information is created is when agents/decision-makers frame problems, or more exactly when they DISCOVER problem structures, the rest of the time they generate only either new/redundant data and/or knowledge updates. One person’s knowledge is the other one’s information only; what information is for one person, it is knowledge for another.

The interesting metric is the relative entropy between the input data and the decision result.  (otherwise known as the Kullback–Leibler divergence). Obviously, if a decision-maker just passes a message on without influencing it in any way, there is no effective information provided, regardless of the amount of information in the message. Useful decision-making occurs when the decision results are different from the input data, which is why effective information is measured as a divergence.

Knowledge is information that supports the decision-making process. Information is situated between data and knowledge. Knowledge is information in use; knowledge is an expression of understanding relating information and experience accumulated over time, it is about known facts and past events. Both data and knowledge are being convergent in terms what action ensues (for example in analytics), whereas information is divergent as the decision-making process essentially is, and knowledge is where the cultural and social context alignment (or misalignment) with the information that precedes it. And ultimately it’s knowledge that supports the decision-making.

Information –Insight – Decision making: Information allows you to build an actionable insight as how to move from one level to the other. It applies to the context and environment in which decisions are made. Information, inclusive of data, as input is the primary driver of decisions when it applies to automated systems, not human beings. In the human context, information drives awareness, which can include all of these characteristics, uncertainty, surprise, difficulty and entropy, although it can also trigger a sense of confidence, confirmation, validation, verification... Once you get to human beings, models become poor approximations for how they respond to inputs of all sorts, although they are still useful in a general sense.

Properly done data analytics will give you information that you wouldn't have otherwise, and it should optimize decision making. However, decision making is both science and art, it takes both data analytics and intuition in order to make effective decisions; information and analytics are important, but do not forget common sense.