Analytics Strategist

September 25, 2007

Decision Theory and Data Analytics

Filed under: Datarology — Tags: , , — Huayin Wang @ 7:56 pm

Data analytics is the core technology used by businesses today. This is mainly due to the increasingly availability of data and the important of data-driven decision-making process in business.

The basic elements of a decision making process are:

  • the set of choice or options
  • the set of outcomes, corresponding to the above options
  • a valuation of outcomes

Decision-making is about making a choice (or selecting an option) that make sense in light of the valuation of outcomes. Without going too crazy with extra assumptions, such as rationality of the decision agent, the above components allow us to analyze the decision-making process. The simplest decision-making case is when there is only one option (in other words, no choice).

In general, there are four types of decision making:

  1. decision making under certainty (the outcome for each choice is known)
  2. decision making under risk (the probabilities of more than one outcomes for each choice are known)
  3. decision making under uncertainty/ignorant (the possible set of outcomes is known, but not the probabilities)
  4. decision making in interactive context (game theory, gaming context)

with everything above prepared, known, and fully specified, a rational decision making will be reduced to an optimization process, with the exception of case 4. This is not to say it is simple, in fact, many optimization processes in real world can be exceedingly difficult.

Optimization technique is at the core of decision making; it is also the center piece technology of data analytics.

In my professional life as an analytics consultant, I have found this basic conceptual framework very valuable. Whenever a new business problem arise, I often start looking for the core decision making problem. The subsequent steps are, in turn: figuring out the set of all possible choices, what are the constraints which, combined with above, gives a feasible choice set), and the outcome measures or project objectives (from which valuations are derived).

What is so valuable about the framework is not that it ultimately gives a formal setup of the problem; more often than not, there are no clear answers to any of the above questions. Instead, it is the process of trying to clear things up that often helps uncover blind spots and missed opportunities that might otherwise be overlooked.

Much of the Modern Decision Theory is, above and beyond its conceptual frame, quite irrelevant to the actual decision making in the real world. The things that get skimmed over, abstracted out, and cut off before it become a well specified optimization problem are often the real issues for (good) decision making; and it is in dealing with these things that data analytics plays a big role. Data analytics help better decision making by:

  • reducing risk and uncertainty associated with options using predictive modeling, and
  • expanding set of feasible options
  • making optimal choice possible through the use of efficient algorithms

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