Data Analytics in Business has been one interesting ride. The faculty, Dr. Bongo has skilfully taken us through the world of uncertainties, attempting to redefine it by assigning appropriate probabilities. The most interesting of the learnings have been the application of theories learned like Bayes Theorem, and probability distributions to real-life problems with the aim to solve them. Most recently, learning climaxed with an introduction to Decision analysis. This paper aims to define decision analysis and explain its importance in the business environment.
What is Decision Analysis?
Decision analysis is a field of analytics that deals with the art of developing the most optimal strategy from a group of alternatives. It involves the identification of the various aspects of the options while taking into cognizance the most favourable outcome for the business.
Businesses are often inundated with risks and uncertainties, especially in relation to future events. It goes without saying that our decision is only as good as our ability to efficiently define the problem in the first place. Hence, decision analysis ensures that risks and uncertainties are carefully studied and evaluated. This process ensures that decisions taken are optimal and not left to chance. For instance, a company can make an informed decision on whether to invest in acquiring a smaller company.
Some examples of the types of decisions made in the business space are strategic, administrative, and operational decisions. Whatever the aim, it is important to make good decisions.
Terminologies in Decision Analysis
Certain terminologies are used in the art of decision analysis. Understanding these terminologies is the first step to the effective use of this type of analysis. A decision problem is fully described by its alternatives, states of nature, and their resulting payoffs.
These are the different possible options that are available to the decision-maker in a business scenario.
States of nature
These are the possible future events that may occur. They are usually not in the control of the decision-maker. States of nature must be fully captured to enhance the analysis
This refers to the resultant effect of the combination of the decision alternative and state of nature.
There are various approaches to decision analysis, depending on the available information such as probabilities. Where the latter information is available, it is referred to as decision-making with probabilities. On the other hand, where the probability information is unavailable, it is called decision-making without probabilities.
Decision making without probabilities
Three main approaches are employed which are the optimistic, conservative, and minimax regret approach.
Optimistic approach: the eventual pay-off with the largest possible pay-off is chosen by the decision-maker. This is also known as the Maximax approach.
Conservative approach: otherwise known as the maximin. It is often used by conservative decision-makers. The maximum of the minimum payoffs is selected. Thus, the maximum can be said to be minimised.
Minimax regret approach: this involves finding the difference between the pay-off for each state of nature and the largest pay-off. After which, the corresponding minimum of the maximum on the regret table is selected. The regret table is also called an opportunity cost table.
In the concluding part of this write-up, we would look at the other approaches used in decision analysis.