Last in my last post on Return on Information I dealt with Information Sophistication and how we could overcome the volume of information and reach an optimized scenario, where actions could in operational systems could be acting on a results from Prescriptive advices.
What Predictive Analytics is
Predictive is really looking into the future, based on information trying to figure out the probability of a certain outcome.
There are many different types of models. One of them is a decision tree, where it flip-flops its ways down a number of deterministic questions, leading down to a probability outcome. The most decisive factors at the top of the tree, while less decisive lower down in the tree. In the basis form is most of the Analytical models are limited to represent linear characteristics. Yet there are now more modern tools that introduce machine learning techniques based on artificial intelligence. These tools are capable of including nonlinearities and complex interdependencies.
A good example of predictive Analytics is Predictive Maintenance, heaps actors goes into predicting when equipment failure might happen. Use of predictive maintenance may be decisive keeping production sturdy. It is key to know when to perform scheduled maintenance to avoid production fluctuations and keeping cost down. This is complicated further by the amount of equipment that goes into running a larger operation.
Another good example is Customer Retention. It is key for companies to gain the ability to group customer after their loyalty, in order to targeting them individually when special designed promotions. The clients is scored after key parameters, such as how many purchases he has performed from the company, his net income, areas where he lives, the channel he used to buy etc.
The rational decision-making process
Rational decision-making involves specification of the problem, identification and weighting of involved factors, identification and rating by factor of alternatives and choosing the optimal.
Let’s dive into two of the concepts Probability, and expected value. Probability tells us the likelihood that any particular outcome will occur. A probability of 1.0 represents certainty that an event will occur. A probability of zero represents certainty that an event will not occur. The expected value of every eventuality is summed up by probability and associated with their alternatives also summed by their probabilities. One simple rule for making decision is to always select the alternative with the highest expected value.