Last June 20th, in the webinar “The value of Prescriptive Analytics in Artificial Intelligence”, one of our experts in Data Analytics explained what this analytical technique is, how it works and what benefits it brings to businesses.
If you are interested in this webinar, you’re lucky! Now you can watch the webinar video here.
“Intelligence consists not only of knowledge, but also of the ability to apply knowledge in practice”.
Aristotle
In the Artificial Intelligence field, the different analytical techniques provide more and more intelligence and business efficiency. The most basic, Descriptive Analytics, provides information and knowledge of the business. Predictive Analytics predicts events and allows us to anticipate them. And the most advanced, Prescriptive Analytics, gives us the ability to automate decision making.
Currently, Descriptive Analytics is used by most companies, and Predictive Analytics has become popular and is already being implemented in their business processes by a large number of companies. But the great unknown is Prescriptive Analytics, the only one capable of automating the decision making process.
What is Prescriptive Analytics and how does it work?
Prescriptive Analytics is the analytical technique capable of automating decision making, evaluating the best solution in complex environments. It uses the information provided by Descriptive Analytics and Predictive Analytics.
There are two possible approaches in this type of Analytics: one based on heuristics (systems based on BRMS) and one based on operational research techniques (modeling of mathematical algorithms).
Models based on business rules management systems (BRMS)
The business rules management systems, or BRMS, are based on an algorithm known as the Rete algorithm. They are decision trees capable of evaluating the match in between the variables and the rules to be executed in a very fast way. Its main advantage is its business orientation, rather than the complexity of the optimization algorithm.
They are used for deterministic processes that automate complex calculations because of the large number of rules that have to be executed. For example, the calculation of the price of an insurance policy according to the customer’s profile or the product to be insured.
Models based on mathematical optimization
Its complexity stems from the large number of variables and constraints that must be taken into account. Millions of variables and millions of constraints to achieve a single solution. The solution is evaluated according to an objective variable that determines the criteria to be considered.
The optimization algorithms match in all possible results, and find the optimal solution. The cases or problems in which mathematical optimization is used are, for example: minimization of costs in a process, maximization of profitability, etc.
Prescriptive Analytics-based tools support decision making, so they are applicable to all departments and business areas.
Prescriptive Analytics is being applied in many innovation areas. From Smart Cities traffic management, energy management or process automation in industry 4.0, to autonomous cars.
If you are interested in the benefits of each of the Prescriptive Analytics approaches in business, see the webinar video “The Value of Prescriptive Analytics in AI”.
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