Today, the terms Artificial Intelligence, Big data or predictive analytics are very often used. They represent a new sector still vague for many people. Anticipating, controlling and especially automating actions in companies are increasingly necessary needs in the professional world. Automating actions to make them more reliable and gaining productivity is a goal for many companies.
Presentation of new technological tools for companies
To understand what is predictive analytics, we need to understand what Big Data and Artificial Intelligence are. Indeed, these tools are all related to each other.
At first, Artificial Intelligence is a set of techniques put in place to create machines capable of simulating human intelligence. Building tools with Artificial Intelligence mixes computational neurobiology, mathematical logic and computing. Problem solving methods are based on algorithms. In concrete terms, voice assistants are concrete illustrations of Artificial Intelligence in our societies.
Then, Big data designates a data set so important that this set exceeds the intuition, the human analysis capabilities and the computer tools normally able to manage its databases. Specifically, the explosion of data has forced researchers to find new orders of magnitude about capturing, searching, sharing or even storing data. Big Data is therefore a concept for storing a very large number of information on a digital medium.
These two components are closely related to predictive analytics. Indeed, predictive analytics is set up to analyze, as its name suggests, the data stored and produced by Big Data and Artificial Intelligence. They are used by companies to improve their competitiveness, for example. Predictive Analytics is therefore based on these data to predict situations or values.
Why use predictive analytics?
Companies are now focused on predictive analytics to solve internal problems. Indeed, Artificial Intelligence, data mining or machine learning make it possible to solve complex problems within companies.
For example, predictive analytics can improve marketing campaigns.
Using data, these tools are able to anticipate the reactions of customers, to know their purchases to guide very precisely the marketing campaign to achieve. These actions help to retain customers and optimize marketing actions.
This is not the only usefulness of predictive analytics. Indeed, this one also makes it possible to improve the chains of production. For example, the supply chain or inventory management is now optimized when it uses machine learning. Human errors are also diminished. The company therefore saves valuable time and realizes financial savings.
Predictive analytics can also anticipate criminal behavior. The behavioral analysis of individuals coupled with algorithms makes it possible to determine the choices and behaviors of individuals with a very high probability. The cybercrime sector now commonly uses these analyzes in their actions. This makes it possible to detect abnormalities tracing frauds or deviant behaviors much more quickly. They have a very high probability. The site lebigdata.fr gives you further details.
Finally, the insurance sector also uses analytical tools today. For example, the Artificial Intelligence of these tools is able to know if a customer is failing or not. The result gives a probability of failure more or less strong. The insurers then adjust their prices according to the results obtained from the 2.0 analyzes. They optimize their service.
Ultimately, predictive analytics is a tool that integrates today perfectly into the life of companies to become progressively indispensable.