What is predictive analysis?
Anticipating, controlling and above all automating actions in companies are more and more necessary in the professional world. Automating actions to make them more reliable and gain in productivity is an objective for many companies. Today, the terms artificial intelligence, big data or predictive analysis are very often used. They represent a new sector still unclear to the public.
Presentation of new technological tools for companies
To understand what predictive analytics is, you need to understand what big data and artificial intelligence is. Indeed, these tools are all linked together.
First of all, artificial intelligence is a set of techniques set up in order to create machines able to simulate human intelligence. Building tools with artificial intelligence mixes computational neurobiology, mathematical logic and computer science. Problem solving methods are based on algorithms. In concrete terms, voice assistants are concrete illustrations of artificial intelligence in our societies.
Then, big data refers to a set of data so important that it exceeds intuition, human analysis capacities and computer tools normally able to manage its databases. In concrete terms, the explosion in the amount of data has forced researchers to find new orders of magnitude in terms of capturing, searching, sharing and storing data. Big data is therefore a concept for storing a very large amount of information on a digital medium.
These two components are intimately linked to predictive analysis. Indeed, predictive analysis is set up in order to analyze, as its name indicates, the data stored and produced by the Big data and artificial intelligence. They are used by companies to improve their competitiveness, for example. The predictive analysis is thus based on these data in order to predict situations or values.
Warning: it is important to understand that these analyses produced thanks to artificial intelligence and Big data are not absolute. In another register, gataka.fr gives you interesting information about the rise of Bitcoin.
Why use predictive analysis?
Today, companies are turning to predictive analytics to solve internal problems. Indeed, artificial intelligence, data mining or even machine-learning allow to solve complex problems within companies.
Predictive analysis allows, for example, to improve marketing campaigns.
Based on data, these tools are capable of anticipating customer reactions and knowing their purchases in order to direct the marketing campaign to be carried out in a very precise manner. These actions help to build customer loyalty and optimize marketing actions.
This is not the only usefulness of predictive analysis. Indeed, this one also makes it possible to improve the chains of production. For example, the supply chain or inventory management is nowadays optimized when using machine learning. Human errors are also reduced. The company therefore saves precious time and makes financial savings.
On another level, predictive analysis also makes it possible to anticipate criminal behavior. The behavioral analysis of individuals coupled with algorithms makes it possible to determine with a very high probability the choices and behaviors of individuals. The cybercrime sector is therefore now routinely using these analyses in their actions. This makes it possible to detect much more quickly anomalies that can be traced back to fraud or deviant behavior. They have a very high probability.
Finally, the insurance sector today also uses analysis tools. For example, the artificial intelligence of these tools is capable of knowing whether a customer is defaulting or not. The result gives a more or less strong probability of failure. Insurance companies then adjust their prices according to the results obtained from the 2.0 analyses. They thus optimize their service.
In short, predictive analysis is a tool that is now perfectly integrated into the life of companies and is gradually becoming indispensable!
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