The advances in artificial intelligence and machine learning represent a great opportunity to analyze Big Data, obtaining information useful for business objectives.
The advent of digitalization has in fact revolutionized daily experience and business organization, opening the doors to the data economy. The proliferation of information represents a concrete opportunity to optimize workflows and create new business. On the competitive chessboard, those who have the ability to analyze large volumes of data and extract hidden evidence useful for decision-making processes and automation of activities win.
According to Idc, the information magnum will expand to reach the exorbitant figure of 163 billion zettabytes by 2025. This is why information governance and data analytics strategies acquire an unprecedented centrality in the growth roadmap of companies.
Artificial intelligence: what it is and how it works
But if the arrival point is clear (the extraction of value information aimed at human or artificial decision making), the approach to building an effective data management system requires careful study.
New technologies, starting from artificial intelligence, can represent the keystone. AI includes a set of hardware systems and software applications that allow you to reproduce human reasoning and skills in solving problems or performing tasks. In the IT and business environment, artificial intelligence refers to technologies that can automate processes, that is, to make decisions and take actions regardless of the intervention of people. In particular, it indicates (according to a modern meaning) the development of algorithms and mathematical models that allow computers and machines to perform a sequence of logical activities, typically within a specific application area.
Subdomain of artificial intelligence, machine learning instead refers to the ability of a technological system to learn automatically without having been programmed in advance. In essence, the algorithms allow you to learn directly from experience (just like man would), processing a set of data acquired over time and improving performance in an adaptive way, as the real examples from which to draw information increase.
For example, cobots (the collaborative robots that increasingly support operators on production lines, in the most repetitive and arduous jobs) learn and refine the actions to be carried out in the field. The most common machine learning applications include search engines (which improve the relevance of outputs based on algorithms), recommendation systems (which study user behavior on the web to propose content of interest), anti-filters spam (which learn to recognize suspicious messages, acting accordingly).
Analyze big data with artificial intelligence
In short, artificial intelligence and machine learning allow to manage (select, cross, analyze) large volumes of multisource data (from online sources, management systems, devices of the Internet of Things and so on) with the aim of extract information useful for human decision-making or to trigger automatisms.
The right set of algorithms allows you to orient yourself in the datasphere: the analytical process starts from the choice of information relevant to the resolution of a question, with machine learning that allows you to perfect the selection over time.
The next step is represented by the actual analysis, then by the application of statistical and predictive models, by the discovery of useful insights and by the consequent execution of the tasks (decision and operational).
If the definition of the algorithms is the beating heart of the artificial intelligence applications, we must not neglect the importance of machine learning for updating and maintaining the calculation models, which must be continuously improved and questioned to increase the level of accuracy.
Thanks to the application of artificial intelligence and machine learning technologies, companies in any sector will be able to secure a series of important competitive advantages: the ability to process business strategies with greater awareness(precisely because they are based on data evidence); the possibility of make decisions quickly thanks to the automatic selection of the relevant information and to the evidence discovered by the algorithms; speed, efficiency and operational continuity thanks toautomation triggered by intelligence.
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