Artificial intelligence and data: the challenges

Where does the data go? Many keys to the future lie in the ability to capture, process and understand them. Artificial intelligence is a predictive tool and it is already here.

As intelligent learning systems ( machine learning ) become more sophisticated and precise, they become great allies for data processing. Business Intelligence (BI) processes have gone from being aesthetic and inflexible reports to interactive systems in which information can be filtered, and understand why and how certain events happen. These systems are still under development, and companies like Google, Microsoft or IBM are currently working on various challenges to take them to the next level. Here we present some of them.

Cost:  Artificial intelligence (AI) systems have begun to generate automated reports that include data analysis. According to Yseop, a company that develops AI systems, their cost will be reduced by 77% once their use becomes widespread.

Lack of experts:  in the US alone, almost 200,000 professionals capable of analyzing data are needed and close to 1,500,000 analysts who are capable of making decisions based on analyzed data. Artificial intelligence systems will replace these professionals and they will take more active roles in companies.

Predict:  AI systems are primarily focused on predicting based on analyzed patterns. Although algorithms of this type are already on the market, the Magic Quadrant predicts that by 2018 these systems will be active in more than 50% of the world’s large companies and conglomerates.

Recommend:  According to Herain Oberoi of the Microsoft Analytics team, the final frontier of AI and Business Intelligence is the ability to make recommendations based on predictions. This will not only be done in a proactive way by the system, but through natural interactions between the user and the machine.



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