The growing importance of data in managing complex and short-time processes has moved analytics tools to the heart of IT infrastructures. The passage of the final analysis to the predictive ones also increases the need to rely on large quantities of data and their quality—a condition to which IoT offers a perfect answer. Sometimes, even too much. The enormous amount of information on the Internet of Things produces risks of being dispersed without adequate tools for collection and selection before using them. The opportunities are as many as the difficulties of grasping them.
Therefore, it is helpful to hear directly from the sector’s protagonists what the state of the art of data analytics is, what the contribution of IoT is and above all, how to exploit this opportunity. Much of the benefits attributable to applying data analytics principles to the IoT result from the ability to close the gap between data availability and the ability to act. Lanfranco Brasca, Oracle’s technology director of cloud engineering, also helps to understand how this result was achieved and the implications.
What Innovations Can IoT Bring To Data Analytics?
Analytics allow you to look beyond the data to begin understanding, by correlating variables, why unwanted events occur—a customer who abandons a mobile phone service, production machinery in a plant that breaks down. The real breakthrough has been made by applying machine learning models, which can correlate hundreds of variables in billions of different scenarios.
Here the companies have gained the awareness that – starting from the occurrence of certain conditions – it was possible to arrive at prescriptive actions capable of preventing unwanted events. For example, a retention action on the customer before he changes manager or planning a preventive maintenance intervention. IoT integrated with data analytics allows this type of analysis to be carried out continuously.
In contrast, in the past, the data could be analyzed asynchronously, setting up massive databases after a filtering action (which today we would call Data Lake House) on which develop and train machine learning algorithms to be used to extract business insights. Simplifying, the IoT closes this time gap between when an event occurs and when the systems can give indications to the business and allows the application of highly evolved strategies on a dynamic database – the so-called “data in motion” movement and processed in near-real-time.
Do Companies Have The Correct Perception?
Despite the specifics linked to the alien presence of more or less large companies. A boost was given by the industry 4.0 programs, but the perception is that we are still at the beginning of a long journey. The real challenge in the coming years will be to bring on board all the Italian companies that can benefit from it, to fill the technological gap that today allows them to work on still and historicized data over time, enabling them to use data in motion.
Could You Indicate Some Examples Of How IoT Potentials Can Be Exploited In Data Analytics?
A typical example concerns the application in Industry 4.0 projects in manufacturing. Still, there are also use cases in the banking sector, which uses these capabilities to improve fraud prevention, and in the insurance sector that can now propose, for example, car insurance plans tailored to the more or less safe driving style of people.
A fascinating sector exploiting these potentialities with enormous advantage is the sports one, mainly where high speed counts. We have direct experience in Formula 1, with the Oracle Red Bull Racing team’s technological partnership in the sailing world with Sail GP competitions. In these sectors, in addition to mechanical characteristics and design, the real-time technology processes billions of data and makes the difference in race strategies before and during the race.
For example, Oracle Red Bull Racing increases the number of simulations it can run before and during each race by 25%, at ten times faster computing speed than previously. He then applies AI and machine learning to this data and gets key insights for engineers and pilots. The simulation capacity is very relevant in the new Formula 1, which has imposed a cap on the costs of the teams and, therefore, a series of limitations on actual tests.
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