Predictive analysis of large amounts of data

Digitization and the web are generating more and more data, and almost everyone knows the term "big data." It is only useful to be able to interpret these amounts of data. The Volkswagen Group deals with experts such as Gabrielle Compostella. In Group IT's data lab, he is a team that uses human expertise and artificial intelligence to analyze big data. Their predictive analytics helps make many processes and business processes more efficient and sustainable.

Predictive analysis of large amounts of data
Gabrielle Compostella is a data scientist at the Volkswagen Group's IT Data Lab. Scientists like him analyze and interpret massive amounts of data. At the data laboratory of the Volkswagen Artificial Intelligence Competence Center (KI) in Munich, a multi-member team of experts is working on this. “Our work is quite similar to the puzzle,” said Compostella, a native of Italy. “We have a lot of parts, but only when we put them together correctly, will an obvious picture appear.”
System approach is important
Compostella and his colleagues don't care about the personal data in their work, but instead focus on the information that the Volkswagen Group produces every day through complex business processes. These include logistics and cargo flows, financial critical data, requirements and minimum levels of consumption. “Seeing a big picture here, a systematic approach is necessary,” Compostella explained.
Why is it whole? “Data can help answer questions correctly and be based on facts,” Compostella said. These are issues for the future. Technical terms are predictive analytics, ie, forward-looking analysis. Compostella gives an example: How does market demand develop into a device line, and how does the supply situation develop? What components and components must be when and where? Can you derive trends? For global companies like Volkswagen, these issues play an important role in making processes and processes more efficient and sustainable.
Supervise machine learning
Analyzing large amounts of data and combining them in a meaningful way is an overwhelming task. In data labs, data scientists like Compostella work closely with artificial intelligence experts. IT experts say: "No one can piece together thousands of parts." "We are developing a self-learning system." The team provides data for these algorithms, evaluates, combines and draws conclusions - and corrects errors. This is called supervised machine learning.
“Information and data have always been in our company. But just a few years ago, we had the technical possibility of linking data from different sources,” Compostella said. At the same time, in the data lab, experts are still experimenting with data analysis of traffic flow. They want to work with cities to test how to optimize urban traffic through intelligent data analysis.

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