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Data-driven system and product development with the customer fleet

The applications that provide this kind of data product require the use of data science methods and machine learning techniques to explore, aggregate, classify, and (to) identify anomalies in huge amounts of data in near real-time. In this case, technical-methodological knowledge is relevant, as is a deep understanding of the respective domain.

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Fig. 2: Excerpt road works information Germany

In general, the modularization and integration of individual data products play a major role. In most cases, customers are only interested in individual data products, but increasingly various data products (also from different providers) are being combined into a new product in order to derive further information. This requires a flair for good product design in the area of conflict between completeness and data protection. In addition to all the challenges, however, the topic primarily means opportunities for automotive manufacturers to position themselves as suppliers of data products in new markets. Potential external customers include cities and municipalities, public institutions, map and weather services, logistics companies, startups, and many others.

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Daniel Wirtz

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