How can different data buckets be integrated and flexibly analyzed using Big Data techniques?

The systematic analysis of ever-increasing data collection presents companies with ever-greater challenges. Many companies simply lack the know-how to handle big data projects. Following to the motto “Let’s do a Data Lake first”, they bring together all available data in one system. Because often they are subject to the misconception that you should put as much data as possible in the system in order to gain the maximum insight and the most flexible evaluations. Unfortunately this does not work, because we can expect performance problems here. Therefore, the company must also think about meaningful evaluations for big data analytics in advance, that offer added value considering the cost-benefit ratio. An upstream potential analysis is recommended and can provide insight and foresight here.

This research report summarizes a part of the work performed in the PRO-OPT SMART-DATA research project. In the project a wide variety of production data modeling approaches of an automotive supplier were tested out. Apart from the problems of systematically merging different data buckets and the possible modeling of the data in NoSQL databases, the main focus of the work was on the analysis of these large data collections. The objective was to be able to apply and compare statistically reliable analyses and classification procedures as well as new procedures from the upcoming AI instruments. The work is summarizes in this report.

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