At its core, the term Big Data describes an enormous amount of structured and/or unstructured data that is too large, too complex, or too fast-moving to examine using simple or manual analysis techniques. However, the term has been used in a variety of ways in recent years. On the one hand, it often appears as a collective term for new, digital technologies; on the other hand,Big Data stands for a new era of digital communication and data processing.

The collection and processing of large amounts of data can optimize business processes in all functional areas of companies and support the use or development of new technologies or products. Basically, however, it is less about a concrete amount of data, but rather about which conclusions can be drawn from the analysis. Data from any source can be collected and analyzed. ERP, MES, CRM systems, databases of any kind, platforms (platform economy), IoT devices, wearables, machines or other networked devices (smart factory | matrix production) can serve as data sources.

Since the data volumes are usually too large or too complex, a simple glance is not enough to identify patterns or connections, so data mining, machine learning or artificial intelligence are used to analyze and gain insights. Based on the new knowledge, informed decisions can then be made to further develop, improve or align the business.

The increasing digitalization in recent years leads to the fact that companies, organizations, platforms and networks generated and stored vast amounts of data. There is incredible potential in this data. It is not without reason that data is also referred to as the new crude oil. A structured analysis of the data only seems sensible and comprehensible. However, the topic of Big Data also comes up against sensitive limits: privacy, personal rights, data retention, data security and surveillance are just a few examples of critical topics that repeatedly arise in the context of Big Data.