Initiatives like Industry 4.0 aim to accelerate data-based decision support in enterprises. These initiatives generate large amounts of heterogeneous data that need to be stored and managed. It is not always clear what benefits this data will later bring to the company. As a result, it is usually not possible to decide at the time of data collection whether and what value the data will have. To avoid losing any potentially important information, all data are stored in their raw format in an enterprise-wide data lake.
While concepts for the topic of data lakes exist, there is no comprehensive view on the different partial aspects. Thus, it remains unclear how enterprises can conceptualize and realize a successful data lake. Interdependencies between existing concepts are often unresearched. The goal of this project is therefore to define a framework for an implementable data lake architecture.
Funded by: Robert Bosch GmbH