Key ingredients in contributing added value to a business are the advanced analytics teams comprising data engineers and data scientists. A large proportion of their work is often spent on data engineering tasks to clean, gather, and combine the data from various data sources into a consumable format. The actual problem to be solved loses prominence to the boilerplate work (work that is repeated in an unchanged form) that needs to be done over and over again. In many cases, only the automation experts can select and understand the available data. This is due to the fact that the operational technology (OT) systems store their data in a format that meets their own requirements but not the purpose of analyzing and merging it with other data sources. A lack of documentation of the data sources creates an additional barrier to utilizing the data. Depending on the problem that needs to be solved, the data must be available in different formats. The data are typically viewed from a condition, quality, and planning perspective. For the maintenance view, the data are often retrieved using certain events in time. The data must therefore be addressable and selectable via a time range.