The automation industry is currently forced to deal with large amounts of data. However, it can be difficult to manage large volumes of heterogeneous data from different sources, so it is important to have methodologies and systems capable of storing and managing these data. Big Data Warehouses are data systems that facilitate the management of big data, but, as a consequence, other challenges arise, such as data modelling issues. This research proposes a methodology for data models that can evolve with time to integrate new business processes and new data. As demonstration case, the methodology is applied to the automation industry to propose a data model that supports the storage and processing of analytical indicators for sustainable product development.