The adoption of the Internet of Things (IoT) in industry provides the opportunity to gather valuable data. Nevertheless, this amount of data must be analysed to identify patterns in the data, model behaviors of equipment and to enable prediction. In this talk, we present a concept for metadata representation that tackles some of the challenges posed by big data ecosystems to data interpretation and analysis. By combining big data technologies (e.g. Spark on Hadoop) with semantic approaches, the processing and analysis of large volumes of data coming from heterogeneous sources becomes reliable. The metadata representation approach is adopted for defining the structure and the relations (i.e., the connections) between the various data sources. This enables the development of a generic, reusable and responsive data analytics framework, which is ensuring data validity and trustful analytics results.