Data masses require a lot of data processing. Data mining is the traditional way to transfer data into knowledge. In visual analytics, humans are integrated into the process due to continuous interaction between the analyst and the analysis software. Data mining methods can be utilized also in visual analytics, where the priority is given to the visualization of the information and to dimension reduction. However, the provided data is not always enough. There is a large amount of background contextual information, which should be included into the automated process. This paper describes a context-sensitive approach, in which we utilize visual analytics by studying all phases in the process. To observe the process from all points of view, we divide it into three architectural sections: sensing, processing, and actuation. Thanks to the versatility of visual analytics methods, our goal in the future is to apply these methods in the field of energy production in a laboratory-scale power plant.