The amount of data has grown exponentially in the past decade. Therefore, analyzing huge volumes of network data has become much more difficult and often inadequate using current data mining methods alone. Human involvement (i.e., interaction, flexibility and creativity) is essential, but it can be extremely time consuming given the current amount of links and nodes present in the data modeling social networks. Therefore, the main aim of this study was to investigate how to decrease the number of links and nodes which, in turn, simplifies the visualization process in social network’s visual analytics, such as size, colors and shapes of the graphical elements matter. This study consisted of a literature review of constructive action research and the development of a new visual analytics system for a Finnish State Department by utilizing an agile system development method. Participants included this author as a developer and eight employees from the above Department who acted as end-users of the system. Of these, five were selected for interviews for their long work experience. The evaluation data was collected by using participant observation and semi-structured theme interviews. Additionally, questionnaires were given to the five interviewees, which allowed for more a concise information collection. After the transcription of the interviews, the data was analyzed using counting, scaling, and thematic analysis. The main result of this study was the development of a new sector model for visualizing social network data which reduces the number of links and nodes and also accelerates and facilitates the preliminary analysis of the data. This increases work productivity of end-users much more than any other implemented system feature. A secondary result was the reduction of the majority of the links and nodes by visualizing a transaction, opposed to the previous method of using two links and a single node with only one link. By changing the way of visualization as well as filtering and summing-up the analyzed data, it was possible to reduce the total number of links and nodes significantly. Overall, it can be stated that by reducing the number of links and nodes it is possible to clarify and facilitate the visual analytics, especially by creating an overall picture of the analyzed social network. However, it is important to note that lost essential data content is still present in an alternative manner so that the needs of every user’s information are provided for.