## 1 Introduction  
Information is the driver of many processes and activities nowadays. Designers, managers, developers, etc., make decisions continuously with the goal of obtaining a benefit or a pursued effect on the context of application. Data-driven decision-making [1] has several advantages, but it is also a complex process in which the involved actors must understand the data they are consuming. Data can hold underlying patterns that could go unnoticed without performing in-depth analysis. However, the great quantity of available data can make the analysis process a resource- and time-consuming task. For this reason, many tools have emerged to support and ease the discovery of insights through data from different domains [2; 3; 4; 5]. Information dashboards are one of these tools. Information dashboards arrange data points into different views, displaying information visually, and allowing users to identify patterns, anomalies, and relationships among variables in a straightforward manner. However. using an information dashboard does not ensure knowledge generation. There are several factors involved in the process of discovering insights through displayed data, and one of the most important factors that condition the utility of dashboards is their audience (i.e., their end-users). People have several characteristics that could make their user experience using the same product (in this case, an information dashboard) very different from each other. For example, some users might not have enough visualization literacy (also known as graphicacy [7; 8]), and thus, they might be unable to understand some charts or encodings. On the other hand, their knowledge regarding the data's domain could be low, hindering their ability to reach meaningful insights. Consequently, dashboards need to be adapted not only to the data they are displaying but also to their audience [9; 10; 11]. This is not a trivial task: the data domain and potential user profiles must be researched, among other factors, to obtain a visual display that is really useful for making decisions (this should be the first priority of a dashboard: to support decision-making or knowledge generation). Tailoring a dashboard is not only complex at design-level (where several factors and design guidelines must be accounted for) but also at the implementation-level. Coding a tailored dashboard for each possible context is a time-consuming process. Different approaches have been considered in the literature to automatize this implementation process and to decrease the development time of tailored dashboards [12]. From configuration wizards that allow users to choose the charts of their dashboards (e.g., Tableau, [[https://www.tableau.com/](https://www.tableau.com/)]([https://www.tableau.com/](https://www.tableau.com/)), or Grafana, [[https://grafana.com/](https://grafana.com/)]([https://grafana.com/](https://grafana.com/))) to model-driven approaches that render personalized dashboards based on formal descriptions of the domain [13; 14; 15; 16], among other variety of solutions. These approaches take into account mainly user preferences, but also the input data structures, business processes, user abilities, user roles, etc. [17]. These factors are extremely relevant to the design process; they can support the selection of appropriate visual metaphors, encodings, or structure of the dashboard to increase its effectiveness and usability. In fact, users might need not only differing visual metaphors to understand the same dataset, but also a whole different composition of views that hold different data variables. This happens because users could be focused on different variables and could have different questions regarding the same dataset; that is, users have their own goals when referring to a dataset. Accounting for a user's information goals is essential for the development of information dashboards; they frame and contextualize the tasks that can be carried out with data, as well as the variables that should be involved within the display.