## 5 Discussion  
Data-driven approaches can report a lot of benefits, but it is necessary to gain insights and generate knowledge from datasets to make informed decisions. Information dashboards present data in an understandable way, enabling the audience to identify patterns, clusters, outliers, etc. Information dashboards that in the past were mainly employed and reserved for technical and analytical profiles are now spreading their use across any kind of profile. However, it is necessary to understand the audience to design an effective dashboard [10; 11]. In this work, a dashboard meta-model has been presented. The meta-model abstracts the main technical and visual features of information dashboards. The different visual marks, and how they encode variables or operation outputs through channels, as well as other elements such as scales, axes, legends, etc., are part of the meta-model, because these are the primitives that are shared among data visualizations. In this case, the meta-model primarily focuses on the user. The final user, which will be the entity that would gain insights through the dashboard, can have different characteristics and goals regarding data, and they could change depending on the context. Capturing these traits through the meta-model is essential, as dashboards' features arise from the users' requirements and are influenced by them [10]. The end-user might be seen as an \"external\" entity that has almost no influence on the dashboard design or technical features, but in the end, these technical features are crucial to deliver a good user experience. This is why user preferences, as well as other characteristics, like user's knowledge level about the data domain, visual literacy, and user's potential biases, are represented in the meta-model and are tightly related to the dashboard elements. This information provides the most suitable view type by configuring recognizable visual marks or visual metaphors, preferred visual design, etc. Moreover, user disabilities are also considered, such as color blindness, hand tremors, etc., because they refine the dashboards' visual design and/or interaction methods making fonts more visible, choosing right color palettes, mouse sensibility, etc. Assessing visualization literacy is currently an important research field [60; 61], in order to know beforehand the users' visualization knowledge level and to deliver an understandable (yet effective) set of visualizations for them. Furthermore, the users' knowledge about the data domain should be addressed in the same manner; by providing views with understandable data dimensions and contextual information to mitigate unawareness about the domain [10]. User bias is also an important trait to account for. Users might be influenced by social or cognitive biases that could distort the discovery of knowledge. Bias could lead to valuable information loss [62; 63], that not only could undermine people but also lead wrong decisions by not addressing biases when analyzing data [64]. However, one of the most important aspects is the users' goals or intents regarding the displayed information when they analyze data. These goals provide hints about which visual metaphors or marks are needed because some charts are more effective for some goals (and tasks) than others [35; 65]. A goal framework and a task taxonomy have been included in the dashboard meta-model to provide more detailed (although abstract) information regarding these two elements. Including this information has a lot of benefits, because goals and tasks are usually expressed in natural language. Using these taxonomies and classifications and arranging them into a meta-model allows the structuration of goals and tasks, thus easing their processing. Many taxonomies were available to classify goals and tasks. However, the analysis goal framework [49] was selected for characterizing goals because of the flexibility that it offers for bridging analytical goals to existing task taxonomies and its well-defined inputs and outputs.