# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 3 Phase 1: Answering RQ1
### A Framework for an Exploratory Case Study for Data Analysis and Visualization Practices  
As introduced in Section 1, the research method adopted in this study is an exploratory case study research with a single case. This choice, which is allowed by the methodology, was made due to the nature of RQ1: a single case study allowed for a deep exploration of visual analytics practices in different organizational departments; hence, it provided a wider glance at different kinds of key activities supported by visualization artifacts,different decision-making granularities, and a higher chance to discover variability in expertise in the management of these tools. Figure 1 depicts the context-dependent workflow of our framework, with its three perspectives: _Technology flexibility and adaptability_, _visual information literacy_, and _business strategy_. Each perspective is instantiated into an entity of the framework: _Routine_VizTask_, _Human_Interpretation_, and _Contextual_Rule_, respectively. The tripartite set of dimensions in Figure 2 was explored in the case study, and is derived from [4], where the above framework for characterizing context-dependent perspectives in human-data viz interaction was designed. Visual analytics practices are instantiations of the _Routine_VizTask_ entity, which concerns the _technology_ perspective. The adopted technology should be flexible and adaptable enough for performing visual analytics as often as the user's role may require. Characterizing the _Routine_VizTask_ entity means providing evidence of the users' difficulties, in terms of challenges when using data viz, integrating data sources together, and choosing the performance measurements (e.g., a key performance indicator--KPI). Visual analytics practices should boost the _Human_Interpretation_ entity in order to answer questions about the organization's conduct [28]. This step regards the second perspective of the framework, the _visual information literacy_ of the user. Visual information literacy has been characterized as \"the ability to properly process information related to data graphics, i.e., encoding information into data graphics and decoding information from data graphics\" [2]. For example, more complex charts may be automatically provided based on the user's domain expertise and ability to interact more intensively with data viz. Visual analytics practices also operationalize the perspective of _business strategy_. This perspective can be regarded as an instantiation of the _Contextual_Rule_ entity, and can be easily mapped to the concept of contextual knowledge sharing that a visual artifact promises to deploy. This third perspective may be characterized as the capability of the data viz to reflect the users' and organization's strategies. In terms of interactions, these strategies concern two aspects: the information quantity contained in the data and the users' behavior during interactions with and communication through data viz [29; 30]. The former requires a knowledge of how data visualizations respond to the query information from the point of view of the complexity of the data that need to be shown. Complexity depends on the number of entities represented in the data, the number of properties depicted for each entity, the type of data of each represented property (being nominal, ordinal, numeric discrete, and numeric continuous), and the level of detail (e.g., aggregated or not aggregated) [31]. Studying the systematic users' behavior can help identify the areas of interest that each user focuses on. This behavior depends on their confidence with the visualization at hand, their current strategy, and their literacy level. Also, engagement and collaboration with colleagues may be dimensions related to all the three perspectives of the framework. Assessing the information complexity and the users' behavior may bring  
Figure 1: The context-dependent human–viz interaction framework [4] and its relationships with the three perspectives.