# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 2 Background and Related Work
### Data Analysis and Visualization in Organizational Contexts  
The problematization of the topic reported in RQ1 regards the gap between systems' capabilities and users' expectations. The structured review in [8] synthesizes three decades of research in data management and visualization. The primary aim of this review is to highlight the gaps in visualization design, where the usability of a system is often compromised by the mismatch between its design and users' wishes. The review categorizes and discusses several key database optimization techniques that have been shown to benefit interactive analysis systems. At the end, considerations about the need for more robust and scalable solutions to support the increasing complexity and size of datasets in visualization systems are discussed, together with an integrated approach, where such optimization techniques are also available to the designers of visualization systems.  
Another work contributing to moving the problem of data analytics and visualization outside the lab was that in [9]. In the vein of analyzing several organizational domains, such as healthcare, finance, and marketing, semi-structured interviews were conducted, and three professional archetypes were identified in the end: hackers, scripters, and application users, each one representing a different approach, skill set, and interaction modality with data analysis tools. The study proposed five high-level tasks that key users perform with data: discovery, wrangling, profiling, modeling, and reporting, as well as three categories of tool functionalities: database, scripting, and modeling. These professional roles, activities, and tools partly overlap with the ones identified in the present study. For ex ample, the same considerations emerged pertaining to what were the more tedious tasks in data analysis. Some of these aspects regard bad data quality, still unresolved when not amplified in AI-assisted tasks [10]. Although this study touched on themes and outcomes that are very similar to ours, it was limited to the framing of organizational roles and practices, without any intent of exploring the impact of different technologies on users' behaviors and expectations.  
These behavioral and procedural aspects are better analyzed in the user study in [11], where 22 participants were observed during interaction with an AI-assisted prototype, based on an LLM, while performing different tasks based on the ARCADE benchmark [12]. The study aimed to observe how data analysts understood and verified the correctness of AI-generated analyses. This was not a case study, but it was based on generic data analysis tasks and on crafting a prompt, and it did not focus on visual analytics. Thus, the focus of the study was not on the behaviors and expectations of users regarding organizational decision-making practices.  
Regarding the gap to be filled in terms of data visualization capabilities and desiderata from decision makers in enterprises, the study by [PERSON] et al. [13] anticipated many of the issues still observable and unresolved in the organizational domain. The study used an online questionnaire asking about organizational data, technological equipment, and visualization usage. Many interesting outcomes emerged from the survey, in particular, the need for qualitative explanations of quantitative data analysis and visualizations; the need to have some background information on the data provenance and preparation; and the need to contextualize data-driven analysis into a wider picture before making sense of data. All of these aspects converged into the hypothesis that AI-based data visualization tools may have a role in them. However, this study did not specifically investigate the use of AI-based tools in enterprises. No design probe was put into place to hypothesize research directions that take into consideration this kind of technology.