Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Abstract

Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.

Proposing a Perceived Expertise Tool in Business Data Analytics

“…Tests revealed significant pairwise differences between the mean scores of PET's levels (see Figure 1 and Competent (M = 3.55 SD = .680), p < .05, based on their visual literacy; and (c) Expert (M = 4.42 SD = .496) and Competent (M = 3.94 SD = .617), p < .05, based on their self-efficacy (indicating the acceptance of (H 3 )). These results may imply on one hand that more expert users perceive also themselves as more knowledgeable and skillful in their line of business while on the other hand they may have the ability to understand and work with a wide variety of data visualizations for specific business tasks [29]. Furthermore, stakeholders that present higher levels of expertise and exercise also higher levels of self-efficacy characterized from high self-control and ability to moderate their behaviour to reach their goals.…”

“…There are many works that studied the influence of expertise (or experience) in the adaptation and personalization of data visualizations. Indicatively, [29] found that an initial understanding of the data visualization types could be predicted significantly by the business users' experience, in terms of education, working in a data-driven job, and the degree of statistical knowledge. Results which have been partially confirmed by [35], arguing that users' experience, besides preference, also affects satisfaction and the capability of being familiarized or switching between graphs to obtain information (e.g.…”

Graph schema and best graph type to compare discrete groups: Bar, line, and pie

“…Future studies should use more data points with different numbers of trend reversals (i.e., slopes of adjacent lines from positive to negative or vice-versa), as it was shown that they have an impact on comprehension time (Carswell et al, 1993 ). Future studies should also examine how schema switches might affect graph processing when a single task involves comparisons between multiple (similar or different) graphs, that is, in complex graph display (e.g., see Riechelmann and Huestegge, 2018 ; Poetzsch et al, 2020 ). Other types of tasks should be used in future studies, such as a more basic “which is larger” comparison, A + B vs. C + D, as pie charts are ideal to combine even non-adjacent slices compared to summing up heights in bar graphs (Spence and Lewandowsky, 1991 ).…”

Human-centred Persona Driven Personalization in Business Data Analytics

“…Nevertheless, although significant effects have been observed in user-data visualization interactions by multiple works and in a variety of application domains, these ideas have rarely been applied, to our knowledge, to the business sector despite the encouraging findings [23]. Henceforth, the vision of this research work is to provide a preliminary step towards addressing this gap for enabling human-centred adaptive data visualizations that will facilitate efficient exploration and analysis of complex and multivariate business datasets, thus, enabling more effective decision making on critical business tasks.…”