Long lead times in supply chain processes can result in significant tied-up working capital in inventory, limiting operational flexibility and profitability. To address this challenge, we present VAILO, a Visual Analytics Dashboard for Inventory and Logistics Operations, designed to support sales and operations managers in identifying inefficiencies and bottlenecks in supply chain workflows. Developed through a co-design process with domain experts, VAILO integrates two coordinated visualizations—a hierarchical treemap and a throughput diagram—to facilitate the exploration of material flow and lead time variability across industries and customers. The dashboard enables secure data uploads, customizable visual encodings, and interactive filtering to support user-driven analysis. We conducted a formative usability study with participants experienced in industrial contexts and explored a real-world usage scenario. While initial results suggest that VAILO may help surface actionable insights, they also highlight areas for improvement in onboarding and interaction design. Our findings offer a preliminary contribution toward the design of domain-specific visual analytics tools that aim to bridge the gap between complex data and decision-making in supply chain contexts.

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize . VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars — Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website ( https://datavisyn.github.io/visAhoi/ ) that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix/heatmap, a scatterplot, and a treemap visualization.

Visual exploration of large and complex data is becoming increasingly important in different domains. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To make new forms of visualizations understandable and interpretable for a broad range of audiences, visualization onboarding methods can support users. However, it is unclear whether concrete or abstract materials yield better results to foster learning. In order to answer this question, we conducted a within-subject study with 40 students to compare abstract and concrete onboarding messages for a treemap visualization. The results show that (1) concrete onboarding messages are more helpful than abstract, whereas the length of the abstract messages is ranked higher; (2) abstract onboarding messages lead to more valuable descriptions; and (3) either concrete or abstract onboarding messages can lead to high valuable insights.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual analysis often have difficulties in correctly reading and interpreting information from novel visualization idioms. Visualization onboarding can support novices in learning how to use new digital technologies. Therefore, we developed an interactive step-by-step guide and applied the method to four visualization techniques—a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. Results using Amazon Mechanical Turk workers show that there is a need for onboarding, especially for more complex visualization techniques. We further investigated the perception and rating of a scrollytelling and a video tutorial for the most unfamiliar visualization—the parallel coordinates plot. A comparison between the three onboarding methods indicates that participants appreciated the easy-to-understand examples, the precise wording of the onboarding messages in a step-by-step manner, and the introduction of interaction concepts by highlighting the most relevant information over all onboarding methods. The video tutorial supported the introduction of unknown interaction techniques best.

Abstract Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four‐year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story‐finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/

Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace "diff" tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selected tables. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios.