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From Instruction to Insight: Exploring the Functional and Semantic Roles of Text in Interactive Dashboards

Nicole Sultanum, Vidya Setlur

TL;DR

This work reframes text as a core component of interactive dashboards, not merely as annotations. Through a survey of 190 dashboards and 13 expert interviews, it characterizes text components, semantic levels, and functional roles, and introduces 12 heuristics to guide text usage. The study demonstrates how textual elements support navigation, context, reading order, and user guidance, and outlines opportunities in authoring tools, content generation, and personalized storytelling. By elevating text to a first-class citizen in dashboard design, the paper offers practical guidance for authors and tool designers to enhance analytic communication and user experience.

Abstract

There is increased interest in the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are: 1) we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns; 2) we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, etc; 3) we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.

From Instruction to Insight: Exploring the Functional and Semantic Roles of Text in Interactive Dashboards

TL;DR

This work reframes text as a core component of interactive dashboards, not merely as annotations. Through a survey of 190 dashboards and 13 expert interviews, it characterizes text components, semantic levels, and functional roles, and introduces 12 heuristics to guide text usage. The study demonstrates how textual elements support navigation, context, reading order, and user guidance, and outlines opportunities in authoring tools, content generation, and personalized storytelling. By elevating text to a first-class citizen in dashboard design, the paper offers practical guidance for authors and tool designers to enhance analytic communication and user experience.

Abstract

There is increased interest in the interplay between text and visuals in the field of data visualization. However, this attention has predominantly been on the use of text in standalone visualizations or augmenting text stories supported by a series of independent views. In this paper, we shift from the traditional focus on single-chart annotations to characterize the nuanced but crucial communication role of text in the complex environment of interactive dashboards. Through a survey and analysis of 190 dashboards in the wild, plus 13 expert interview sessions with experienced dashboard authors, we highlight the distinctive nature of text as an integral component of the dashboard experience, while delving into the categories, semantic levels, and functional roles of text, and exploring how these text elements are coalesced by dashboard authors to guide and inform dashboard users. Our contributions are: 1) we distill qualitative and quantitative findings from our studies to characterize current practices of text use in dashboards, including a categorization of text-based components and design patterns; 2) we leverage current practices and existing literature to propose, discuss, and validate recommended practices for text in dashboards, embodied as 12 heuristics that underscore the semantic and functional role of text in offering navigational cues, contextualizing data insights, supporting reading order, etc; 3) we reflect on our findings to identify gaps and propose opportunities for data visualization researchers to push the boundaries on text usage for dashboards, from authoring support and interactivity to text generation and content personalization. Our research underscores the significance of elevating text as a first-class citizen in data visualization, and the need to support the inclusion of textual components and their interactive affordances in dashboard design.
Paper Structure (28 sections, 4 figures, 1 table)

This paper contains 28 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dashboard Characteristics and Guidelines
  4. Text and Chart Integration
  5. Dashboard Authoring and Search
  6. Methodology
  7. Dashboard Corpus
  8. Dashboard Analysis
  9. Expert Interviews
  10. Characterizing Current Text Practices
  11. Text Components
  12. Semantic Levels
  13. Outlining Dashboard Text Practices
  14. Dashboard Text Practices are Based on Experience
  15. Text Components and Semantic Levels
  16. ...and 13 more sections

Figures (4)

  • Figure 1: Text components in various dashboard snippets. Figure 2(a) (by https://public.tableau.com/app/profile/joshua.jong6344/vizzes, https://public.tableau.com/views/assignment1_16295362252850/Dashboard13): (A) The title provides an overarching theme; (B) The subheading offers additional context and credits the creator; (C) Section headers categorize the dashboard into thematic areas; (D) chart titles denote the focus of each visualization; (E) chart annotations explain specific data points or trends for clarity; and (F) Content blocks supply narrative insights, interpreting the ethnic composition changes over time and providing definitions for terms such as 'Bumiputera' and the 'Indian' diaspora. Figure 2(b) (by https://www.emilydepadua.com, https://public.tableau.com/app/profile/emily.de.padua/viz/AllByMyself/AllbyMyself): Features a prominent percentage highlighting days spent alone, a narrative text block reflecting personal reflections on quarantine, and a (G) custom tooltip displayed on hover, showing an interactive timeline charting average daily mood. Figure 2(c) (by https://kevinwee.com, https://public.tableau.com/app/profile/kevin.wee/viz/Data23DemoCreativeUsesofImagesinaTableauDashboard_16828225482900/Overview): (H) Metadata blocks, indicating author, data source, and when the dashboard was last updated; (I) text-data summary blocks featuring total profit and sales, and (J) interaction guidance on tooltip hover. Figure 2(d) (by https://excelfort.com, https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Accounts-Receivable-Dashboard/td-p/2530321): (K) A strip of text-data summaries featuring icons; and (L) a data table featuring embedded graphics.
  • Figure 2: A snippet of a dashboard displaying text components at varying levels of semantic detail (by https://kowvincent.com/, https://public.tableau.com/views/Assignment1_16303281835750/Dashboard2). Level 1: basic chart descriptions and titles, indicating visible data dimensions like "Gender & Population Graph." Level 2: statistical concepts shown as BANs providing numerical summaries such as "17M Total Population." Level 3: perceptual insights on population segments elucidate more complex data features. Level 4: contextual and domain-specific insights offer additional information regarding accommodation affordability in New York City.
  • Figure 3: Tooltip featuring LV2-LV3 content (by https://uk.linkedin.com/in/acotgreave, https://public.tableau.com/app/profile/acotgreave.tableausoftware.com/viz/ArkhamDBDataDrivenDeckBuidling/DeckBuilderpopularcards2).
  • Figure 4: Dashboard depicted in Fig. \ref{['fig:transport_dashboard']}https://public.tableau.com/app/profile/kevin.wee/viz/Data23DemoCreativeUsesofImagesinaTableauDashboard_16828225482900/Overview with active instruction overlay.