Dashboard Design Patterns

1 INTRODUCTION

• Existing guidance covers broad principles, but designers still lack applicable support for rapid dashboard design and face many choices about data, representation, and structure.The need is especially timely as dashboards increasingly target wider non-expert audiences and designers without visualization or interface-design backgrounds.Source: §1 · p. 1
• A review of 144 dashboards formalized 42 patterns in eight groups, spanning dashboard content and composition.The groups cover data information, meta information, visual representation, page layout, screenspace, structure, interaction, and color.Source: §1 · p. 2
• The patterns are combined into six dashboard genres with shared characteristics and common design patterns.The genres relate to other information-visualization genres, including Multiple Coordinated View systems and infographics.Source: §1 · p. 2
• In a two-week workshop with 23 participants, the pattern collection and terminology helped novice and advanced designers streamline individual and co-design.Participants brought their own data and dashboard challenges; discussions surfaced difficulties balancing information amount, audience, and context.Source: §1 · p. 2
• The authors position the patterns as design knowledge for informing dashboard creation, teaching, and possible structured evaluations of dashboard effectiveness.Detailed pattern descriptions, design guidance, and workshop materials are available online.Source: §1 · p. 2

2.1 Characterizing Dashboards

• Dashboard definitions range from a single-screen, at-a-glance view of important information to a purposefully created lens for communicating and engaging with data.Dashboards serve strategic, tactical, and operational purposes, among others.Source: §2.1 · p. 2
• The range of dashboard purposes and audiences suggests that design solutions should suit the audience, context, and tasks.Reported uses include organizational monitoring, planning, communication, motivation, learning, and personal analytics.Source: §2.1 · p. 2
• There is no consensus on what counts as a dashboard, so the authors adopt an open-minded scope to examine wider design possibilities.The distinction between monitoring dashboards and analytical displays also entails a tradeoff between single-screen detail and interaction for exploration.Source: §2.1 · p. 2

2.2 Design Guidelines

• High-level guidelines address issues such as clutter and information load, but leave practical tradeoffs about content, interaction, page size, and personalization unresolved.The authors report that these questions were not covered in the literature they reviewed.Source: §2.2 · p. 2
• Dashboard design patterns complement high-level guidelines by making common solutions and concrete design options available for discussion.The discussion also points to user-centered, iterative design with a shared understanding of options.Source: §2.2 · p. 2

2.3 Design Patterns

• The dashboard patterns extend earlier work on dashboard features and are derived from analysis of 144 dashboards.They are descriptive rather than prescriptive, and can support ideation, discussion, and structured evaluation.Source: §2.3 · p. 2

3 DASHBOARD DESIGN PATTERNS

• Qualitative coding of 144 dashboards produced 42 design patterns grouped into eight categories, focusing on interface building blocks rather than an exhaustive taxonomy.The corpus began with 83 dashboards, excluded three, and added 64; 61% had an interactive version available for coding.Source: §3 · p. 3

3.1 Content Design Patterns

• Content patterns describe data information, meta information, and visual representations, while composition patterns describe page layout, screenspace, cross-page structure, interaction, and color.The reported pattern percentages are not mutually exclusive and therefore may not sum to 100%.Source: §3 · p. 3

3.1.1 Data Information Patterns

• Data-information patterns range from detailed datasets to more abstracted summaries, thresholds, derived values, and single values.Detailed datasets were observed in 94% of dashboards, aggregation in 69%, thresholds in 42%, and single values in 88%.Source: §3.1.1 · p. 3

3.1.2 Meta Information Patterns

• Meta-information patterns provide context and explanation, including data sources, descriptions, update information, timestamps, and annotations.The coding distinguishes explicit metadata from examples where context may be understood implicitly.Source: §3.1.2 · p. 3

3.1.3 Visual Representation Patterns

• Visual representations span detailed charts for precise reading and miniature charts that provide a quick trend impression without axes, labels, or tick marks.Gauges and progress bars place a single value in context, while pictograms can represent data or index nearby values.Source: §3.1.3 · p. 3

3.2 Composition Design Patterns

• Composition patterns concern how dashboard components are combined, fitted to screenspace, distributed across pages, made interactive, and colored.Page layout is one of five composition aspects identified by the authors.Source: §3.2 · p. 4

3.2.1 Page Layout Patterns

• Page-layout patterns classify how higher-level widgets are organized and grouped on a page, where a widget combines visual representations with a title and possible metadata.The paper identifies open, stratified, table, grouped, and schematic layouts.Source: §3.2.1 · p. 4, Open layouts · p. 4, Figure 3 · p. 4

Open layouts

• Stratified layouts present widgets in a top-down ordering (49%) and can emphasize information at the top over other information.Source: Open layouts · p. 4
• Table layouts use semantically meaningful rows and columns to repeat information, while grouped layouts connect widgets with a specific relation, often under a common title.Table layouts were reported in 19% of dashboards and grouped layouts in 33%.Source: Open layouts · p. 4
• Layout patterns are not exclusive: dashboards combine them, such as stratified layouts with open or grouped layouts.Schematic layouts, found in 1% of dashboards, arrange widgets in relationships such as spatial layouts, networks, or workflows.Source: Open layouts · p. 4

3.2.2 Screenspace Patterns

• Screenspace patterns address fitting content on a page, which may be split across pages or overflow beyond the single page visible at a time.In 44% of dashboards, all page content fits on screen without scrolling or tooltips.Source: §3.2.2 · p. 4, Screenfit · p. 4

Screenfit

• Overflow lets a page exceed available screenspace, usually through vertical scrolling, with potentially no limit to the overflow size.Source: Screenfit · p. 4
• Detail-on-demand reveals extra content through interactions such as mouseover tooltips or pop-ups, avoiding the need to fit that content alongside other elements.Source: Screenfit · p. 4
• Parameterization lets viewers specify visible information, such as data or filters, but requires manual input and typically shows only one state at a time.Source: Screenfit · p. 4
• Multiple pages split content across separately navigable pages; tabs can show similar views with different data, reducing information on each page.Source: Screenfit · p. 4, Figure 4 · p. 4

3.2.3 Structure Patterns

• A single-page dashboard was the most common structure (61%), and may rely on screenfit patterns to fit desired content.Source: §3.2.3 · p. 4
• Parallel structures (16%) repeat layouts, data, and visual representations, while hierarchical structures (19%) support drill-down through pages with gradually increasing detail.Source: §3.2.3 · p. 4
• Other structures (8%) capture structural relationships beyond the single-page, parallel, and hierarchical arrangements.Source: Open structures · p. 4, §3.2.3 · p. 4

• Only 4% of dashboards had no interaction, and the four major interaction uses are supported by often-overlapping components such as tabs, sliders, and drop-down menus.Source: §3.2.4 · p. 4
• Exploration was the most common interaction use (89%), letting users inspect data, obtain or view it differently, and explore relations between data.Source: Exploration · p. 4

Exploration

• Finding or focusing on specific data (55%) is supported by actions such as searching values or filtering to retain relevant information.Source: Exploration · p. 5
• The cited passage reports that Navigation (76%) enables navigation between pages of a dashboard or screen features, supported through scrollbars, navigation buttons, page tabs, and hyperlinks.Source: Exploration · p. 5
• Personalization (23%) lets viewers reconfigure dashboard information to suit preferences or tasks, including adding, resizing, or reordering visual representations.Source: Exploration · p. 5

3.2.5 Color Patterns

• Color schemes primarily encode data categories or scales (80%), while unique colors for groups or facets (35%) maintain dashboard-wide consistency and familiarity.Source: §3.2.5 · p. 5
• Semantic colors (26%) convey meanings such as good or bad status, often alongside gauges or progress bars that indicate thresholds.Source: §3.2.5 · p. 5
• Emotive color schemes (6%) add aesthetic strength and develop an emotive response, appearing particularly common in infographic-like dashboards.Source: §3.2.5 · p. 5

Static Dashboard

• Static dashboards (21%) are single-page, no-interaction displays; few classic examples appeared in the corpus, amid digital interaction and varied screen sizes that encourage responsive structures.Static dashboards often use concise representations such as single values.Source: §4 · p. 5

Analytic dashboards

• Analytic dashboards (73%) provide complete, often interactive visualizations for exploring larger and more detailed datasets, but their views need not share data or a common task.The paper relates this genre to Faceted Analytic Displays and Coordinated Multiple Views, while distinguishing dashboards from coordinated views.Source: §4 · p. 5

Magazine Dashboards

• Magazine dashboards (2%) interleave visualizations with commentary and storytelling, often across pages, and require editorial oversight to keep the story consistent with changing data.Visualizations may update automatically, but written content is also frequently updated as the story changes.Source: §4 · p. 5

Infographic Dashboards

• Infographic dashboards (6%) embellish data with non-data media and annotations to tell a story, often presenting static snapshots that may extend beyond the screen.Their tailored artistic elements take additional design time and make them less suited to frequently changing data.Source: §4 · p. 5

Repository Dashboards

• Repository dashboards (17%) collect many charts across pages, often with extensive metadata, regular updates, and links for exploring or downloading open data.They may impede comparison across views and often lack textual explanation beyond metadata.Source: §4 · p. 6

Embedded Mini Dashboards

• Embedded mini dashboards (4%) occupy little screen space and use concise visualizations with interactive navigation and parameterization, often linking to a more in-depth dashboard.The example is embedded in a news page and links to a magazine or infographic dashboard for further exploration.Source: §4 · p. 6

5 DISCUSSING DESIGN TRADEOFFS

• Design patterns address choices designers can control—such as screenspace, structure, layout, color, and visual representations—within constraints set by audiences, devices, and use scenarios.The discussion draws on the authors’ experience creating over 7,000 Covid-19 dashboards in the UK and workshop discussions.Source: §5 · p. 6

5.1 Patterns to Balance Design Parameters

• A time-series example shows how numerical, trend, and detailed visual encodings lose different information; combining abstraction levels can also increase screen use and cognitive cost.The authors describe risks of confusion or misinterpretation from information loss, which can be counterbalanced with interaction or multiple pages.Source: §5.1 · p. 6
• Reducing screenspace, abstraction, page count, or interaction requires increasing one or more other parameters, so dashboard design negotiates tradeoffs rather than minimizing every parameter at once.The model treats these competing parameters as a stress function and uses design patterns to discuss ways to minimize that stress.Source: Figure 6 · p. 6, §5.1 · p. 6
• Interactive parameterization lets viewers choose relevant information, while static displays suit cases where interaction is unnecessary but demand careful abstraction within available space.The broader design space includes exploration tools such as tooltips, filtering, and search, as well as navigation across pages.Source: §5.1 · pp. 6–7

5.2 Curated Dashboards vs. Data Collections

• Curated dashboards selectively translate data for a defined purpose, while data collections expose larger volumes for users to find information relevant to more open-ended tasks.Curated dashboards tend toward single-screen layouts and limited parameterization; data collections commonly use pagination to maximize available information.Source: §5.2 · p. 7

6 DASHBOARD DESIGN WORKSHOP

• The two-week online workshop guided participants through dashboard design decisions and aimed to refine the pattern collection and the discussion of design tradeoffs.Participants worked toward interactive mockups in Figma rather than implementing dashboards in tools such as Tableau or Power BI.Source: §6 · p. 7

6.1 Setup and Participants

• Twenty-three participants from varied disciplines and sectors designed dashboards around their own data and work contexts; about half had prior dashboard-design experience.For almost all participants, this was their first deliberate, structured engagement with dashboard design.Source: §6.1 · p. 7

6.2.1 Workshop Discussions

• Discussions repeatedly addressed information overload, screenspace, color consistency, accessibility, and matching information and interaction to audience expertise.Participants noted that individual values need comparison context; past data, miniature charts, detailed data, trend arrows, or indicators can provide it, especially for novices and casual users.Source: §6.2.1 · pp. 7–8

6.2.2 Reflections on the Design Process

• Participants’ reflections emphasized starting simply, focusing each graph on one question, and balancing simplification against imposing the designer’s story.They also described using filters and parameters to reduce the number of static visuals shown at once.Source: §6.2.2 · p. 8

6.2.3 Pattern and Workshop Utility

• Participants valued individual feedback, peer discussion, and the workshop’s framework and guidance; they described patterns as helping narrow options, support co-creation, and review design decisions.Some participants used the patterns first as design guidance and later as a checklist.Source: §6.2.3 · p. 8

7.1 Design Knowledge

• The paper introduces dashboard design patterns and genres as reusable design knowledge, while keeping the boundary between dashboards and other visualization forms deliberately blurred.The patterns establish terminology and describe common solutions; genres illustrate common combinations of patterns.Source: §7.1 · pp. 8–9
• The 144-dashboard corpus extends earlier work, but online abundance makes a systematic survey impossible and the qualitative patterns are not prescriptions for good design.The patterns and their frequencies represent the dashboards analyzed; the authors invite future work to complement the collection.Source: §7.1 · p. 9, §7.5 · p. 9

7.2 Design Tradeoffs

• Contradictory guidance across users, tasks, contexts, and devices makes tradeoffs inevitable, while the costs and benefits of specific choices remain open questions for empirical study.The authors describe their tradeoff discussion as a first formal treatment, partially informed by information theory.Source: §7.2 · p. 9

7.3 Dashboard Design Processes

• The proposed design process assumes requirements analysis has identified users, tasks, and datasets, then introduces pattern groups through iterative negotiation of tradeoffs.Source: §7.3 · p. 9

7.4 Tool and Technical Support

• Dashboard-authoring tools could guide design choices with pattern and genre templates or recommenders, but automation requires formal design rules that need further study.Source: §7.4 · p. 9
• Only a small number of dashboards support personalization, and responsiveness should address data abstraction and visual encodings as well as screen size.The authors describe responsiveness as potentially turning a static dashboard into an analytic one.Source: §7.4 · p. 9

7.5 Limitations

• The patterns reflect a curated sample and close coder agreement, but the collection is qualitative and aims to inform future studies rather than identify good designs.Source: §7.5 · p. 9

8 CONCLUSION

• Dashboard patterns map common solutions involving data abstraction, screenspace organization, grouping, relations, and interaction, without requiring a strict dashboard definition.Source: §8 · p. 9
• Patterns, tradeoffs, and genres offer structured guidance for deliberate design across questions of audience, data extent, usage scenario, and medium.Source: §8 · p. 9
• The framework does not determine what makes a good dashboard, which depends on subjective and domain-specific factors; the authors call for research on genre strengths and weaknesses.Source: §8 · p. 9