1060 Research Nicole Sultanum and Vidya Setlur are with Tableau Research.
E-mail<EMAIL_ADDRESS>
# From Instruction to Insight: Exploring the Functional and
Semantic Roles of Text in Interactive Dashboards

Nicole Sultanum0000-0001-8608-1427 and Vidya Setlur0000-0003-3722-406X

###### Abstract

There is increased interest in understanding 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 (such as
text annotation overlays) 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 threefold. First, 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. Second, we leverage current
practices and existing literature to propose, discuss, and validate
recommended practices for text in dashboards, embodied as a set of 12
heuristics that underscore the semantic and functional role of text in
offering navigational cues, contextualizing data insights, supporting reading
order, among other concerns. Third, 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.

###### keywords:

Text, dashboards, semantic levels, metadata, interactivity, instruction,
description, takeaways, conversational heuristics.

Examples of interactive dashboards that demonstrate how descriptive text
elements can guide users through interactive data exploration and communicate
key insights. Left
105https://public.tableau.com/app/profile/kevin.wee/viz/USHigherEdInstituteswithDataAnalyticsProgram/DashboardKevin
Weehttps://kevinwee.com/: An analytic-style dashboard that provides
descriptive and instructional text for the geographical distribution and
profiles of U.S. higher education institutions with data analytics programs.
Users can interact with the map to discover information about each institute,
with supplementary bar charts and histograms providing details on the degrees
offered, campus settings, student enrollment sizes, average net prices, and
graduation rates. Right 173https://community.fabric.microsoft.com/t5/Data-
Stories-Gallery/How-Covid-19-strikes-the-bridal-
industry/td-p/1332158Infoceptshttps://www.infocepts.ai/: An infographic-style
dashboard showing changes in appointments, total revenue, and sales, with
qualitative assessments to narrate the broader effects on shopping behavior
and wedding planning. Visual elements, along with quantitative metrics and
descriptive narratives, are employed to draw attention to data trends and
provide an overview of the industry’s adaptation to virtual platforms.

Introduction

Dashboards have become a prevalent analytical artifact for visualizing and
interacting with data across a variety of domains. From their original
conception as glanceable visual monitoring systems for time-critical decision
making [13, 48], dashboards have evolved into versatile data communication
tools that support both analytical and storytelling purposes [45, 2]. This
shift in utility has led to an extensive body of research aimed at cataloging
and categorizing dashboards, focusing on their communication goals [45],
dashboard intent [42], their interaction mechanisms [62], and conversational
design heuristics [48], all aspects that underscore the growing importance of
dashboards to support effective communication of data to their intended
audience.

Among the diverse components that make up a dashboard, text elements such as
titles, labels, captions, and data descriptions play a pivotal role in this
communication [13, 64, 25, 47]. An assessment of public dashboards in the wild
found a majority (55%) contained blocks of text [52], stressing the prevalence
of textual elements in dashboard design. There has been increased interest in
studying relevant uses of text in the context of individual charts [58, 20,
24], but text use in dashboards has received relatively little attention so
far. And arguably, it is a timely concern. The current repertoire of dashboard
generation tools focuses on automating chart and layout creation, placing less
emphasis on text generation and placement [30, 16]. With the rise of large
language models, this gap can be more easily filled, but without a solid
understanding of practices and expectations for text, we are ill-equipped to
design appropriate guardrails that can steer dashboard content generation in
useful, meaningful directions.

Acknowledging the growing relevance of text in data visualization and the
current landscape of generative AI for text, our work aims to understand and
formalize the use of text in dashboards, and to provide guidance for dashboard
authors and dashboard tool designers alike. Our approach was informed by (a) a
survey and qualitative analysis of 190 publicly available dashboards to
catalog features of text and characterize their impact on dashboard design and
communication, and (b) design review sessions with 13 dashboard creators to
reflect and learn from on their personal practices for text use (§2.2). Our
contributions are threefold. First, we (i) outline current practices for text
in dashboards based on dashboard analysis and expert feedback (§3), which
helped inform a typology of text elements in dashboards and a compilation of
text use patterns. Second, we discuss (ii) recommended practices for text use
in dashboards, based on an adaptation on Setlur et al.’s dashboard heuristics
[48] and validated by experts (§4). Third, we outline (iii) future
opportunities for the usage of text in dashboards, reflecting on emerging
challenges for text use and potential solutions based on the state of the art
in data visualization research (§5). Our work sheds light onto the functional,
structural, and semantic aspects of text and reflects on its vital role in
dashboards for analytical and navigation support. Our investigation shows that
text is much more than a mere accompaniment to visuals but rather a
fundamental enabler for narrative, explanation, and insight discovery.

##  1 Related Work

Our work builds on several lines of work: dashboard characteristics &
guidelines, text & chart integration, and dashboard authoring & search.

###  1.1 Dashboard Characteristics and Guidelines

The role of dashboards has evolved to support the increasing complexity of
user needs and the recognition that dashboards serve not just as a means for
data consumption but as platforms for data conversation. Tory et al.’s [62]
concept of data conversations highlights the diverse communication goals of
dashboard users, emphasizing tasks such as summarizing, monitoring, and
predicting insights.

Dhanoa et al.[10] and Sarikaya et al.[45] identify key elements that
contribute to an effective dashboard, including the importance of guiding
users through the data exploration process with clear narratives and
structured interaction pathways. Bach et al. [2] extend this understanding by
identifying design patterns and dashboard genres that cater to varied user
intents and interaction styles, where text plays a key role in data narration
and guiding user exploration. Setlur et al. [48] present a set of $39$
dashboard design heuristics grounded in the Gricean maxims of cooperative
conversation, a framework that views dashboard interaction as a form of
analytical conversation. The heuristics consider the role of text in
facilitating analytical conversation, ranging from legibility and
descriptiveness, effective use of iconography, and the provision of
navigational cues and breadcrumbs. Aspects of communication that dashboard
text contributes to are acknowledged and studied in these works, but not how
text specifically influences this communication.

Our work is also informed by a long history of design heuristics for
interfaces and visualizations that include Nielsen’s ten usability guidelines
[38, 39] and Shneiderman et al.’s eight golden rules for interface design and
evaluation [51], which provide directives for user-centered design. The
examination of usability heuristics for evaluating visualizations by Tory &
Möller [63] emphasizes the benefit of incorporating both usability and
visualization expertise in creating effective analytical interfaces.
Subsequent work has aimed to develop and evaluate visualization-specific
heuristics [14, 65, 61, 75, 11, 8], expanding upon the principles of user-
centered design to cater specifically to the unique challenges and
opportunities presented by visual data representation. Resources by Few [13],
Yigitbasioglu[71], and Wexler [68] introduce a user-centered design
perspective to dashboard design, complemented by frameworks that focus on user
goals and intents [27, 31]. Together, these works guide visualization design
towards more meaningful user experiences and support users’ cognitive
processes [67].

We build upon this body of research by examining the role of text in
dashboards along with its categories, semantic levels, and functional roles in
guiding and informing users. Through an extensive dashboard coding exercise,
we uncover the interplay between text and dashboard visualizations to support
analytical and narrative communication goals.

###  1.2 Text and Chart Integration

Recent research has explored the integration of text and charts in
visualizations through various lenses, offering insights into how text can
influence the perception and understanding of visualized data.

Kim et al. [20] conducted a crowdsourced study to understand how readers
synthesize information from both charts and captions, finding that the
emphasis on high-prominence features within both elements led readers to
identify those features as key takeaways. Their research underscores the
importance of coherence between visual and textual elements and how external
context can enhance the reader’s comprehension of the chart’s message.
Building on these insights, Lundgard and Satyanarayan [34] proposed a four-
level semantic categorization of text content designed to enhance
visualization accessibility. Their framework distinguishes between perceiver-
independent descriptions, such as objective chart specifications, and
perceiver-dependent insights, including interpreted observations and
contextual knowledge. Recent work broadens the scope of accessible and
inclusive data representation by championing the parity of visualization,
textual description, and sonification in multimodal data analysis [74].

Further exploring the role of text in visualizations, Stokes [58] observed
that study participants favored heavily annotated charts over less annotated
charts or text alone. This preference highlights the added value of text in
aiding data interpretation, with emphasis on how different types of semantic
content — statistical, relational, elemental, or encoded — impact the
takeaways drawn by readers. This finding aligns with Stokes and Hearst’s
advocacy for treating text as co-equal to visualization, urging researchers to
focus on the readability and integration between these two modalities [57].
Their call to action reflects a growing body of work, including Ottley et al.
[41], exploring the critical role of text in visual analysis and its
importance for conveying key messages to the reader. Brath provides for a more
integrated and text-centric perspective on visualization, suggesting new
avenues for enhancing data communication through textual integration [5].

Eye-tracking studies by Borkin et al. [4] have shown that participants were
more likely to fixate on and recall textual content surrounding
visualizations, such as titles and labels. Similarly, Kong et al. [22, 23]
investigated the influence of titles on the perceived message of
visualizations, discovering that slanted framings (e.g., emphasizing only part
of the chart’s message) significantly impacted recall and interpretation. Zhi
et al. [73] found that explicit text-chart integration in data-driven stories
may increase recall and engagement. Several tools build on these synergies,
e.g., FacetNotes [3], a novel concept for integrating text annotations
directly with data points on interactive dashboards; Kori [29], a tool to
support identification of text-chart links; and VizFlow [60], a tool that
leverages text-chart links to generate dynamic data-driven articles.

However, the preference for integrating text and visual elements is not always
universal. Hearst & Tory [15] investigated visualization preferences in
conversational interfaces with chatbots and found a notable division in
participant preferences. Nearly half of the participants expressed a
preference against viewing charts, while those who did prefer charts also
favored the inclusion of additional contextual data within the visual
representation. Based on these foundational studies, we specifically focus on
the dynamic interplay between text and chart features in the context of
interactive dashboards. Unlike previous research that primarily considers
static visualizations or singular aspects of text integration, our work
investigates how authors specifically incorporate text elements in dashboards,
exploring both the role of text and their various forms.

###  1.3 Dashboard Authoring and Search

Understanding the analytical intent for authoring dashboards is crucial for
effectively integrating text and chats for enhancing the overall communicative
effectiveness of dashboards [6, 46]. Recent work by Pandey et al. [42]
proposed a set of dashboard intents and objectives accompanied by specific
objectives like summarizing measures, comparing categories across dimensions,
and displaying univariate summaries, to support dashboard composition through
auto-generated variations of intent and objectives. Srinivasan and Setlur [55]
extend the capability for dashboard composition through a natural language
interface, enabling authors to generate and arrange dashboard views based on
user-provided linguistic utterances. Machine learning techniques have also
been explored for dashboard authoring and search. Ma et al.[35] and Wu et
al.[69] propose deep learning-based methods to assist in dashboard generation
and chart view suggestions, respectively, demonstrating the potential of ML to
expedite dashboard prototyping and enhance the generation process. Similarly,
Deng et al. [9] introduce DashBot, leveraging reinforcement learning to
automate dashboard generation, focusing on the creation of valid dashboards
that facilitate data insight discovery and support direct manipulation of
recommendations. However, this prior work tends to focus less on the
systematic integration of text beyond basic labels and titles in dashboard
generation, presenting an opportunity to explore the role of text to further
enhance the dashboard’s communicative effectiveness.

Oppermann et al. [40] explore leveraging text-based content within dashboards
to aid in dashboard search and recommendation for specific analytical
questions. Their work highlights the challenge of sparse text presence in
dashboards and the necessity of including non-visible text for context, thus
offering a pathway toward more meaningful recommendations based on textual
content. EmphasisChecker [19] explores the integration of text and charts more
directly in the authoring experience by identifying mismatches between the
visual emphasis of charts and the textual emphasis of captions. Our work
extends beyond prior research that primarily focused on dashboard intents,
objectives, and composition; rather, we explore the specific interplay between
text and visualizations, wherein text is not merely an accessory to visual
elements but a primary medium for communication within dashboards, conveying
key insights to the intended audience.

##  2 Methodology

Our efforts to characterize text use in dashboards are threefold: to identify
relevant features of current text use, to provide recommendations for text
use, and to identify opportunities for improvement. To support these goals, we
performed two elicitation studies. First, we surveyed and analyzed a large
collection of publicly available dashboards, which informed a typology of
dashboard text elements and provided samples to illustrate best practices.
Second, we conducted interviews with 13 dashboard designers, which helped
contextualize and validate findings from our dashboard survey and inform
challenges of text use in dashboards, which in turn helped inform research
opportunities for text use in dashboards. We detail our study methodology in
the following subsections, and our cross-cut findings are presented throughout
our investigation goals to describe current practices (§3), recommended
practices (§4), and opportunities (§5).

###  2.1 Dashboard Corpus

We compiled a large and diverse collection of 190 publicly available
dashboards (Tableau (116), Power BI (50), and other web visualization tools
(24)) sourced four ways:

1\. Dashboard corpora from prior work (52). We included dashboards listed in
prior surveys [2, 45, 48] that were both in English and publicly available,
yielding 52 dashboards (from a total of 173). While these dashboards cover a
relatively diverse range of platforms and genres, they have been
opportunistically sampled, which could suggest a skewed selection of
dashboards from limited sources and topics.

2\. Curated sample from Tableau Public (74). To broaden diversity, we gathered
dashboards from a large dataset of Tableau Public [1] dashboards featured in
Srinivasan et al. [52] (with assistance from the authors). From an initial
random sample of dashboards, we filtered for dashboards in English featuring
five or more dashboard zones (i.e., charts, filters, legends, text, multimedia
blocks), with at least one text zone and one chart zone. This led to a diverse
set of 74 dashboards, spanning a broad range of genres, topics, communication
goals, analytical complexity, and levels of refinement.

3\. Popular Power BI dashboards (43). We also included a third set of 43 Power
BI dashboards, particularly those highly regarded in the “Top Kudos” category
(i.e., high community value) of the Power BI Data Stories Galleries [44] to
ensure platform diversity and to analyze best practices in dashboard design
across different software ecosystems.

4\. Dashboards from expert interview participants (21). We included a fourth
set of 21 dashboards from interviews (§2.3) that were authored by expert
interview participants and handpicked by them as meaningful examples of text
use in their dashboard practice. While most (19) are Tableau dashboards, they
present a balanced coverage of genres and topics, from business dashboards to
infographics for the general public.

###  2.2 Dashboard Analysis

The first author conducted an initial round of inductive analysis on a subset
of dashboards to flesh out a preliminary typology of text-based components.
Both authors were then involved in a follow-up deductive stage to refine these
codes and incorporate other text-relevant features from past literature that
encode dashboard semantics, including dashboard genres [2], dashboard goals
[45] and semantic levels of data-driven content [34]. This resulted in a code
book for independent coding covering a typology of text components and
relevant dashboard-level features. A detailed breakdown of our derived
typology is presented in §3.

We also compiled a set of heuristics for text usage in dashboards. Based on
the work by Setlur et al. [48] that proposes 39 heuristics for dashboard
design to foster analytical conversation, we reframed their heuristics from a
perspective of text use, leading to a set of 12 heuristics for text use. These
heuristics were appended to the code book alongside a detailed rubric listing
4 levels of application for each heuristic: strong application, weak
application, weak violation, strong violation. A detailed discussion on the
derived heuristics is presented in §4.

Using the code book, both authors conducted an independent assessment of
dashboards in our corpus to extract meaningful text features and assess the
application of the heuristics. Authors met regularly during the coding process
to resolve disagreements and refine code book descriptions and rubric. In
parallel, recurrent and interesting applications of text use were also
cataloged. Unanimous consensus was reached on all features for the 190
dashboards in our collection.

Outcomes of this coding effort showcased the diversity of our corpus,
featuring a broad range of dashboard genres [2] (analytic (59%), infographic
(25%), static (10%), repository (8%) and magazine (4%)) and dashboard goals
[45] (decision making (40%), awareness (22%), motivation & learning (38%)). It
also contributed a more systematic look over patterns of text use (§3),
meaningful examples to showcase patterns (§4), and inspiration for future
opportunities for text use in dashboards (§5). Our coded dashboard corpus and
code book can be found in Supplemental Materials. We reference specific
dashboard instances by their corpus (#ID) (with embedded access links).

(a) A dashboard visualizing the population and demographic trends in Malaysia.

(b) A personal dashboard tracking the experience of social distancing during
the COVID-19 pandemic.

(c) A business dashboard for sales and profit analysis.

(d) A business dashboard for sales monitoring.

Figure 1: Text components in various dashboard snippets. Figure 2(a)
87https://public.tableau.com/views/assignment1_16295362252850/Dashboard13Joshua
Jonghttps://public.tableau.com/app/profile/joshua.jong6344/vizzes: (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)
136https://public.tableau.com/app/profile/emily.de.padua/viz/AllByMyself/AllbyMyselfEmily
De Paduahttps://www.emilydepadua.com: 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)
59https://public.tableau.com/app/profile/kevin.wee/viz/Data23DemoCreativeUsesofImagesinaTableauDashboard_16828225482900/OverviewKevin
Weehttps://kevinwee.com: (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) 156https://community.fabric.microsoft.com/t5/Data-Stories-
Gallery/Accounts-Receivable-Dashboard/td-p/2530321Fowmy
Abdulmuttalibhttps://excelfort.com: (K) A strip of text-data summaries
featuring icons; and (L) a data table featuring embedded graphics.

###  2.3 Expert Interviews

To complement our dashboard analysis efforts, we conducted interview sessions
with experienced dashboard creators. Our goal was to validate observations of
text use from the dashboard analysis, elicit rationale for design patterns,
document common practices for text use, and validate our proposed set of
heuristics.

We recruited 13 participants ($P1$-$P13$) from various channels, including
social media, specialized community forums, targeted recruitment, and word of
mouth. Most participants create dashboards professionally; listed occupations
include business intelligence (BI) managers, data analysts, BI consultants,
and students in data-relevant fields. All participants had at least one year
of experience producing dashboards, with 10 reporting at least five years of
professional experience on the topic. Tableau (11/13) and Power BI (5/13) were
the most commonly cited dashboard platforms, but several participants also
used other tools (5/13), and many stated being conversant in more than one
tool (6/13).

Sessions were one hour long and consisted of a semi-structured interview on
text practices for dashboards followed by a feedback segment on the
heuristics. Prior to the session, participants were asked to provide links to
dashboards they authored that made for interesting discussion on the use of
text (and were later included in our fourth sample set, §2.1). At the session,
we asked them to share design rationale on text use for their dashboards,
including semantics, level of detail, text formatting choices, layout and
reading order, user guidance, dynamic text, and accessibility. We also
inquired about practices and challenges they generally face with text. We then
presented our list of heuristics and asked them to reflect on how
representative they were of their current practices and what aspects of text
practices we should further consider. Sessions took place over video
conference and were screen-recorded and auto-transcribed. Findings from
transcripts were derived via thematic analysis and contributed to
contextualize patterns of dashboard text use (§3), validate our proposed
heuristics (§4), and inform opportunities for text use in dashboards (§5).

Figure 2: A snippet of a dashboard displaying text components at varying
levels of semantic detail
89https://public.tableau.com/views/Assignment1_16303281835750/Dashboard2Kow
Vincenthttps://public.tableau.com/app/profile/kow.vincent/vizzes. 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.

##  3 Characterizing Current Text Practices

Our examination of current practices for dashboard text is based on findings
from our coding exercise (§2.2) and interviews with expert dashboard creators
(§2.3). First, we provide a characterization of text components in dashboards
from a functional lens by outlining different embodiments and roles of text
use (§3.1) and from a semantic lens via an adaptation of Lundgard and
Satyanarayan’s four-level model of semantic content [34] (§3.2). We then
discuss prominent text usage patterns that emerged from the coding exercise
and expert interviews, presented alongside implications for design (§3.3).

###  3.1 Text Components

We identified two groups of dashboard components where text plays an integral
role: (a) text blocks, i.e., where text appears as a standalone element; and
(b) text-based components, where text is intrinsically combined with visual
and interactive features but is still prominent. Text decorators to
interactive widgets (e.g., filter labels, drop-down labels) and primarily
visual components (e.g., chart axis and ticks and color legend labels) are not
considered in this assessment, as their content and functionality are dictated
by design requirements of corresponding charts and legends, rather than
offering independent or additional narrative or analytical insights. We also
did not explicitly code for text visualization instances, as their use of text
is highly bespoke.

The list of text blocks we identified include:

  * $\rightarrow$

Title  (present in 92% of dashboards in our corpus). A prominent, discernible
label (or short sentence) displayed with a font size larger than other text
blocks in the dashboard (Fig. 1(A)).

  * $\rightarrow$

Subheading  (36%). Text that goes below (or beside) the dashboard title and
adds more context to the title (Fig. 1(B)).

  * $\rightarrow$

Section Header  (29%). A textual element that labels a partition of a
dashboard containing multiple components (e.g., a text block alongside one or
more views) and provides a high-level overview of what the ensuing section
contains (Fig. 1(C)).

  * $\rightarrow$

Chart Title  (84%). Text labeling an individual chart or view (Fig. 1(D)).
Even though they play a supportive role to charts, they allow for more
authoring freedom than text decorators and are thus included in our assessment
as a standalone component.

  * $\rightarrow$

Interaction Guidance  (58%). Text that describes possible interactions with
the dashboard, often written in imperative language, e.g., “click here to
filter" (Fig. 1(J)).

  * $\rightarrow$

Metadata  (76%). A feature borrowed from Bach et al. [2], it designates text
that describes data sources (53%), author (43%), last update (18%), and data-
related disclaimers (26%) (Fig. 1(H)).

  * $\rightarrow$

Content Block  (67%). Sentence or multiline text that (a) supports the
analytical and narrative goals of the dashboard (such as explaining what the
data is, the visual encodings, salient points, and relevant context), and (b)
is hierarchically perceived as “body” level text (Fig. 1(F)).

The list of text-based components that we consider are:

  * $\Rightarrow$

Text-data summaries  (58%). Also known as BANs (i.e., “big-a** numbers”), they
feature key measures (i.e., a numerical value and a corresponding label) in a
prominent way (Fig. 1(I) and (K)).

  * $\Rightarrow$

Chart Annotation  (19%). Text callouts overlaid on charts to bring attention
to particular data elements, e.g., salient points, trends, data-specific
context, and interaction guidance (Fig. 1(E)).

  * $\Rightarrow$

Data Table  (35%). A (still very prominent) text-based alternative to showcase
data (Fig. 1(L)).

  * $\Rightarrow$

Tooltip . A dynamic text callout that appears when hovering data marks (Fig.
1(G) and (J)). Dashboards created in Tableau and Power BI implement basic
default tooltips that show the numerical values for the hovered data points,
and so they are nearly ubiquitous (i.e., appearing in nearly 100% of our
dashboards). However, a substantial subset of them (42%) are customized to
display text content in a richer way (e.g., in the form of sentences), making
tooltips a relevant content authoring resource.

###  3.2 Semantic Levels

Beyond a functional understanding of text components, our coding also sought
to capture how text content shapes dashboard communication. To this end, we
adapted Lundgard and Satyanarayan’s four-level model for semantic content
[34], which was originally created in the context of accessibility to
categorize analytical levels of detail of chart caption paragraphs. Given that
the scope of our coding encompasses shorter, label-style text as opposed to
fully formed sentences, we made a few adaptations when applying the four-level
model to dashboard text components, described below and exemplified in Fig. 2:

  * •

Level 1 (LV1): Elemental and encoded properties (97%). Refers to the
description of charts and visible chart data, e.g., adjacent  content blocks ,
and  chart titles .

  * •

Level 2 (LV2): Statistical concepts and relations (74%). Refers to mentions of
statistical features of the data formulated at a “data facts” [53] level,
e.g., descriptive statistics, outliers, point-wise comparisons.  Text-data
summaries  generally fit in this category as they often depict sums, averages,
and extrema in a similar granularity as LV2 statements.

  * •

Level 3 (LV3): Perceptual and cognitive phenomena (27%). Refers to more
complex data statements, such as higher-level trends and pattern synthesis,
while using more “natural-sounding" language than LV2 statements. Most often
found in  content blocks , and occasionally  chart annotations  and
subheadings .

  * •

Level 4 (LV4): Contextual and domain-specific insights (50%). Text that
provides additional context to the observed patterns and trends, e.g.,
historical, social, and political factors. Largely present in  content blocks
, and sometimes  tooltips .

###  3.3 Outlining Dashboard Text Practices

Building on the functional and semantic categorizations above, plus dashboard-
level features [2, 45] and expert interviews, we discuss various usage
patterns for dashboard text alongside implications for design.

####  3.3.1 Dashboard Text Practices are Based on Experience

When asked about their text use more generally, most participants (8/13)
stated that they are not explicitly formalized ($P1$, $P4$, $P6$, $P8$) nor
taught ($P6$), but rather based on experience, exposure, and honed senses,
e.g., learning from others ($P6$, $P7$, $P13$), learning on the job ($P5$,
$P12$), and developing a feel for “what looks good” to them ($P3$, $P7$),
which feeds into an implicit set of shared practices: ‘even if it hasn’t been
formalized, there is kind of this collective understanding (..) because you’ve
seen it repeated in other forms of conception” ($P4$). The more explicit
guidance emerging from expert sessions was on structure and formatting
guidelines borrowed from other established fields, such as publishing &
typography (e.g., magazines) ($P1$, $P4$, $P7$), and graphic & web design
($P5$, $P7$, $P13$).

Implications: While text practices are not formalized, shared practices do
exist and borrow inspiration from other communication and UX fields.
Explicitly formalizing text practices is valuable and viable to some extent,
but the most sustainable path forward is likely learning from examples (e.g.,
machine-learning approaches).

####  3.3.2 Text Components and Semantic Levels

Our coding exercise helped inform how text components tend to cover semantic
levels.  Chart titles  were the most frequent contributors of LV1 content, and
text-data summaries  of LV2 content.  Content blocks  encompassed a lot of the
higher level, fine-grained detail and context in dashboards, appearing in 96%
of dashboards featuring LV3 content, and 90% of dashboards featuring LV4
content (versus 67% of all dashboards); and so did  chart annotations
(present in 42% of dashboards with LV3 and 28% with LV4 content, versus 19% of
all dashboards).

That said, text components also showcase a lot of diversity in terms of the
range of semantic levels they may support.  Tooltip  instances in our corpus
expanded far beyond their default role of displaying data values under hovered
marks (LV1), to also correlate marks with other data dimensions (Fig. 1(b)
(#136)), list associated data facts (#137), add in historical context (#55),
and showcase disclaimers (#34).  Chart annotation  content was found to be
equally diverse, covering the whole range of LV1 (e.g., overlaying associated
data values, (#126)), LV2 (e.g., emphasizing salient points and trends, (#111)
(#118)), LV3 (e.g., explaining complex trends, (#87) (#37)) and LV4 content
(e.g., incorporating external context, (#79) (#58)). We also noted several
instances of  titles  and  subheadings  being used to convey LV3 content, such
as summarizing higher-level insights from the data and framing them as
takeaways, e.g., (#178), Fig. From Instruction to Insight: Exploring the
Functional and  Semantic Roles of Text in Interactive Dashboards (right)
(#173).

Related to this last observation, it is worth noting that the hierarchical
structure of text blocks ( titles ,  subheading ,  section headers ,  chart
titles ) also tend to mirror a hierarchy of semantic concepts, going from
titles  and  subheadings  that allude to higher level analytical goals down to
chart titles which are closer to the data.

Implications: There is a strong relationship between structural levels of text
and their semantic level coverage. This underscores that the visual design and
formatting of text is more than an aesthetic concern. From a content
generation perspective, this means not only looking at semantic hierarchy, but
also how that hierarchy may be scaffolded by an explicit, visual text
structure.

Figure 3: Tooltip featuring LV2-LV3 content
77https://public.tableau.com/app/profile/acotgreave.tableausoftware.com/viz/ArkhamDBDataDrivenDeckBuidling/DeckBuilderpopularcards2Andy
Cotgreavehttps://uk.linkedin.com/in/acotgreave.

####  3.3.3 Text Use Across Dashboard Genres & Goals

The strongest signal we found to discriminate text use, based on the coding
exercise and on expert feedback, is if the dashboard is more narrative-driven
(e.g., infographic and magazine genres, often motivation & learning goal, for
general audiences, casual use), or more exploratory (e.g., largely analytic
genre, and decision making goals, for expert audiences, recurrent use).

By definition, narrative-driven dashboards make far more liberal use of
explicit salient points and takeaways. This emphasis is clear when we look at
the prevalence of perceiver-dependent content (LV3-LV4) and associated text
components against infographic (more explanatory) and analytic (more
exploratory) dashboards. Infographics feature more  content blocks  (91%, vs.
53%),  chart annotations  (29% vs. 14%), and LV3-LV4 content (57% and 76% vs.
14% and 33%, respectively) than analytic dashboards. Differences between
dashboard goals (motivation & learning vs. decision making) are even more
pronounced, for  content blocks  (93% vs. 49%),  chart annotations  (32% vs.
12%) and LV3-LV4 semantic levels (58% and 75% vs. 8% and 29%).

Many experts (5/13) report on a similar divide on the design of enterprise
dashboards as “meant to be exploratory and analytical rather than narrative-
driven” ($P9$), and they acknowledge actively avoiding “prescribed analytics”
($P4$) in these cases: “the last thing I want to do is to feed conclusions to
the user” ($P12$). That said, there is still a place for LV4 content in
corporate dashboards, in the form of metric definitions and explanations
($P4$, $P7$, $P9$, $P12$), metadata (e.g., when last updated) ($P7$), data
alerts ($P4$), and interaction guidance ($P1$, $P5$): “You’re supposed to
guide them on how to use the dashboard but not guiding them to the insights”
($P5$).

Implications: Context of use is a key factor guiding the choice of text
elements and semantic level coverage. Dashboard genres, dashboard goals,
dashboard audience, and frequency of use are potentially useful signals to
discriminate text use at scale.

####  3.3.4 Text Formatting for Analytical Support and Emphasis

In our coding exercise, we found that text formatting and placement is
frequently used to support analytical reasoning. From interviews, we found
that text formatting was the aspect of text use that experts were most
intentional about, and the majority (7/13) reported following some form of
text formatting guideline. The practices we compiled are:

  * •

Leveraging font sizes to convey hierarchy of text components (e.g.,  titles
vs.  chart captions ) and support reading order ($P3$,$P4$).

  * •

Text color used as color legends ($P6$, $P7$, $P9$) (Figs. 1(a) and 1(d)).

  * •

Boldface and text color for emphasis (e.g., data values, data labels,
takeaways) ($P7$, $P8$); (Figs. 1(b) and 1(a)).

  * •

Italics and bold face to distinguish interaction guidance  from  content
blocks  ($P13$) (Fig. 1(d)).

  * •

Avoid small fonts, for legibility and accessibility reasons (5/13): “screen
readers can read best anything that’s 12 pt and up” ($P4$)

  * •

Consistent use of fonts (4/13): “I don’t like mixing fonts” ($P4$).

While seemingly obvious, these text practices require deliberate action from
dashboard designers. There is also general consensus that text formatting
affordances in dashboard tools are minimal, and scriptable tasks like aligning
text color to match visual marks require much manual intervention.
Unsurprisingly, several experts reported the use of style guides in their
corporate practice ($P4$, $P7$, $P11$), to help inform and align formatting
practices across larger design teams.

Implications: Text formatting plays a significant role in dashboard
communication and accessibility, but realizing its potential presently
requires designer knowledge and experience. Given reasonable consensus around
formatting practices, rule-based approaches to facilitate enforcement of best
practices might be feasible as a first step. More importantly, explicit text-
data linking would enable useful formatting affordances.

####  3.3.5 Managing Content Overload with Details on Demand

Regarding overall dashboard content, many experts expressed a desire to
minimize “big walls of text" ($P1$, $P11$, $P12$), following a general
understanding that users “tend to glance over” text content ($P1$, $P4$, $P8$,
$P13$): “my approach is to shorten everything that you can shorten” ($P11$).
These considerations are particularly relevant in the context of explanatory
dashboards, where experts report being very deliberate in choosing what
content to include and trying to “keep it as relevant as possible” ($P7$) by
focusing on the key takeaways ($P4$, $P6$, $P7$): “I’m constantly questioning
myself. Why am I not including this? what is the ulterior motive of why you’re
doing this?” ($P4$).

As for minimizing visible text content, the most cited strategy is providing
details on demand via  tooltips  ($P1$, $P7$, $P9$, $P10$, $P13$). While
dynamic views and tabs were occasionally used for this same purpose, tooltips
were prevalent and featured a broad range of implementations, including
embedded visual reports (e.g., Fig. 1(b) (#136)). Another notable tooltip
adaptation is having content formatted as a templated sentence (seen in 9% of
dashboards, e.g., Fig. 3), aimed at simplifying the interpretation of data
facts ($P8$, $P13$): “if it’s a less technical audience, then I’ll try and
turn it into a sentence” ($P13$).

Implications: Choosing what content goes into dashboards is a nuanced task
that leans on minimizing visible text. Content summarization, text templating
tools, and more content nesting affordances would be helpful for authors. From
a content generation perspective, ranking strategies should also be
considered.

Figure 4: Dashboard depicted in Fig. 1(c) (#59) with active instruction
overlay.

####  3.3.6 Guiding Users (or Not)

As discussed earlier, different dashboard genres entail different degrees of
user guidance, from analytical support that highlights salient points and
articulates takeaways to usability support via visualization interpretation
instructions and interaction guidance. A lot of the text practices already
covered pertain to user guidance, including the significant prevalence of
interaction guidance  components (58%), text formatting practices for guidance
text, and simplifying analytical interpretation with sentence-style tooltips.

Despite the differences in analytical guidance needs across dashboard genres,
the presence of  interaction guidance  in our corpus is fairly balanced across
all non-static genres (between 53% and 65%). We found interaction guidance
text to be most often displayed as co-located text blocks (either adjacent to
or embedded in corresponding views) or as instructions in interactive widgets
(e.g., filters). Other creative but less common implementations include: using
icons in lieu of text (#82); instruction overlays that, when active, reveals
text instructions next to relevant dashboard components, e.g., (#22), Fig. 4
(#59); and staged presentation (#136), where portions of the dashboard are
shown or highlighted one at a time, e.g., (#172) (#181).

While useful, guidance may not always be needed. Relevant use cases for
guidance, according to experts, include audiences with lower visualization
literacy ($P3$, $P13$) and bespoke or unusual interactions ($P6$, $P7$,
$P13$): “I like to assume most people know how general filters work, so I
don’t tend to explain those unless there is some sort of nuance that needs to
be explained” ($P13$). One participant argues that explicit interaction
guidance may not even be always beneficial: “They’re really useful, but
they’re only useful once, right? (..) An area I continually struggle with is,
do you make that explicit and make the experience for first-time users better,
or do you not make that explicit and make it better for continuous users?”
($P9$). They also commented on cases where discovering hidden interactions
makes for a “delightful” experience: “That’s always fun to get the element of
surprise. If it can be done well, you can spark joy” ($P9$).

Implications: Determining appropriate levels of analytical and interaction
guidance is a key design consideration that requires understanding the
analytical needs and visualization literacy levels of the target audience.
While guidance is not always necessary or desirable, there is value in
providing better support for authors to create more and better guidance and
encourage thinking about end users and their information needs.

####  3.3.7 Dynamic Text for Analytical Support and Breadcrumbs

One of the aspects considered in our coding exercise was the presence of
dynamic text, i.e., understanding if and how text components respond to
interaction or embody animations. Dynamic text is present in 49% of all
dashboards in our corpus and 62% of analytic dashboards, underscoring its role
in more exploratory-driven dashboards. Common versions of dynamic text include
text-data summaries  that update with dashboard filters and interaction
breadcrumbs for  titles  and  chart titles  that update to reflect newly
selected data dimensions. These embodiments play an important awareness role
in communicating what data slices are included or excluded, e.g., “I’m putting
on [filter indicators] on the dashboard so that they can see very clearly,
‘Oh, I have filters applied, I don’t really mean to do that’ ”($P6$).

Less common but interesting uses of dynamic text are on dynamic takeaways,
i.e., pre-computed text findings that change depending on the filter
selections (e.g., (#53) (#149) (#190)); and a circular “news ticker" strips
with data highlights (e.g., (#22) (#189)). On the latter, some experts
commented on the value of animated features for user engagement ($P3$, $P7$,
$P9$, $P10$): “Maybe you hover on top of a tiny text and it’s just goes
bigger, little things that just keep you engaged” ($P7$); and some also
mentioned practical uses of dynamic formatting and alerts in their corporate
practice ($P4$, $P6$): “if it’s an overall data issue, it’s at the very top
and it gets color-coded by brand” ($P4$). But despite the excitement and the
meaningful use cases for dynamic text and dynamic formatting, adding such
dynamic behaviors with mainstream dashboard tools is, again, reportedly
challenging ($P2$, $P5$, $P6$, $P13$).

Implications: Dynamic text is a compelling resource to enhance comprehension
and delight dashboard users but is difficult to implement with current tools.
There are opportunities to provide more user-friendly ways to link dashboard
text and data properties, and more flexible strategies for templated content
generation.

####  3.3.8 Communication Elements Beyond Text

Finally, we mention a few text-adjacent topics that have an impact on
dashboard communication and that emerged in the coding and discussions. First,
we noted a prevalence of icons for communication (33%) either as space-
efficient alternatives to text, as redundant visual elements, or as text
decorators (e.g., Fig. 1(d)). When well designed, icons and symbols enhance
dashboard comprehension, but again, practices are not formalized. There is
room to facilitate more principled icon usage via standard icon libraries and
dedicated design recommendations.

Second, text plays an important role in dashboard accessibility, as both an
object (e.g., with formatting concerns) and enabler (e.g., via alt text) of
accessibility. Experts agree that this is an important consideration, but also
not a top-of-mind concern for most (7/13), and something that entails design
trade-offs ($P9$) (e.g., limited font options).

Third, an accessibility-adjacent topic that has received relatively little
attention is language translation support. Our English corpus features two
distinct uses for multilingual dashboards, including one where all text is
duplicated to feature both English and Japanese (#58); and one featuring
protest chants in Polish (with English translations) for affect (#78).
Advocating for the expansion of text visualization techniques and fostering
multilingual accessibility is crucial for engaging a global audience and
ensuring dashboard usability.

Implications: While text is a major player in dashboard communication, many
other communication aspects can affect or are affected by text. Keeping a
broader outlook on the dashboard communication landscape may help inform
additional areas of research.

Analytical Support | HA1 |  Text in the dashboard supports specific analytical questions or tasks.
---|---|---
HA2 |  The text in the dashboard should emphasize the most salient points of what the visuals in the dashboard convey.
HA3 |  The text in the dashboard supports readers in deriving clear takeaways from the dashboard.
Semantics | HS1 |  The text provides sufficient contextual information to describe what the dashboard is about.
HS2 |  Text content within the dashboard is at the appropriate level of detail to convey the intended message.
HS3 |  The text in the dashboard plays a role in disclosing sources, disclaimers, and biases.
Presentation | HP1 |  The text in the dashboard is legible, easy to read, and useful. Different parts of the dashboard are well-described.
HP2 |  Text supports a clear reading order within the dashboard, and it is logical.
HP3 |  Text formatting and placement provide a clear and consistent visual style, mood, and guidance in understanding the analysis.
HP4 |  Icons and symbols are used with text to help communicate patterns in data.
Interaction | HN1 |  Text makes it clear to the user where they need to start interacting with the dashboard.
HN2 |  The text indicates a clear path and breadcrumbs for performed user actions within the dashboard.
Table 1: Our set of 12 heuristics for the use of text in dashboards, based on
the work of Setlur et al. [48].

##  4 Recommendations for Text Use

Reflecting on the empirical nature of text practices, we argue it is useful to
materialize such tacit knowledge in the form of explicit design guidance, not
only for dashboard authors but also for dashboard tool designers. As described
in §2.2, we turned to the work of Setlur et al. [48] and their heuristics for
analytical conversation in dashboards. We reframed their 39 heuristics from a
text-centered perspective to derive a set of 12 heuristics (Table 1)
specifically on the use of text in dashboards to support analytical
conversation. We present our derived heuristics (§4.1), discuss findings from
applying these heuristics to our dashboard corpus (§4.2), and share expert
impressions (§4.3).

###  4.1 Heuristics for Use of Text in Dashboards

Our construction process started by assessing each of the 39 heuristics in
Setlur et al. [48] (H1-H39) and transforming relevant ones to be centered
around text use. Some heuristics made explicit mentions of the appropriate use
of text (e.g., H6 “the text in the dashboard is legible, easy to read, and
useful", here mapping to HP1) and are used almost (if not) verbatim. Others
covered communication aspects where text plays an implied or supporting role,
which we then redrafted to explicitly mention text as an actor, e.g., H15
“Charts within the dashboard are at the appropriate level of detail to convey
the intended message.”, here mapping to HS2, “Text content within the
dashboard are at the appropriate level of detail to convey the intended
message”. Heuristics with similar meaning after these redraftings were merged
(e.g., H9 thru H12, now HP3). While we iteratively fine-tuned descriptions and
rubrics, the essence and scope of the heuristics remained.

Instead of the five conversation states used to organize the heuristics in
Setlur et al. [48], we grouped ours into four guidance categories tailored to
more directly address properties and roles of text use in dashboards:
Analytical support, referring to how text aids users in understanding the data
and analytical questions they can derive from the dashboard; Semantics,
referring to the role of context and granularity of information to support
analytical tasks; Presentation, referring to formatting and layout
considerations to support navigation; and Interaction, highlighting the role
of interaction guidance and awareness.

###  4.2 Applying the Heuristics

We wanted to understand how heuristics may be used in practice to assess
dashboards in supporting analytical conversation and what features of text use
tend to contribute to each heuristic. As mentioned in §2.2, both authors
independently assessed dashboards in the corpus using the heuristics via a
rubric that spans four assessment levels (strong application, weak
application, weak violation and strong violation), and engaged in in-depth
discussions to reach consensus on what aspects of dashboard text contributed
to the assessment of each heuristic. We made our best efforts to account for
differences in topic, genre, and dashboard goals in each case. This process
allowed us to shift from a creator’s perspective to a user’s perspective and
to reflect on how text components in dashboards may best serve their intended
audience. In the following segments, we share our process findings from
applying the heuristics to our dashboard corpus and how our functional (§3.1)
and semantic (§3.2) categorizations may be leveraged to guide a heuristics-
based dashboard assessment. For those seeking to perform similar heuristic
assessments, we encourage the use of our assessment rubric in our code book
(Supplemental Materials).

Analytical support. We found that having a combination of complementary text
components that work well together is key for good analytical support. Header-
level text blocks like  title ,  subheading ,  section headers , and  chart
titles  lend structure to connect data-level chart communication goals (HA1)
to higher-level takeaways (HA3). Some components make direct and obvious
contributions to heuristics, such as  chart annotations ,  text-data summaries
, and  content blocks  to salient points (HA2), and subheading to takeaways
(HA3), but as discussed earlier in §3.3.3, prescriptive analytics are not
always needed or desired, nor more content equate better communication.
Several dashboards in our corpus showcased good application of these
heuristics even with minimal use of text (#53) (#98) (#111).

Semantics. Assessing sufficient context (HS1) is a subjective measure that
heavily depends on the use case and intended audience [17]. We assessed this
metric based on the extent we were able to interpret and analyze dashboard
data given available information (e.g., in  content blocks ,  tooltips , and
chart annotations ). Dashboards for expert users or general knowledge topics
(like sports or the pandemic) might be sufficiently grounded without explicit
context (LV4), but we found that having explicit context was generally
helpful. On the other hand, the assessment of semantic levels of detail (HS2)
is more objective and can be characterized in terms of the coverage of the
LV1-LV4 content spectrum to support analytical reasoning. A strong application
here means that there are clear reasoning paths from LV1 and LV2 findings to
overarching conclusions (LV3 and LV4). All text components collectively play a
role in supporting this aspect. Finally, sources, disclaimers, and biases
(HS3) are present in  metadata  blocks; in our assessment, applications
featured easily retrievable sources (45%), and strong applications featured
notes on data collection and processing (19%).

Presentation. Presentation factors like formatting and placement play both
functional and aesthetic roles in dashboard communication, and are addressed
by HP2 and HP3. Good applications for HP2 refer to the extent that dashboard
text supports users with navigation: it is about conveying a strong visual
sense of content hierarchy via font size and header placement for content
navigation, as well as strategic placement of text blocks close to relevant
visual components. HP3, on the other hand, focuses on appropriate and
consistent use of text formatting more holistically, including fonts, color,
mood, and branding. HP1 pertained specifically to the quantity and quality of
text at appropriate levels to fulfill dashboard goals; dashboards we assessed
lower in this criteria featured little to no content beyond  chart titles .
Finally, HP4 is about ensuring that icons (when used) have a synergistic role
with text, clearly communicating their intent and matching dashboard
aesthetics.

Interaction. The final two heuristics pertain to the role of text in
scaffolding interaction. The first is about aiding discovery (HN1), which we
assessed as the presence of explicit text instructions (i.e.,  interaction
guidance  blocks) to point new users to key interactive features of the
dashboard; strong violations happened when meaningful interactive features
were missed due to lack of explicit guidance. The second refers to text
communicating interaction outcomes (HN2), which was most frequently embodied
as dynamic labels updating to match filters, e.g., a  chart title  updating
alongside an updated chart. Strong violations here map to cases where no
traces of user action are provided in text or visuals, e.g., filtered views
misrepresented as whole dataset views.

###  4.3 Expert Feedback on Heuristics

Expert feedback on heuristics was largely positive. The empirical nature of
text practices is again acknowledged ($P4$, $P6$, $P12$): e.g., “I teach data
visualization frequently (…) [and] we don’t really talk about text that much,
or how to use text appropriately” ($P6$); and that having these practices
formalized as heuristics is useful (10/13): “For those who are not familiar
with the norms or may come from an experience where the norms are different,
[this formalization] is a unifier ($P4$). Cited benefits and uses entailed:
fostering intentional and “thoughtful usage of text" ($P5$, $P6$, $P13$);
guidance for novice designers ($P6$, $P8$) and sanity checks for experienced
designers ($P5$, $P9$); and providing a base for more bespoke guidance in
applied scenarios ($P7$). Participants also stated that our set of heuristics
felt complete (8/13) and representative of their practices (5/13), and they
also appreciated the structure with four guidance categories ($P3$, $P4$,
$P7$).

While the heuristics were found to provide “a good template” ($P7$) for
recommended text practices, many participants argued more specificity is
needed to support practical hands-on support ($P3$, $P4$, $P8$, $P9$), e.g.,
“If it’s just guidance to point people in the right direction, great. But if
you want people to have a standard and follow, I need a little more functional
direction and just something to grasp and hold on to” ($P4$). A participant
also remarked that the heuristics on their own will not ensure dashboard
quality, e.g., “you tick the boxes altogether and then maybe you might get an
ideal dashboard, but sometimes you might not” ($P12$). Topic suggestions for
more specific guidance entailed best practices in text formatting ($P4$, $P5$,
$P7$), guidance for annotations ($P3$), and an example catalog to showcase
both good and bad applications of individual heuristics ($P3$, $P5$, $P11$,
$P13$). More generally, some also underscored the importance of “context and
perspective of the user.” ($P10$, $P12$), and better communicate the extent to
which each heuristic matters on different use cases ($P10$).

##  5 Opportunities for Text use in Dashboards

We identified several areas for future research that build on our dashboard
findings, discussions with experts, and application of heuristics.

Dashboard linters. Based on the strongly empirical nature of dashboard text
practices (§3.3.1) and informal dashboard text practices (§3.3.4), novice
dashboard designers would greatly benefit from direct design guidance. Akin to
the goals of visualization linters [36, 7], dashboard linters could provide
real-time feedback on text use during authoring, ensuring adherence to best
practices within the scope of our 12 heuristics (§4), e.g., typography,
readability, and information hierarchy. These tools could also potentially
offer suggestions for improving text clarity, as well as accessibility [54].

Example-based style transfer and recommendations. In view of the prevalent use
of style guides in large design teams (§3.3.4), we also see value in more
easily transferring text design features from one dashboard to another,
similar to the notion of style transfer in image rendering [18]. By analyzing
exemplary dashboards within various domains and genres, algorithms can also
provide smart defaults and recommendations for effective text formatting
styles (e.g., font sizes, colors, hierarchy) that can help lower the barriers
for text design that can effectively support navigation and reading order
(§3.3.1, §3.3.2).

Automatic explanations. Reflecting on user guidance needs for exploratory and
explanatory dashboards alike (§3.3.3, §3.3.6), we see an important opportunity
for the development of automated and semi-automated content generation tools
for context-specific explanations on underlying data. In the context of
corporate dashboards, for example, explanations on visible metrics are widely
used but onerous to create and keep consistent with changes in the data
processing pipeline. Being able to generate accessible, domain-specific
explanations that can be vetted by dashboard authors would significantly ease
the burden on the author to produce comprehensive explanations.

Provisions for text+charts links in dashboard design. Following research on
text-chart links for visual storytelling [60, 37, 28, 72, 73, 26],
facilitating the linking of dashboard text and charts would allow for exciting
opportunities to expand the capabilities of dynamic text for analytical
communication and storytelling (§3.3.4, §3.3.7). Automating the linking of
text and charts in this context would involve developing algorithms that not
only detect and establish connections between textual descriptions and
corresponding data points for guidance [29, 37, 43], but also suggest
contextually relevant emphasis [59], such as bolding key takeaways, coloring
data references, or italicizing instructions (§3.3.4, §3.3.6). Future research
could focus on creating intelligent storytelling tools that adapt text
formatting based on the data narrative and integrate these capabilities into
existing dashboard authoring platforms.

Guidance overlays and personalization. Incorporating instruction overlays into
dashboards can guide users through complex datasets or analysis features,
enhancing learnability (§3.3.6). Drawing from just-in-time learning and in-
situ instructions [56], research could explore the inclusion of overlays that
adapt to user preferences and interaction patterns, offering better
scaffolding for dashboard users. By analyzing user behavior, along with
analytical and accessibility needs (§3.3.3, §3.3.8), the dashboard could then
take user assistance a step further by tailoring personalized walkthrough
versions for specific audiences, featuring data insights relevant to the
user’s specific needs or roles.

Reactive documents meet dashboards. Reflecting on next steps for dynamic text
(§3.3.7) and the benefits of prose text for analytical understanding (§3.3.5),
we are inspired by past work on explorable documents [12, 66]. Applying these
concepts to interactive dashboards presents a fertile ground for future
research where interactive narratives and user-driven data exploration
coalesce. For example, revealing textual explanations based on user
interaction; breadcrumbs that provide dynamic summaries of the analytical
journey; and contextual information surfacing on demand. Future work could
also explore how text can play an active role in the storytelling experience
through interactive text widgets that allow users to manipulate data directly
from the narrative.

##  6 Limitations

While we made deliberate efforts to increase diversity and representation in
our dashboard sample, our research primarily analyzes publicly available
dashboards, a limitation given the vast universe of private corporate
dashboards that remain beyond our purview. Business dashboards in public
platforms (e.g., Tableau Public) are often portfolio versions depicting mock
data, whereas real business dashboards are embedded in intricate data
ecosystems, feature complex visualizations, and tend to be tailored for
specific organizational contexts and decision-making processes, potentially
incorporating proprietary design elements and interaction paradigms. This
exclusion could mean that our analysis misses crucial insights into dashboard
design and use in professional environments and might raise concerns regarding
the generalizability of our findings. While our interview experts helped us
uncover various text-related concerns relevant to their professional practice,
we argue that collaborations with professional and media organizations could
help identify novel applications of text in domain-specific use cases, thus
broadening the applicability of this work.

We also acknowledge the inherent subjectivity that accompanies our evaluative
framework of the heuristics. Arriving at a unanimous consensus on the
application or violation of heuristics was a laborious and nuanced exercise,
but also an informative one, and we stand by the value of these heuristics as
tools for design guidance. That said, we agree that further assessment with
wider usage would be valuable, e.g., evaluating the heuristics with novices as
per original work [48].

Finally, while the heuristics proposed in our work provide a structured
approach to evaluating and designing text for dashboards, they may not offer
the specificity required for direct application in all authoring scenarios.
This limitation arises partly from the diverse contexts in which dashboards
operate, making it challenging to formulate universally applicable rules
without sacrificing relevance to specific analytical use cases and target
audiences. However, the broad nature of our guidelines also presents an
opportunity for designers to interpret these guidelines in ways that best suit
their unique objectives and user needs.

##  7 Conclusion

Our work highlights the fundamental role of text in the context of dashboard
design and communication, wherein textual elements do much more than accompany
visualizations; they enrich and clarify, providing essential context and
direction. We hope this paper provides useful insights and future directions
for not only dashboard authoring tools to better leverage the utility of text
with visualizations, but also to guide effective analytical conversation and
text generation in dashboard-adjacent text-data spaces, such as data stories
[50, 49], data videos [70], computational notebooks [32], visualization
thumbnails [21], and chart captioning [33, 54]. This synergy between text and
visuals reaffirms the adage, “A picture is worth a thousand words, but a word
can paint a thousand pictures,” underscoring the dual power of text to distill
complex data into information understanding.

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