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Data Visualization for Business Intelligence


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* Accompanying more recent lecture notes: or ------- Compared to other types and applications of visualization, business data visualization, particularly concerns about the visualization of business data, is mainly for the purpose of communication, information seeking, analysis, and decision support. This chapter provides a comprehensive high-level view of different types of data visualizations that can be used in the business environment, and to provide a guidance of technology and system selection.
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6 Data Visualization in Business Intelligence
Data Visualization in Business
Jack G. Zheng
Business Intelligence (BI) is a set of methods, processes, architectures,
applications, and technologies that gather and transform raw data into
meaningful and useful information used to enable more effective strategic,
tactical, and operational insights and decision-making to drive business
performance (Evelson and Nicolson, 2008). A general BI process covers a
number of sub-processes or phases including data gathering, data cleanse,
data storage, data analysis, data presentation and delivery (Zheng, Zhang,
and Li, 2014). In the phase of data presentation, query or analysis results are
presented and delivered in various human comprehendible formats (such as
tables and charts) which directly supports sense-making and decision-
making. Data presentation also includes interactive queries and data
explorations that help users find useful information. Correspondingly in the
technology stack, BI systems include various data visualization and
interaction forms and techniques through reports (static and interactive
reports), digital dashboards, and more complex analytical visual tools
(Chiang, 2011).
Data visualization has been rising rapidly for the past a few years in the BI
and analytics industry, as part of the modern BI movement which
emphasizes on self-service (Parenteau et al., 2016). It is also a big part of
data science which has gained wide popularity recently. There have a been a
plethora of tools and systems that feature their data visualization solutions.
As an interdisciplinary field, data visualization brings together psychology,
technology, art, and decision science to deliver the last mile of the complete
BI and analytics capability to users. Compared to other types and
applications of visualization, business data visualization, particularly
concerns about the visualization of business data, is mainly for the purpose
of communication, information seeking, analysis, and decision support.
One of the key questions in business data visualization is how, and in what
form, data visualization contributes to the overall business intelligence
6 Data Visualization in Business Intelligence
process and system. This chapter provides a comprehensive high-level view
of different types of data visualizations that can be used in the business
environment, and to provide a guidance of technology and system selection.
The chapter starts with defining business data visualization and comparing it
to other common types of visualizations and their applications, then provides
a comprehensive review and analysis of common tools and applications of
business data visualizations used in business intelligence, and concludes
with a brief overview of recent trends and prospects.
What Is Business Data Visualization?
The term business in business data visualization, as well as in business
intelligence, has a broader meaning than just commercial activities. It
generally refers to many human and organizational activities and operations
that keep a system running. This can include commerce, education, sports,
entertainment, government, and many others. In these business activities and
processes, data are produced and recorded to reflect all aspects of the
business (human or organizational activities), and then it is analyzed and
reported at various levels. Business intelligence is about transforming raw
data into meaningful and useful information that is consumed by humans.
Business data or information is different from other types of data (Tegarden,
1999). In the context of business intelligence, business data has the
following features:
Abstract: most business data describes abstract activities and processes
(e.g. product sales, member registration, product or user movement,
etc.). The data does not describe or is not directly used to create real-life
entities (objects, models) or phenomenon. The visual representation of
this kind of data is also abstract by using metaphors.
Quantitative: although qualitative data also offers great insights and has
a lot of values today especially in the artificial intelligence discipline,
quantitative data is the focus of business data. In many cases, qualitative
data is quantified in business intelligence analysis and business data
Structured or semi-structured: most data is structured and shares
common attributes with clearly defined metadata.
Multidimensional: facts or measures can be viewed and analyzed
through different perspectives and levels. This is particularly common in
business analysis.
Atomic: most business activities are based on business transactions;
each raw data record represents a transaction and can be viewed and
understood independently.
6 Data Visualization in Business Intelligence
Comprehendible: data and results can be directly understood by human
users (assuming with domain knowledge) in a short time.
The BI process typically consists of data management (also including data
gathering, cleanse, storage), data analysis, and data presentation. The term
data presentation describes the interfacing layer between data and human. In
this layer, data (can be raw, aggregated, or any types of analysis results) are
presented to users in their desired forms and formats. In the statistics
discipline, the three basic categories of data presentation are commonly
summarized as textual, tabular, and graphical. These categories can also
apply to BI data presentation methods. Data visualization is the graphical or
visual method of presenting data. In the context of business intelligence, it
can also be called business data visualization or business information
visualization to distinguish other types of visualization.
In general, visualization is the process of forming a concrete and direct
vision-perceivable image in a human mind by utilizing a combination of
visual elements (shapes) and variables like color, positions, etc. Things that
can be visualized include visible reality that people can see (person, world,
nature), hidden reality that normally be hidden (earth core, blood, universe),
invisible reality (wind, air, heat, electron, sound, smell), and abstract things
(data, idea, hierarchy, process, relationship).
Data visualization is the visual and interactive exploration and graphic
representation of data of any size, type (structured and unstructured) or
origin. The purposes of visualizing data are multifold, ranging from general
comprehension and understanding of ideas, supporting information
behaviors (analysis and decision support, information seeking, browsing,
navigation), to artistic (beauty) expression and appreciation (Viégas and
Wattenberg, 2007), and even just for fun or storytelling. In contrast, the
goals of visualizing business data are focused on human information seeking
and decision-making behaviors, particularly in two broad goals: (a)
visualizing key metrics for easy and fast comprehension which directly
facilitates decision-making; (b) providing a visual and interactive way to
explore data. Such visualizations often use simple, standard, and abstract
charts or diagrams, and utilize data binding techniques at the back end.
Both research and practices have shown data visualizations value and
contribution to the decision process (Vessey, 1991) and information-seeking
process (Shneiderman, 1996). Visualization generally helps data
comprehension and enhances problem-solving capabilities. More
Visualization eases the cognitive load of information processing, and it
helps one recall or memorize data easily because of the perceivable
image (Borkin et al., 2013).
6 Data Visualization in Business Intelligence
Data visualization techniques provide a visual overview of complex data
sets to identify patterns, structures, relationships, and trends at a high
Visualizations provide visual cues that draw peoples attention to
quickly focus on areas of interest or areas of difference (can be an
anomaly). This allows decision makers to use their natural spatial/visual
abilities to determine where further exploration should be done
(Tegarden, 1999).
Visualization exploits the human visual system to extract additional
(implicit) information and meaning, sometimes referred to as intuition.
Business Data Visualization vs. Other Types of Visualization
Business data visualization has some unique features compared to some
related fields or methods that also utilize general visualization techniques.
These related fields or methods mainly include: information visualization,
illustration, scientific visualization (discussed together with computer
graphics and VR), and simulation. Their differences can be best illustrated in
their content (what is to be visualized), visual forms/tools (how they are
visualized), and purposes. The comparison is summarized in Table 6.1.
Table 6.1 Comparison of Related Visualization Fields
6 Data Visualization in Business Intelligence
Business data
Quantitative data,
metrics, key
indicators (KPIs)
Charts, diagrams,
Data exploration,
analysis, decision-
All kinds of
quantitative and
seeking, artistic
illustration, casual
structures concepts,
Diagram, image,
Making the content
more vivid and
engaging, easier to
understand the
Real-world object
or phenomenon,
functions and
3D virtual reality
Recreate or
simulate the real-
world object or
phenomenon, or
visualize an
algorithm effect
Calculated data
based on formulas
or rules
diagram or virtual
Demonstrate the
effect of scenarios
under certain rules
Information visualization is a very close field to data visualization. In fact,
the term is often used as the synonym to data visualization if data is used in a
more general sense (in contrast to business data). They share many common
features, principles, and methods. However, information can be generally
more qualitative and less structured, for example, information about
workflows, structures, concepts, and ideas. The visualization of information
utilizes more free forms of visual diagrams or illustrations (illustrational
diagrams) that are not specifically for quantitative data, for example,
network graphs and workflow charts.
Information graphics or infographics are a common tool for information
visualization especially in a more casual context. An infographic is
commonly a mixture of different forms of information (text and numbers)
and multiple visual forms (charts, diagrams, images, tables, maps, lists, etc.)
to quickly and vividly communicate a good amount of information in an
engaging manner (Harrison, Reinecke, and Chang, 2015). Many
infographics have a typical format characterized by large typography and
long vertical orientation (Lankow, Ritchie, and Crooks, 2012). They are
gaining popularity in online marketing over the years and their use has
expanded in many occasions where communication to the public is
important. Some examples can be found at and
6 Data Visualization in Business Intelligence
Information visualization is more casual (Pousman, Stasko, and Mateas,
2007), general, and subjective than business data visualization whose
purpose is more for decision support or data exploration. It is intended for a
wider and casual audience with a focus on storytelling or narrative
visualization (Segel and Heer, 2010). Because of this, information is
presented with stronger artistic expression than that found in typical business
data visualizations (Hagley, n.d.), sometimes with overuse of artistic design,
often referred to as visual embellishment (Bateman et al., 2010).
Illustration, as a term, is a little different than visualization. Illustration
often is used to explain ideas or concepts with the help of diagrams or even
general pictures and graphs. It materializes abstract ideas using more
concrete and directly perceivable images for explanation, or uses simplified
diagrams for explaining more complex situations (processes or structures).
Most importantly in the context of business, illustrations are not necessarily
data driven.
Scientific visualization, commonly used in science, is primarily
concern[ed] with the visualization of three-dimensional phenomena
(architectural, meteorological, medical, biological, etc.), where the emphasis
is on realistic renderings of volumes, surfaces, illumination sources, and so
forth (Friendly and Denis, 2006). Examples include physical science
visualization, visualization (simulation) of reality (universe, sun, explosion,
atom, climate, etc.), and mathematical model/algorithm visualization. The
visual output can be a virtual replica creation based on real data, or
computer-generated data based on algorithms and imaginary creation.
Scientific visualizations often make use of computer graphics and virtual
reality technologies to recreate the visual scene.
Simulation is somewhat related to scientific visualization and is
specifically used to demonstrate motion-based visuals. It can utilize complex
computer graphics to generate realistic scenarios. On the other hand, it also
can create simple scenarios using animated diagrams or simple graphics (e.g.
Business Data Visualization Forms
With the increasing recognition of data visualizations roles and values in a
business intelligence system, tools and applications that specifically target
business data visualization solutions have become widely available. This
section will review some most common types of data visualization forms
and tools used in BI.
Categorizing Business Data Visualization Forms
Typically, BI results are presented in the form of reports, dashboards, and
analytical tools. Among these, dashboards are mostly data visualization
driven. Reports are traditionally static and non-interactive, and they present
6 Data Visualization in Business Intelligence
more detailed data. Modern reports add a lot of elements of visualization
(either embedded visuals or charts/diagrams) and interaction, which enhance
reports readability. Analytical tools are also becoming more visually
oriented. Some analytical tools, labeled as visual analytical tools (or
analytical dashboards), are also driven by visualizations.
There are several commonly used visualization forms and tools in BI
reporting and analytics. There are three basic categories of visual forms
based on how visualizations are presented on screen: embedded visuals,
block visuals, and standalone visuals. Table 6.2 summarizes features and
examples of each one.
Table 6.2 Common Forms of Business Data Visualization
Typical Types and
Embedded visual
Inline chart
Block visual
Map (smaller)
Data table
(usually with embedded
Standalone visual
analysis tool (or an
analytical dashboard)
Map (bigger
or full screen)
Embedded Visuals
Embedded visuals are visual effects embedded in another form of
presentation. They are not independently presented but always used on top
of other presentation forms. Embedded visuals include two major forms:
conditional formatting and inline mini charts (or Sparkline).
Conditional formatting refers to the direct formatting or styling of text,
numbers, shapes, and other contents utilizing visual variables like color, size,
etc. (Bertin, 2010). Conditional formatting does not significantly change the
layout and flow of contents, thus it is less intrusive to the content. Instead, it
provides a decorative effect that reveals more meaning or highlights selected
content from the data or text.
6 Data Visualization in Business Intelligence
A Sparkline is a small minimized chart embedded in the context of text
paragraphs, tables, images, or other type of information. It presents the
general data pattern (variation, trends, differentiations, etc.) in a simple and
highly condensed way (Tufte, 2006). Interpretive and supporting information
like title, label, data point, legend, are omitted from the chart. A miniature
line chart (hence called Sparkline) is most commonly used, but it can be of
other chart types, including bar charts, bullet graphs, etc.
Charts and Diagrams
A block visual occupies a larger space but still part of a report or dashboard,
appearing together with other content. It is a more independent and self-
contained visual unit. Sometimes it can become a standalone visual if there
are many data points or enough visual and interaction complexity. Charts
and diagrams are the two most common forms of block visuals.
Charts are a visual combination of symbols (visual elements of point, line,
and area) and visual variables (color, shape, size, etc.) which are directly
associated with data. The terms of chart and diagram can sometimes be used
interchangeably without any explicit differences. More often, diagrams are
considered to include charts. In the context of business data visualization, a
chart is more abstract and focuses on visualizing quantitative values (e.g.
business performance measures and indicators), while a diagram can also
visualize qualitative information as well to illustrate structures, relationships,
sequences, etc. Charts and diagrams are the major forms of business data
visualizations used in BI. They are the fundamental piece to present data in
many reports and presentations.
Basic types of charts include line charts, bar charts, pie charts, etc., and
examples of diagrams include organization structure diagrams, tree
diagrams, network diagrams, workflows diagrams, etc. Abela (2008)
provides a basic categorization of charts by purpose; the visual guide has
been widely used for guiding chart choices (Table 6.3). Another purpose,
profiling, is also added to the table for a more complete comparison.
Profiling can be seen as a special case of comparison among multiple data
Table 6.3 Chart Chooser by Abela (2008)
6 Data Visualization in Business Intelligence
Example Charts
Comparing and sorting data
Bar/column chart, line chart,
radar chart
Showing part-to-whole
Stacked column/area chart, pie
Aggregated value (usually
count) of data points placed in
categories; the category can be
value ranges or time (trend).
Histogram, scatter plot, bubble
How things (data items) are
related or positioned in a
bigger context.
Scatter plot, bubble chart
Comprehending things through
visual shapes and patterns.
Radar chart, parallel
* Added by the author to enhance Abela’s version.
Other more specific types of charts are used in different business contexts
for more specific purposes. These charts are based on the more generic chart
types like bar charts and line charts, and add more specific visual elements,
or arrange the elements in a specific way to represent domain-specific
meanings. For example, bullet charts (based on bar charts) are used in
performance measuring; perceptual maps (based on scatter plots) are used in
marketing; waterfall or bridge charts (based on column charts) are used in
driving factor analysis; Gantt charts (based on data tables and bar charts) are
used in project management; funnel charts are used in sales; candlestick
charts are used in stock technical analysis.
Location-Based Visuals
Location as a dimension plays an important role in many areas of business
data analysis and decision-making. Many business activities are associated
with locations. It has been gaining increased attention especially with the
wide adoption of location sensors (like GPS and other location capture
technologies) which generate location data. Location-based visuals,
commonly based on a map, provide a background or a context that is
familiar to the users and make the location-related data more
comprehendible and perceivable. A 2015 yearly survey (Dresner, 2015)
map-based visualization of information as the top priority, and more
than 95 percent of respondents rank it as at least somewhat important.
More than 60 percent report that the functionality for layered
visualizations is very important or critical for their organization.
6 Data Visualization in Business Intelligence
The location-based visuals involve three basic factors: type of location
data, visual forms, data points representation on the map.
The types of location data are directly associated with a business and its
analysis. One major type of location data is geo locations that come with
real-world maps. Many places and regions are based on geospatial mapping,
such as political regions (country, state, city, etc.), various types of real
estate properties or areas (park, campus, road), or any other arbitrary
locations determined by businesses (postal ZIP area, sales region, service
district). A second type of location data is local contextual locations which
do not directly rely on geo coordinates. These locations are relative locations
within in a confined area, such as inside a park, campus, building, room,
court, bus/subway line, or even as small region such as a shelf, body, etc. For
example, many sports-related analytics analyze the data related to locations
on playing fields; stadium and airlines analyze seating data which relates to
locations; mall, hospitals, universities, and apartments analyze room/facility
usages which are also related to locations. The last type of locations is
associated with more abstract ideas like processes, computer networks,
organization structures, etc. These abstract locations can also be visualized
on an abstract map (or more like an illustration diagram).
The visualization forms for location data is how the background layer of
the map is presented. There are two broad categories: (a) real-world maps
are used as the background layer, then points, paths, and areas are displayed
accurately or closely proximate to the background (Figure 6.1a); (b) a more
abstract map (either geo location or non-geo location), sometimes just an
illustrational diagram, is used as the background layer, and positioning of
objects are based on relative position (e.g. X/Y coordinates) in the map
context. The positions or areas on the map are for illustration purpose only,
and not corresponding to their real-world positions or sizes (Figure 6.1b).
[Insert Figure 6.1a Here]
6 Data Visualization in Business Intelligence
[Insert Figure 6.1b Here]
Figure 6.1 Business Measures Visualized on MapsCreated Following (Taylor,
Figure 6.1b shows a type of abstract map called tile grid map (Shaw,
2016). Tile grid maps abstractly use similar-sized tiles to represent geo
regions with irregular sizes. It has several visual advantages in some cases
when location precision is not important:
6 Data Visualization in Business Intelligence
Eliminate map distortions on some real-world map projections. For
example, avoid the Alaska effect on US maps (Taylor, 2017).
Provide a more consistent view of places of irregular shape and different
sizes. In some cases, it makes smaller areas more visible.
Provide a more modern and consistent look and feel.
Standalone visuals are more like applications than visualizations. They
occupy even larger space or even full screens. They also contain multiple
types of content as well as interaction controls. A digital dashboard is a
major type of standalone visual. A dashboard is a visual display of the most
important information needed to achieve one or more objectives;
consolidated and arranged on a single screen so the information can be
monitored at a glance (Few, 2004). The term dashboard originally came
from operational status monitoring on machines which provides visual
display for quick reading. Its use has been expanded to visualization of
digital data associated with business performance on screens. A dashboard
(at the front end) is basically an integrated application of data (content),
visual views, and user interface/interaction (UI).
Dashboard = Data + Visualization + UI
The data on the dashboards primarily consists of metrics, Key
Performance Indicators (KPIs), and textual information. Metrics (or
measures, indicators) are numerical values that measure various aspects of
the business activities. A KPI is a metric that compares to its target (goal)
and other comparable benchmarks (performance intervals, historical periods,
or industry averages) (Barr, 2009). KPIs are used intensively in
performance-focused dashboards. Other common data on a dashboard
include a set of values to reflect history, trends, distributions, breakdowns,
forecasts, or other kinds of comparisons and relationships. Textual
information is not typically on many dashboards but it can be included
depending on the purpose of the dashboard.
The data on the dashboard are presented via a variety of views or
visualization forms discussed earlier, including charts, diagrams, tables (with
conditional formatting or other embedded visuals), and styled standalone
numbers (usually KPIs).
Last, a dashboard is a business application with a rich user interface for
users to interact with data. The key UI elements considered in dashboard
include layout (arrangements of data and visualizations, following human
information behavior best practices), overall formatting/styling components
(which can be visuals themselves) such as title and background, and user
6 Data Visualization in Business Intelligence
interaction controls such as command buttons and navigational controls
(menus, tabs).
Traditional BI reports contain detailed data in a tabular format (or pivot
tables) and typically display numbers and text only. The two main purposes
of reports are printing (with styling) and exporting (raw data). It is geared
towards people who need detailed data rather than direct analysis and
understanding of data. Modern BI reports can be interactive and visual, but
the focus is still on presenting detailed data. The distinction is a bit blurred
between reports and dashboards in some practical cases. For example, the IT
spending dashboard ( is more
like a report (a visual-intensive interactive report).
Compare to reports, dashboards are more focused on data visualizations
arranged in a single screen, or with limited scrolling and panning. Textual
information and detailed data tables can be part of a dashboard only if they
are necessary and important to user needs. Even so, it is better to present
detailed data through interactive means like pop-ups, tooltips, or in separate
screens via details-on-demand designs (Shneiderman, 1996).
A well-designed dashboard allows decision makers to see the most
relevant data that reflects business status and supports decisions. It is a
highly summarized and centralized snapshot that saves a users time by
eliminating the need to run multiple reports or get data through different
sources. It should allow the user to quickly understand data and respond
promptly at one place (, n.d.).
As the use of dashboards grew, they have expanded into three basic types
of dashboard: overview, operational, and analytical. Each of them share
common attributes of dashboards (data + visualization + UI), but each of
them has some different purpose, data, and design best practices.
Operational dashboards display data that facilitate the operational side of a
business, monitoring operational activities and statuses as they are
happening. They provide views of important operational indicators, often
based on real-time or near real-time data; they focus on current performance
and are action-oriented. Summary/overview dashboards provide high-level
summary of business performance represented by KPIs. A strategic
dashboard is a typical example of a summary/overview dashboard at the
strategic or executive level, which specifically concerns the state of the
overall business against strategy goals.
Analytical dashboards, or visual analysis tools, focus on interactive
exploration or analysis (visual analysis) of a large amount of data. They
allow users to investigate trends, predict outcomes, and discover insights.
This kind of use is a bit different from traditional dashboards, which are
primarily used for quick scanning and understating of key metrics. Some
people specifically categorizes them as visual analysis tools (Chiang,
2011) rather than a type of dashboard. Nonetheless, the design of analytical
dashboards is similar to general dashboards with a focus on data,
6 Data Visualization in Business Intelligence
visualization, and UI design. Analytical dashboards usually visualize
patterns, trends, and other complex relationships among a large amount of
data, without a significant focus on a few metrics. The purpose is to explore
data and analyze patterns through an interactive process. They contain
abundant parameter settings, selections, filters, and other controls to
manipulate the main visualization. The main visualization is usually a single
(or very few) visual component that occupies a big portion of the screen as
the main UI component, with a large number of data points displayed on it.
Users major activity will be repeatedly setting parameters and examining
the generated visuals. This type of dashboard often supports ad hoc
querying, dynamic visualization generation, common OLAP operations like
drill down. It is primarily used for intensive data exploration or analysis,
used by data analysts and researchers.
Trends and Prospects
Data visualization has been one of the growing forces driving the BI
industry. As an important part of modern BI systems and platforms, business
data visualization closely follows or even impacts the general BI and
analytics trends. Some of the notable trends are presented below.
Personal or self-service BI: Self-service BI features control in the hands
of users, especially power users. This group of people is highly skilled in
using technology applications in business tasks, and they often need instant
results. They are able to use computer tools and languages to get what they
want with little assistance from their IT departments. Some of the tools like
Tableau and Power BI have quickly risen to satisfy this need using a
visualization-driven approach and gained wide recognition (Sallam, 2017).
Embedded BI: Personal BI tools are usually standalone tools which need a
separate data connection or import process. Embedded BI emphasizes the
analytics and data presentation as an integral part of an application, instead
of using an independent tool or system. Embedded BI or visualization has an
advantage in local data modeling and integration, thus delivers standard
reports and dashboards in a more efficient way to satisfy the most common
needs. The analytics component has become a competitive component of
many business systems. However, in many systems, the module is often seen
as a separate module which requires businesses to pay separately (for
example, Brightspace Insights).
Mobile BI: Mobile computing also drives the evolvement of BI and data
visualization to be more accessible and usable on multiple devices with
different screen sizes and interaction techniques. Although not typically for
mobile phones, access to dashboards though tablets or tablet-like devices is
increasing in many business environments where people move around
regularly, such as sales, field support, sports, and hospitals. The major
influence from mobile computing is the interaction method of touch-oriented
6 Data Visualization in Business Intelligence
interfaces, which requires some new design principles and best practices on
dashboard interactions.
The job market and career development related to data analysis and
visualization have seen an increasing demand over recent years. Preferably,
data visualization development requires skills from a number of fields,
including information design, UI design, human information behavior and
cognition, interaction design, artistic design, programming, data processing,
and business domain knowledge. The demand for multi-skill and
interdisciplinary experience will continue to grow.
The shifting focus on end users with better and more effective data
presentation and visualization is a global phenomenon. Business data
visualization has an increasing importance in the complete BI process and is
becoming an integral part of any BI system. Various forms of data
visualization, each with their unique features, help BI users and decision
makers at different levels from different perspectives. BI managers and
developers should understand their features, strengths and weakness, and use
them together to create a good mix that satisfies different types of users with
different needs. This chapter provides a comprehensive review of these
visualization forms and tools, which will help BI managers, decision makers,
analysts, and developers better select and utilize them. The perception of
visualization is common across all cultures and business environment, but
the meaning delivered through visualizations may be affected by a number
of factors like color and orientation. Adapting data visualization solutions in
local culture is also important in the global business environment.
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... Differences between MCS and other disciplines have implications on visualization research findings as they are often "standard participants" for many studies on task performance and design choices; a considerable amount of information visualization is also created and studied by those in MCS related fields [20,21,87]. We chose Business as there is a wealth of literature exploring data visualization for business intelligence and business decisions, which often cites design and cognition research [36,57,84,95]. However, we are unaware of any comparative studies between MCS and Business domain cognitive abilities applied to data visualization task performance. ...
... However, they found correlations between spatial visualization and task performance across all disciplines, implying that spatial visualization is an important underlying source of variations. We anticipate marked differences between MCS, Business, and LPS given the three domains varying levels of interaction, creation, and consumption of data visualization [38,95,97], with MCS performing the best and LPS the worst following their spatial visualization levels. Confirming this would substantiate the work of Hall et al., that effective visualizations should consider not only the needs of a discipline, but the abilities of the individuals within domains. ...
... We conjecture this trade-off, and difficulty perception, has to do with the motivations and domain differences between Business and LPS. Literature suggests there is higher value placed on data visualization in Business [36,57,84,95] when compared to LPS [46,97]. This is reflected in the motivations of Business and LPS participants in our study (see Table 1). ...
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... vividness (Reynolds et al., 2018;Zheng, 2017); still, their effectiveness in crisis communication has produced mixed results and they have only been examined in a limited way (e.g., Claeys et al., 2021;Stewart, 2014). ...
... Moreover, infographics have been suggested to generate higher social media engagement (Ahmad, 2016), making them a highly effective tool for sharing information across multiple platforms. Researchers have also found that information presented through infographics is perceived as more vivid than other formats (Reynolds et al., 2018;Zheng, 2017). ...
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... Strengths (GF_15): Data visualization is critical for firms to identify data trends, relationships and structures quickly, processes that would ordinarily be time-consuming. 98 Moreover, analysts can perceive concepts and new patterns thanks to the graphical depiction of datasets. 98 In addition, a proper understanding of a quintillion bytes of data is difficult without data proliferation, which incorporates data visualization into the rising rush of daily data. ...
... 98 Moreover, analysts can perceive concepts and new patterns thanks to the graphical depiction of datasets. 98 In addition, a proper understanding of a quintillion bytes of data is difficult without data proliferation, which incorporates data visualization into the rising rush of daily data. 44 Pavlopoulos et al. 89 stated that Pajek's strength is in its variety of layout algorithms, while PIVOT, Medusa and ProViz are best suited for PPI visualization. ...
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... Organizations generate vast amounts of data, and data visualization provides a comprehensive means of summarizing this data. It facilitates communication, reduces misinterpretation, and offers insights by visually representing data through tools like dashboards and charts [12,13,14]. ...
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... Reporting refers to the production, delivery and management of reports (Vaisman and Zimányi, 2022), i.e. static or interactive overviews of business facts, such as key performance indicators (KPIs) and corresponding visualizations (Zheng, 2017). For the automatic generation of reports, predefined queries are typically employed and periodically executed against the stored data (Vaisman and Zimányi, 2022). ...
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... Data visualization techniques use visual representation of data and usually perform data reduction, and transformation [12]. For instance, dashboards enable users to investigate and track trends, predict outcomes, and discover insights, using quick scanning and understating key metrics [13]. ...
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The graphic portrayal of quantitative information has deep roots. These roots reach into histories of thematic cartography, statistical graphics, and data visualization, which are intertwined with each other. They also connect with the rise of statistical thinking up through the 19th century, and developments in technology into the 20th century. From above ground, we can see the current fruit; we must look below to see its pedigree and germination. There certainly have been many new things in the world of visualization; but unless you know its history, everything might seem novel.
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Guidelines for designing information charts often state that the presentation should reduce ‗chart junk' - visual embellishments that are not essential to understanding the data. In contrast, some popular chart designers wrap the presented data in detailed and elaborate imagery, raising the questions of whether this imagery is really as detrimental to understanding as has been proposed, and whether the visual embellishment may have other benefits. To investigate these issues, we conducted an experiment that compared embellished charts with plain ones, and measured both interpretation accuracy and long-term recall. We found that people's accuracy in describing the embellished charts was no worse than for plain charts, and that their recall after a two-to-three-week gap was significantly better. Although we are cautious about recommending that all charts be produced in this style, our results question some of the premises of the minimalist approach to chart design.
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An ongoing debate in the Visualization community concerns the role that visualization types play in data understanding. In human cognition, understanding and memorability are intertwined. As a first step towards being able to ask questions about impact and effectiveness, here we ask: 'What makes a visualization memorable?' We ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon's Mechanical Turk, we collected memorability scores for hundreds of these visualizations, and discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types). Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards determining how to design effective visualizations.
Conference Paper
Information visualization is traditionally viewed as a tool for data exploration and hypothesis formation. Because of its roots in scientific reasoning, visualization has traditionally been viewed as an analytical tool for sensemaking. In recent years, however, both the mainstreaming of computer graphics and the democratization of data sources on the Internet have had important repercussions in the field of information visualization. With the ability to create visual representations of data on home computers, artists and designers have taken matters into their own hands and expanded the conceptual horizon of infovis as artistic practice. This paper presents a brief survey of projects in the field of artistic information visualization and a preliminary examination of how artists appropriate and repurpose "scientific" techniques to create pieces that actively guide analytical reasoning and encourage a contextualized reading of their subject matter.
Data visualization is regularly promoted for its ability to reveal stories within data, yet these “data stories” differ in important ways from traditional forms of storytelling. Storytellers, especially online journalists, have increasingly been integrating visualizations into their narratives, in some cases allowing the visualization to function in place of a written story. In this paper, we systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration). Our framework suggests design strategies for narrative visualization, including promising under-explored approaches to journalistic storytelling and educational media.