http://idi.kennesaw.edu/it7113/

Chart Design Process
IT 7113 Data Visualization
J.G. Zheng
Spring 2025
http://idi.Kennesaw.edu/it7113/
Content Overview
This lecture notes discuss two chart design
issues.
1. Chart design process and considerations
2. Tool selection
2
Why do we need a design process?
• A design process involves a defined set of
design considerations and tasks.
– A process consists of steps arranged in an order.
– A repeatable and defined design process embodies
maturity in design capability and experience.
• Benefits:
– serves as a guide and a checklist to plan and
manage the whole project
– reduces the randomness and improves efficiency
3

https://digitalimpact.io/getting-started-a-3-step-approach-to-data-visualization/


https://health.usf.edu/-/media/v3/usf-health/medicine/Internal-Medicine/IMpact/Files/a-5-step-guide-to-data-visualization.ashx


https://depictdatastudio.com/data-visualization-design-process-step-by-step-guide-for-beginners/


https://www.youtube.com/watch?v=GVkXbQOzKNs&t=754s


https://www.interaction-design.org/literature/article/how-to-design-an-information-visualization

Example Processes
• There are various ways to define a design process or a list of
consideration.
– Each process consists of configurable steps and actions.
– A process can be flexible
• For example:
– A 3-Step Approach To Data Visualization
https://digitalimpact.io/getting-started-a-3-step-approach-to-data-
visualization/
– A 5-step guide to data visualization
https://health.usf.edu/-/media/v3/usf-health/medicine/Internal-
Medicine/IMpact/Files/a-5-step-guide-to-data-visualization.ashx
– Data Visualization Design Process: A Step-by-Step Guide for Beginners
https://depictdatastudio.com/data-visualization-design-process-step-by-
step-guide-for-beginners/
– Andy Kirk’s 4 stages (also in Andy Kirk’s book “Data at Work” Chapter 2)
https://www.youtube.com/watch?v=GVkXbQOzKNs&t=754s
– Design process for information visualization
https://www.interaction-design.org/literature/article/how-to-design-an-
information-visualization
4
A Basic Chart Design Process
Requirement
analysis
• Set goals,
objectives,
messages
Chart type
choice
• Choose one
basic chart
type
(general or
industry
specific)
based on a
number of
factors
(mainly
purposes
and data
features)
Representation
design
• Determine
visual data
coding,
involving
visual
mapping and
visual
properties
(SCOPeS)
Presentation
design
• Apply
perceptual
and attention
shaping best
practices (for
example,
pre-attentive
processing
and Gestalt
principles) to
make charts
more
effective and
efficient
5
The following is a basic simple process for the most common scenario:
We need to visualize all data using a commonly used chart type (or
with limited alterations).
Details of each step/task are presented in following slides.
1. Goals and Contexts
• Before any development or technical work.
• Analyze the requirements and be clear about
the following factors, which impact all following
steps.
• Key questions
– What is the general goal and purpose of this data
visualization app?
– What message (or what story) are we trying to
communicate through this visual?
– What part of the dataset or metrics are we focusing
on?
– Who is the audience?
6

https://www.qlik.com/blog/third-pillar-of-mapping-data-to-visualizations-usage

General Charting Purposes
Purpose/function Description
Comparison
Comparing and sorting data points; can also
compare to benchmarks or norms.
Composition
A hierarchy relationship. Also, it may imply part-
to-whole comparisons.
Distribution
Aggregated value (usually count) of data points
placed in categories; the category can be value
ranges or time (trend).
Relationship
How things (data items) are related or positioned
in a bigger context.
Trend/evolution
Variation of comparison involving temporal data.
Profiling
To comprehend things through visual shapes
and patterns.
7
Review the six general purposes or categories of charts in module 3
– https://www.qlik.com/blog/third-pillar-of-mapping-data-to-visualizations-usage (the basic four)
– http://www.excelcharts.com/blog/classification-chart-types/ (added evolution and profiling)

https://www.storytellingwithdata.com/blog/2012/10/my-penchant-for-horizontal-bar-graphs

Messages vs. General Purposes
• What message does it deliver?
– Messages are more granular details, insights, or stories you want to
emphasize.
– Purposes focus on big picture for exploration, while messages focus on
more specific insights for communication.
– Messages may be based on selected data or metrics.
– The latest story telling trend in data visualization focuses more on the
message.
• Example
– General purpose: compare student enrollments in different degree
programs across three departments
– One may want to deliver different messages:
•
to emphasize the size of MSIT as the biggest program, or
•
to show insignificance between programs/departments
• Another example:
8
https://www.storytellingwithdata.com/blog/2012/10/my-
penchant-for-horizontal-bar-graphs
A general purpose on
comparison among areas
A more specific
message or point
Audience
• Consider specific user needs and preferences
– User’s familiarity of certain charts or techniques
• Some chart types may be more complicated for a less-experienced
person to understand, but they can communicate information better
for more advanced users. For example, radar chart or dual axis
chart.
– Personal preferences: some may prefer more condense
visual format while other prefer guided story style with
narratives
– Corporate culture
– Business sector/industry
• These considerations will influence the choice of more
specific chart type and presentation style.
• For example, users (audiences) from a certain industry
may have a convention and expectation of using certain
color scheme, type of charts, and layout.
9

https://flowingdata.com/2017/01/24/one-dataset-visualized-25-ways/


https://public.tableau.com/app/profile/maryia.maziuk/viz/Onedataset20visualizationsRetirementAgesinOECDCountries/Onedataset20visualizations

2. Chart Choice
• With the same data set, there could be many
ways of presenting it
– https://flowingdata.com/2017/01/24/one-dataset-
visualized-25-ways/
– One dataset 20 visualizations | Retirement ages
in OECD countries | Tableau Public
10

https://www.edocr.com/v/lonn6pal/jgzheng/chart-types-and-purposes

How to Choose?
Two important considerations:
1. Purposes and messages
– Start with some general grand purposes; match the intended
purpose and the chart’s major function. Please review slide #7 and
module 3 for more details.
– Grand purposes and categories still may lead to multiple choices;
so, this is just a starting point. Elaborate with specific messages
(see slide #8)
2. Data features
– What kind of data? Need to know data types, structures, and # of
attributes and data items.
3. Other considerations (not focused on in this lecture)
– Shape and size of charting space might be a factor
– User/audience needs, preferences, conventions, etc.
– Emotion, affection, culture, etc.
– Chart customization: is one chart enough?
11

https://extremepresentation.com/design/7-charts/


https://www.qlik.com/blog/third-pillar-of-mapping-data-to-visualizations-usage


https://excelcharts.com/classification-chart-types/


https://www.juiceanalytics.com/chartchooser


https://policyviz.com/2014/09/09/graphic-continuum/


https://www.informationisbeautifulawards.com/showcase/611-the-graphic-continuum


https://www.ft.com/vocabulary


http://experception.net/


http://datavizproject.com/


http://www.datavizcatalogue.com/


https://www.data-to-viz.com/


http://chartmaker.visualisingdata.com/

Initial Selection of Chart Type
12
• Match the intended purpose and the chart’s major function.
– Many guides and tools are created to guide the selection of chart
types based on purposes. Some of them are one-page quick
references and others are more interactive and detailed.
– Please review module 3 for more details.
Abela's version
• https://extremepresentation.com/de
sign/7-charts/
• https://www.qlik.com/blog/third-
pillar-of-mapping-data-to-
visualizations-usage
Camões’s version
https://excelcharts.com/classification-
chart-types/
Juice Analytics
https://www.juiceanalytics.com/chartc
hooser
Schwabish’s Graphic
Continuum
• https://policyviz.com/2014/09/09/graphi
c-continuum/
• https://www.informationisbeautifulawar
ds.com/showcase/611-the-graphic-
continuum
Financial Times Visual
Vocabulary
https://www.ft.com/vocabulary
Fraconeri’s version
http://experception.net
Ferdio*
http://datavizproject.com
Data catalog*
http://www.datavizcatalogue.com
From Data to Viz
https://www.data-to-viz.com
Chart make directory
http://chartmaker.visualisingdata.c
om

https://www.pluralsight.com/guides/tableau-playbook-waffle-chart

Examine Data Set Features
•
Important data features to consider
– Data size: how many data items or data serials will be in the chart?
– Data type: temporal data, geo data, textual data, performance data, etc.
•
Data size: different chart types have different limit for data sizes. Understand each chart type’s
limit.
– How many data points or items (rows of data)?
– How many dimensions or attributes (variables)?
• Variable/measure type – including data value range, measuring unit, business meaning, etc.
– Measure vs. dimension
– Numerical, ordinal, and nominal (categorical)
– Are they same in range and unit?
– Are data needed to be presented of consistent type of mixed?
• Data structure
–
Is there any specific relationship between data items and between variables, such as hierarchical?
•
In addition, data model
–
Is the source data in a format suitable for the intended visual/chart?
– Do we need to structure/transform data set in a particular form for desired visualization?
– Depending on the tool, there may be a specific way to structure and transform the data, or special
calculations need to be performed. For example, waffle chart in Tableau
https://www.pluralsight.com/guides/tableau-playbook-waffle-chart
13

https://www.data-to-viz.com/


https://datavizproject.com/

Data Size (Variables) Impact on Chart Choice
14
See how data set features are
described at https://www.data-
to-viz.com and
https://datavizproject.com
(under the “Input” menu item)
Number of variables
leads to different chart
types
Data Size (Items) Impact on Chart Choice
• A few data items/records <5  Pie chart, column/bar chart
• Quite some items <10 or 20  Bar chart, line, bubble chart
• Many data items (rows) >20  Profile charts, tree map, bubble chart,
scatterplot, line charts (many time periods), parallel coordinates, etc.
15
Pie chart is a mess when handling
too many data items; and it can only
display one variable.
Bubble charts can
handle up to 4 variables,
and many data items.

https://www.edocr.com/v/631d1wpb/jgzheng/scopes-visual-properties

3. Representation (Visual Features) Design
• A chart type only provides a foundation or framework to the final chart design.
The next step is to apply various visual features.
• Note a chart type already sets a framework for most of the visual mapping on
measures – column chart maps values to size of the columns, etc.
This usually involves two major tasks.
1.
Visual mapping – mapping of data to visual elements of the chosen chart; this
is usually chart specific
– For example, in a dual axis combo chart, which data serials should be mapped to x axis or
y axis, etc.; which data should be represented by bars, etc.
– Or in a cluster column chart, determining clustering order.
– Or in a bubble chart, choose which dimension to be coded (as the bubble chart support
limited number of dimensions)
2.
Choice of visual property and visual encoding for data
– SCOPeS (refer to module 2 https://www.edocr.com/v/631d1wpb/jgzheng/scopes-visual-
properties). For example, choosing colors or sizing options.
– Proper use of visual variables; apply the right visual decoration/properties.
– Color choices

http://kenhirakawa.com/significance-of-contrast/


https://www.coursera.org/lecture/dataviz-design/strategic-use-of-contrast-sDV6C


https://www.webfx.com/blog/web-design/data-visualization-gestalt-laws/


https://www.edocr.com/v/e6ql9njn/jgzheng/data-visual-foundation

4. Presentational Design
• The main focuses of presentational design is the usability and
perceptual enhancement features, which involves preattentive
features and Gestalt laws
• Apply pre-attentive attributes to distinguish the major data points
or serials, or ones closely related to your message.
– Pre-attentive processing can help to rapidly draw the focus of
attention to a target with a unique visual feature
– For example, using contrast to differentiate the part that needs to
draw attention
– http://kenhirakawa.com/significance-of-contrast/
– https://www.coursera.org/lecture/dataviz-design/strategic-use-of-
contrast-sDV6C
• Apply Gestalt principles to group and sort data points and chart
objects
• https://www.webfx.com/blog/web-design/data-visualization-gestalt-laws/
17
For details, refer to learning module 2
https://www.edocr.com/v/e6ql9njn/jgzheng/dat
a-visual-foundation
Other Presentational Issues
• Contextual data
– Benchmark, reference line, trend line,
total, average, difference, quartile,
estimate, confidence range, etc.
– How to add them?
• added directly in the chart, as a data
serial
• added as background handled by the
system
• using annotations
• Other UI and decorative features
– font, grid line, shading, etc.
• Descriptive and explanatory features
– including title, legend, annotation,
label, etc.
18
Note: these issues are not
focused on in this class.
We will briefly mention
general UI design
principles and guidelines
in module 5. Please apply
them based on your own
study and experience.
Need Customization?
• Most of the time, we start with a conventional and standard chart, and
that may be enough.
• Sometimes, we have some need to go beyond conventional chart
types.
• Customization examples
– Add more custom objects and properties
–
Incorporate features from other chart types
– Using additional charts
• Need to stack or overlay additional chart to make desired effect
• These additional charts can be hierarchical, supplementary, or just change of
view/perspective.
• Arrangement of multiple charts: need to consider position, layout, sequence,
transition.
• Multiple charts can be placed side by side, stacked, overlapped, and sequenced.
•
If, none of the standard chart and customization is satisfying – rare
situation but it happens
– Design some unique and innovative visualizations beyond common well-
known chart types – a good research topic.
19
Other Design Issues
• Charting principles and best practices (see
module 5)
• Interactivity features (see module 11)
20

http://chartmaker.visualisingdata.com/

Choosing and Using a Visualization Tool
• Modern visualizations are largely dependent or enabled by
visualization software tools
• The design of the visualization should also consider tool features
and capabilities
• Know tool (software application) features
– Data handling features: complexity, volume, structure, source
import, calculation, data modeling/structuring and transformation
capability, etc.
– The possibility or complexity of certain types of charts
– Customization and hacks: overlapping, static add, scripting,
annotation
– Data update: how, frequency
– Delivery medium: screen type, web, etc.
– Skills sets and needs of users and developers
• Refer to this web app for a summary of tools and features match
– http://chartmaker.visualisingdata.com
21

https://youtu.be/GVkXbQOzKNs?t=2152

A Design Case
22
https://youtu.be/GVkXbQOzKNs?t=2152 The
discussion of the case started at 35:52

https://www.storytellingwithdata.com/blog/2021/1/10/lets-improve-this-graph-yt9xj


https://www.storytellingwithdata.com/blog/2018/6/5/an-alternative-to-treemaps

More Cases
• What’s your point? Communicate the most important insight, from a story telling
perspective
– https://www.storytellingwithdata.com/blog/2021/1/10/lets-improve-this-graph-yt9xj
• An alternative to tree maps
– https://www.storytellingwithdata.com/blog/2018/6/5/an-alternative-to-treemaps

https://digitalimpact.io/getting-started-a-3-step-approach-to-data-visualization/


https://health.usf.edu/-/media/v3/usf-health/medicine/Internal-Medicine/IMpact/Files/a-5-step-guide-to-data-visualization.ashx


https://depictdatastudio.com/data-visualization-design-process-step-by-step-guide-for-beginners/

Readings
• A 3-Step Approach To Data Visualization
https://digitalimpact.io/getting-started-a-3-step-
approach-to-data-visualization/
• A 5-step guide to data visualization
https://health.usf.edu/-/media/v3/usf-
health/medicine/Internal-
Medicine/IMpact/Files/a-5-step-guide-to-data-
visualization.ashx
• The Data Visualization Design Process: A Step-
by-Step Guide for Beginners
https://depictdatastudio.com/data-visualization-
design-process-step-by-step-guide-for-
beginners/
24

https://www.youtube.com/watch?v=GVkXbQOzKNs&t=754s


https://www.interaction-design.org/literature/article/how-to-design-an-information-visualization


https://www.datarevelations.com/accurate-vs-emotional-comparisons-sometimes-pies-bubbles-and-waffles-are-the-better-choice/

Additional Good Resources
• Andy Kirk’s 4 stages:
https://www.youtube.com/watch?v=GVkXbQOz
KNs&t=754s or Andy Kirk’s book “Data at Work”
Chapter 2
• Design process for information visualization
https://www.interaction-
design.org/literature/article/how-to-design-an-
information-visualization
• Accurate vs. Emotional Comparisons
https://www.datarevelations.com/accurate-vs-
emotional-comparisons-sometimes-pies-
bubbles-and-waffles-are-the-better-choice/
25