Data vizualization Techniques in Data Analytics and visualization

Data Visualization
Data visualization is the process of acquiring, interpreting, and comparing data in order to clearly
communicate
complex ideas, thereby facilitating the identification and analysis of meaningful
patterns.
•Data visualization can be essential to strategic communication: it helps us interpret available data; detect
patterns, trends, and anomalies; make decisions, and analyze inherent processes. All told, it can have a powerful
impact on the business world.
• Data Visualization Application enables users to visualize data, draw insights and understand it
better. It allows people to organize and present information intuitively. People can understand pictures
better than tables that contain rows and columns.
Tableau, Roambi, Qlik, Salesforce Einstein Analytics, High Charts, Google Charts, Fusion Charts,
Infogram, Sisence, and Final Words are some of the Web Applications for Data Visualization

Stages in Data visualization
process
Several different fields are involved in the data visualization process, with the aim of simplifying or
revealing existing relationships or discovering something new within a data set.
•Filtering & processing. Refining and cleaning data to convert it into information through analysis,
interpretation,
contextualization, comparison, and research.
•Translation & visual representation. Shaping the visual representation by defining graphic resources,
language, context, and the tone of the representation, all of which are adapted for the recipient.
•Perception & interpretation. Finally, the visualization becomes effective when it has a perceptive impact on
the construction of knowledge.

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Advantages of visualization
• Visualization provides an ability to comprehend huge amounts of data. The important information from
more than a million measurements is immediately available.
• Visualization allows the perception of emergent properties that were not anticipated. In this visualization,
the fact that the pockmarks appear in lines is immediately evident. The perception of a pattern can often
be the basis of new insight. In this case, the pockmarks align with the direction of geological faults,
suggesting a cause. They may be due to the release of gas.
• Visualization often enables problems with the data to become immediately apparent. A
visualization commonly reveals things not only about the data itself but also about the way it is collected.
With appropriate visualization, errors and artifacts in the data often jump out at you. For this reason,
visualizations can be invaluable in quality control.
• Visualization facilitates understanding of both large-scale and small-scale features of the data. It can
be especially valuable in allowing the perception of patterns linking local features.

Visualization Stages
The process of data visualization includes four basic stages, combined in a number of feedback loops.
These are illustrated in the below Figure.
The four stages consist of:
● The collection and storage of data.
● A preprocessing stage is designed to transform the data into something that is easier to manipulate. Usually,
there is some form of data reduction to reveal selected aspects. Data exploration is the process of changing
the subset that is currently being viewed.
● Mapping from the selected data to a visual representation is accomplished through computer algorithms that
produce an image on the screen. User input can transform the mappings, highlight subsets, or
transform the view. Generally, this is done on the user’s own computer.
● The human perceptual and cognitive system (the
perceiver).
Figure: Visualization process.

Why is data visualization so
important inreports and
statement
s?
We live in the era of visual information, and visual
content plays an important role in every moment of
our lives. A study by SH!FT Disruptive Learning
demon- strated that we typically process images
60,000 times faster than a table or a text, and
that our brains typically do a better job remembering
them in the long term. That same research detected
that after three days, analyzed subjects retained
between 10%and 20% of written or spoken
information, compared with 65% of visual information.
The rationale behind the
power of visuals:
• The human mind can see an image for just
13mil- liseconds and store the information,
provided that it is associated with a concept. Our
eyes can take in 36,000 visual messages per
hour.
• 40% of nerve fibers are connected to the
retina.
All of this indicates that human beings are better
at processing visual information, which is lodged
in our long-term memory.
Consequently, for reports and statements, a visual
representation that uses images is a much more
effective way to communicate information than text
or a table; it also takes up muchless space.
This means that data visuals are more
attractive, simpler to take in, and easier to
remember.
Try it for yourself. Take a look at this
table:
Identifying the evolution of sales over the course of
the year isn’t easy. However, when we present the
same information in a visual, the results are much
clearer (see the graph below).
The graph takes what the numbers cannot communi-
cate on their own and conveys it in a visible,
memorable way. This is the real strength of data
visualization.
Month
Sale
s
20
40
36
Jan Feb Mar Apr May Jun
9
60
80
100
56
45
58
75
62
Graphical excellence is that which gives
to the viewer the greatest number of
ideas in the shortest time with the
least ink in the smallest space.”
- Edward
Tufte(2001)
Month Jan Feb Mar Apr May Jun
Sales 45 56 36 58 75 62

Data visualization chiefly helps in 3 key
aspects of reports and statements:
1)Explaining
Visuals aim to lead the viewer down a path in order to describe situations, answer
questions, support decisions, communicate information, or solve specific
problems. When you attempt to explain something through data visualization, you
start with a question, which interacts with the data set in such a way that enables
viewers to make a decision and, subsequently, answer the question.
For example: This graphic below could clearly explain the country with the
greatest demand for a certain product compared globally, in a concrete month.
For example: an interactive graphic from The Guardian2
invites us to explore how
the linguistic standard of U.S. presidential addresses has declined over time. The
visual is interactive and explanatory, in addition to indicating the readability score
of various presidents’ speeches.
3)Analyzin
g
Other visuals prompt viewers to inspect, distill, and transform the most significant
information in a data set so that they can discover something new or predict
upcom- ing situations.
For example: this interactive graphic about learning machine3
invites us to explore
and discover information within the visual by scrolling through it. Using the
machine learning method, the visual explains the patterns detected in the data in
order to categorize characteristics.
We’ll close this introduction with a 2012 reflection by Alberto Cairo, a
specialist in information visualization and a leader in the world of data
visualization. For the author, a good visual must provide clarity, highlight
trends, uncover patterns, and reveal unseen realities:
We create visuals so that users can analyze data and, from
it, discover realities that not even the designer, in some
instances, had considered.”
2 Available at: https://www.fusioncharts.com/whitepapers/downloads/Principles-of-Data-Visualization.pdf
3 Available at: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
100
0
300
200
500
400
2)Explorin
g
Some visuals are designed to lend a data set spatial dimensions, or to offer
numerous subsets of data in order to raise questions, find answers, and discover
opportunities. When the goal of a visual is to explore, the viewers start by
familiarizing themselves with the dataset, then identifying an area of interest,
asking questions, exploring, and finding several solutions or answers.
United Russia South Europe Canada Australia Japan
States Africa

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Data
types,
relationship
s,
2.and
visualization
formats

Data types,
relationships,
and visualization
formats
There are a number of methods and approaches to
creating visuals based on the nature and
complexity of the data and the information.
Different kinds of graphics are used in data
visualizations, including representations of
statistics, maps, and diagrams. These
schematic, visual representations of content vary in
their degree of abstraction.
In order to communicate effectively, it is important
to understand different kinds of data and to
establish visual relationships through the proper use
of graphics. Enrique Rodríguez (2012), a data
analyst at DataNauta, once explained in an
interview that...
2)Qualitative
(categoric)
This kind of data is divided into categories based
on non-numeric characteristics. It may or may not
have a logical order, and it measures qualities and
generates categorical answers. It can be:
• Ordinal: Meaning it follows an order or sequence.
That might be the alphabet or the months of the
year.
• Categorical: Meaning it follows no fixed order. For
example, varieties of products sold.
2 kinds of data
Before we talk about visuals themselves, we must first understand the
different kinds of data that can be visualized and how they relate to one
another.
The most common kinds of data are4
:
Quantitative Qualitativ
e
A good graphic is one that
synthesizes and contextualizes all of
the information that’s necessary to
understand a situa- tion and decide
how to move forward.”
1)Quantitative
(numeric)
Data that can be quantified and measured. This kind
of data explains a trend or the results of research
through numeric values. This category of data can
be further subdivided into:
•Discrete: Data that consists of whole numbers (0, 1,
2, 3...). For example, the number of children in a
family.
• Continuous: Data that can take any value within
an
interval. For example, people’s height (between 60 -
70 inches) or weight (between 90 and 110 pounds).
5 Source: Hubspot, Prezy, and Infogram (2018). Presenting Data People
Can’t Ignore: How to Communicate Effectively Using Data. |p.10 of 16 |
Available at: https://offers.hubspot.com/presenting-data-people-cant-ignore.

7data relationships
Data relationships can be simple, like the progress of a single metric over time (such as visits to a blog over the course of 30 days or the number of users on a social
network),
or they can be complex, precisely comparing relationships, revealing structure, and extracting patterns from data. There are seven data relationships to consider:
Ranking: A visualization that relates two or more
values with respect to a relative magnitude. For
example: a company’s most sold products.
Deviation: Examines how each data point relates to
the others and, particularly, to what point its value
differs from the average. For example: the line of
deviation for tickets to an amusement park sold on a
rainy versus a normal day.
Correlation: Data with two or more variables that can
demonstrate a positive or negative correlation with
one another. For example: salaries based on level of
education.
Distribution: Visualization that shows the
distribution of data spatially, often around a
central value.
For example: the heights of players on a
basketball team.
Partial and total relationships: Show a subset of
data as compared with a larger total. For example:
the per- centage of clients that buy specific products.
Nominal comparisons: Visualizations that
compare quantitative values from different
subcategories. For example: product prices
invarious supermarkets.
Series over time: Here we can trace the changes in
the values of a constant metric over the course of
time. For example: monthly sales of a product over
the course of two years.

11formats
There are two types of visualizations: static and
interactive. Their use depends on the search
and analysis dimension level. Static visuals
can only analyze data in one dimension,
whereas interactive visuals can analyze it
in several.
As with any other form of communication,
familiarity with the code and resources that are
available to us is essential if we’re going to use
them successfully our goal. In this page, we
present the different kinds of graphics that we
can use to transform our data into information.
This group of visualization types
is listed in order of popularity in the “Visualization
Universe” project by Google News Lab and
Adioma, as of the publication of this report.
1.Bar
chart
Bar charts are one of the most popular ways of
visualizing data because they present a data set
in a quickly understood format that enables
viewers to identify highs and lows at a glance.
Vertical
column
Used for chronological data, and
it should be in left-to-right
format.
Horizontal
column
Used to visualize
categories.
Full
stackedcolumn
Used to visualize
categoriesthat collectively
add up to 100%.
6,000
5,500
5,000
4,000
3,500
3,000
2,500
2,000
1,500
1,00
0
500
0
Jan Feb M
ar
Apr M
ay
0% 20% 40%
60%
80% 100% 0% 20% 40%
60%
80% 100%
Education
Entertainment
Heatlh
Jan
They are very versatile, and they are typically
used to compare discrete categories, to
analyze changes over time, or to compare
parts of a whole.
The three variations on the bar chart are:

3.
Piecharts
Pie charts consist of a circle divided into sectors,
each of which represents a portion of the total.
They can be subdivided into no more than five data
groups. They can be useful for comparing discrete or
continuous data. The two variations on the pie chart
are:
• Standard: Used to exhibit relationship between
parts.
• Donut: A stylistic variation that facilitates the inclu-
sion of a total value or a design element in the
center.
-60 -40 -20 0 20 40 60
Horizontal
columns
<60
60-80
81-
100
101-
120
>12
0
Vertical
columns
400K
350K
300K
250K
200K
150K
100K
50K
0
25 30 35 40 45 50
55
60 65
A
B
Standard
piechart
B
C
Donut piechart
A D
2.
Histograms
Histograms represent a variable in the form of
bars, where the surface of each bar is
proportional to the frequency of the values
represented. They offer an overview of the
distribution of a population or sample with respect to
a given characteristic.
The two variations on the histogram are:
• Vertical columns
• Horizontal
columns

4. Scatter
plots
Scatter plots use the spread of points over a
Car- tesian coordinate plane to show the
relationship between two variables. They also
help us determine whether or not different groups
of data are correlated.
5. Heat
maps
Heat maps represent individual values from a
data set on a matrix using variations in color or
color
intensity. They often use color to help viewers com-
pare and distinguish between data in two different
categories at a glance. They are useful for visualizing
webpages, where the areas that users interact with
most are represented with “hot” colors, and the
pages that receive the fewest clicks are presented in
“cold” colors.
The two variations on the heat map are:
• Mosaic
diagram
• Color map
0.6
0.8
Scatter plot
3 4
5
Mosaic diagram
Scatter plot with
grid
30% 50%
Color
map
1.
0
0.8
0.6
0.4
0.2
0
0.2 0.4 1.
0
1.
2
30.000
25.000
20.000
15.00
0
10.00
0
5.000
0
10%
0% 70% 100%
1 2 6
E
D
C
B
A
1
0
-
1
2

6.
Linecharts
These are used to display changes or trends in
data over a period of time. They are especially
useful for showcasing relationships, acceleration,
deceleration, and volatility in a data set.
7.Bubble
charts
These graphics display three-dimensional data
and accentuate data in dispersion diagrams
and maps. Their purpose is to highlight nominal
comparisons and classification relationships. The
size and color of the bubbles represent a dimension
that, along with the data, is very useful for visually
stressing specific values. The two variations on the
bubble chart are:
• The bubble plot: used to show a variable in
three dimensions, position coordinates (x, y)
and size.
8.
Radarcharts
These are a form of representation built around a
regular polygon that is contained within a circle,
where the radii that guide the vertices are the
axes over which the values are represented.
They are equivalent to graphics with parallel
coordinates on polar coordinates. Typically, they are
used to represent the behavior of a metric over the
course of a set time cycle, such as the hours of the
day, months of the year, or days of the week.
Line chart
• Bubble map: used to visualize three-
dimensional values for geographic regions.
Radar
chart

9. Waterfall
charts
These help us understand the cumulative
effect of positive and negative values on
variables in a sequential fashion.
10. Tree
maps
Tree maps display hierarchical data (in a tree
structure) as a set of nested rectangles that
occupy surface areas proportional to the
value of the variable they represent. Each tree
branch is given a rectangle, which is later placed in a
mosaic with smaller rectangles that represent
secondary branches. The finished product is an
intuitive, dynamic visual of a plane divided into areas
that are proportional to hierarchical data, which has
been sorted by size and given a color key.
A
200
B
80
C
120
F
20
G
40
H
60
Start
Fall Rise
End
F G H I J K L
A E
B D
C
400K
350K
300K
250K
200K
150K
100K
50K
0
A
B C
E H
G F
D
D
30
E
50

1
1.Areachart
s
These represent the relationship of a series
over time, but unlike line charts, they can
represent volume. The three variations on the
area chart are:
• Standard area: used to display or compare a
pro- gression over time.
• Stacked area: used to visualize relationships as
part of the whole, thus demonstrating the
contribution of
each category to the cumulative total.
• 100% stacked area: used to communicate the
dis-
tribution of categories as part of a whole, where
the cumulative total does not matter.
Selecting the right graphic to effectively
communicate through our visualizations is no easy
task. Stephen Few (2009), a specialist in data
visualization, proposes taking a practical approach
to selecting and using an appropriate graphic:
• Choose a graphic that will capture the
viewer’s
attention for sure.
• Represent the information in a simple, clear,
and precise way (avoid unnecessary flourishes).
• Make it easy to compare data; highlight
trends and differences.
• Establish an order for the elements based on
the quantity that they represent; that is, detect
maxi- mums and minimums.
• Give the viewer a clear way to explore
the graphic and understand its goals;
make use of guide tags.
1 2 3 4 5 6
Standard
area
100% stacked
area
1.
0
0.8
0.6
0.4
0.2
0
1.
0
0.8
0.6
0.4
0.2
0
0 1 2 3 4 5 6
B
Stacked
area
A C
1.
0
0.8
0.6
0.4
0.2
0
0 1
2 3
4 5
6

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7
Basic principles
for
3.data
visualization

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Basic principles
for data
visualization
Shneiderman introduces his famous mantra on how
to approach the quest for visual information, which
he breaks down intothree tasks:
1.Overview first: This ensures viewers have a
general understanding of the data set, as their
starting point for exploration. This means offering
them a visual snapshot of the different kinds of data,
explaining their relationship in a single glance. This
strategy helps us visualize the data, at all its different
levels, at one time.
2. Zoom and filter: The second step involves supple-
menting the first so that viewers understand the
data’s underlying structure. The zoom in/zoom out
mechanism enables us to select interesting subsets of
data that meet certain criteria while maintaining the
sense of position and context.
3. Details on demand: This makes it possible to
select a narrower subset of data, enabling the user to
interact with the information and use filters by
hovering or click- ing on the data to pull up
additional information.
The chart on the right side summarizes the key
points to designing such a graphic, with an eye to
human visual perception, so that users can translate
an idea into a set of physical attributes.
These attributes are: structure, position, form
size, and color. When properly applied, these
attributes can present information effectively
andmemorably.
OVERVIE
W
FIRST
1.
System Context
Thesystem plus users
and
system
dependencies
2.
Containers
The overall shape of the archi-
tecture and
technologychoices.
3
.
Components
Logical components and their
interactions within a
container.
4.
Classes
Component or
patternimple- mentation
details.
ZOOM
AND
FILTER
DETAILS
ON
DEMAND
Graphics with
an
objective:seeking
your mantra
The goal of data visualizations is to help us
understand the object they represent. They are a
medium for com- municating stories and the results
of research, as well as a platform for analyzing and
exploring data. There- fore, having a sound
understanding of how to create data visualizations
will help us create meaningful and easy-to-remember
reports, infographics, and dash- boards. Creating
suitable visuals helps us solve problems and analyze
a study’s objects in greater detail.
The first step in representing information is
trying to understand that data visualization.
Ben Shneiderman gave us a useful starting point in
his text “The Visual Information-Seeking Mantra”
(1996), which remains a touchstone work in the
field. This author suggests a simple methodology for
novice users to delve into the world of data
visualization and experi- ment with basic visual
representation tasks.5
5 Shneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy
for Information Visualizations. Visual Information Seeking Mantra (p. 336).
Available at: https://www.cs.umd.edu/~ben/papers/Shneiderman1996eyes.pdf

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Furthermore, the visual hierarchy of elements plays a
role in this encoding process, because the elements’
organization and distribution must have a well-
defined hierarchical system in order to communicate
effectively (Meirelles: 2014). In a sense,
visualizations are paragraphs about data, and
they should be treated as such. Words, images,
and numbers are part of the information that will be
visualized. When all of the elements are integrated
in a single structure and visual hierarchy, the
infographic or report will organize space properly
and communicate effectively, according to your
user’s needs.
Layout anddesign:
communicative elements
In order to begin designing our reports and state-
ments, it is essential to understand that visual
representations are cognitive tools that
complement and strengthen our mental ability to
encode and decode
information6
. Meirelles (2014) notes that:
“Allgraphic representation affects our visual
perception, because the elements of
transmission utilized act
as external stimuli, which activate our
emotional
state and knowledge.”
Thus, when our mind visualizes a representation,
it transforms the information, merges it, and
applies a hierarchical structure to it to facilitate
interpretation.
For this reason, in order to have an efficient per-
ceptive impact, it is important to adhere to a
series of best practices when creating reports and
infographics. As with any other form of
communication, success depends largely on the
business’s familiarity
with the established code and the resources
available. Space, shapes, color, icons, and
typography are a few of the essential elements of a
striking visual with
communicative power.
6 Meirelles, I (2014). “La información en el diseño,” (p.21-22). Barcelona:
Parramón.
Structuring: the
importance
of layout
All visual representations begin with a blank
dimensional space that will eventually hold the
information which will be communicated. The process
of spatial coding is
a fundamental part of visual representation because
it is the medium in which the results of our
compositional
decisions and the meaning of our visual statement
will
be visualized, thereby having an impact on the user.
Edward Tufte (1990) defines “layout” as a scheme for
distributing visual elements in order to achieve
organization and harmony in the final composition.
Layout planning and design serve as a template for
applying hierarchy and control to information at
varying levels of detail.7
In his book Envisioning
Information, Tufte offers several guidelines for
information design:
• Have a properly chosen format.
• Give a broad visual tour and offer a focused
reading
at different detail levels.
• Use words, numbers, anddrawings.
• Reflect a balance, a proportion, a sense of
relevant
scale, and a context.
Spatial encoding requires processing spatial
proportions (position and size), which have a
determining role in the organization of perception and
memory.
7Tufte, E. (1990). Envisioning Information. Cheshire: Graphics
Press.

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Visual variables
and
theirsemantics
Visual variables are the building blocks of visual
representation. They conform to an order and
spatial context in order to convey a quantitative
message. These resources can be used to categorize
meaningful properties and amplify the message
being represented. Let’s take a look at their
semantics:
• Point: Has no dimensions and indicates a
place.
• Line: Has one dimension and indicates
length and direction.
• Plane: Has two dimensions and indicates
space and scale.
Jacques Bertin, cited in Meirelles (2014), used the
term “visual variables” for the first time in his book
Semiol- ogie Graphique, where he presented them as
a system of perceptive variables with corresponding
properties of meaning. He offered a guide for
combining graphic elements in an appropriate way
according to their order, position, orientation, size,
texture, and value.
Poin
t
Variable
s
2
dimensions
(X,Y
)
Siz
e
Valu
e
Lin
e
Are
a
Visual
variables
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Using consistent
andattractive color schemes
Color is one of the most powerful resources for data
visualization, and it is essential if we are going to
understand information properly.
Color can be used to categorize elements,
quantify or represent values, and communicate
cultural attributes associated with a specific
color.
It dominates our perception and, in order to analyze
it,
we must first understand its three dimensions.
Hue: this is what we normally imagine when we
picture colors. There is no order to colors; they can
only be dis- tinguished by their characteristics (blue,
red, yellow, etc.).
Brightness: the color’s luminosity. This is a relative
mea- sure that describes the amount of light reflected
by one object with respect to another. Brightness is
measured on a scale, and we can talk about brighter
and darker values of a single hue.
Saturation: this refers to the intensity of a given
color’s hue. It varies based on brightness. Darker
colors are less saturated, and the less saturated a
color is, the closer
it gets to gray. In other words, it gets closer to a
neutral (hueless) color. The following graphic offers a
brief sum-
mary of color
application.
Grayscal
e
Double
complementary
Complementary
Monochromatic
Split
complementary
Cool
colors
Saturated
colors
24

Isabel Meirelles (2014) notes that selecting a color
pal- ette in order to visualize data is no easy task,
and she recommends following Cynthia Brewer’s
advice uses three different kinds of color
schemes, based on the nature of the data:
1.Monochromatic sequential
palettesor their analogue
These palettes are great for ordering numeric data
that progresses from small to large. It is best to use
brighter color gradients for low values and darker
ones for higher values.
2. Diverging
palettes
These are more suitable for ordering categorical data,
and they are more effective when the categorical
division is in the middle of the sequence. The change
in brightness highlights a critical value in the data,
such as the mean or median, or a zero. Colors
become darker to represent differences in both
directions, based on this meaningful value in the
middle of the data.
Thus, brightness levels can be used as a visible,
coherent aspect of a graphic scheme. Sequential color
schemes make it possible to create a smooth, low-
contrast design. This color scheme is better for an
image than for data visualization.
TIP: Try to emphasize the most important
information using arrows and text, circles,
rectangles, or contrasting colors. This way, when
you visualize your data, your analysis will be
moreunderstandable.
3. Qualitative
palettes
These are better for representing ordinal or
categorical data to create primary visual differences
between categories. Most qualitative schemes are
based on differences in hue, with differences in
brightness between the colors.
TIP: To create a color hierarchy in a sequential
scheme, choose one dominant color and use the
others with moderation; alternatively, you can simply
use two softer versions of the dominant color, which
will naturally make them feel lower on the hierarchy.
22
TIP: The qualitative color scheme is perfect for
visualizing data because it affords a high degree of
contrast and helps you draw attention to important
points, especially if you use one predominant color
and use the second as an accent in your design.
Finally, don’t forget to use palettes that are
comprehen- sible to people who can’t see color.
Color blindness is a disability or limited ability that
makes it difficult to distinguish certain pairs of
colors, such as blue and yellow, or red and green.
One strategy for avoiding this problem is to adapt
designs that use more than just hue to codify
information; create schemes that slightly vary another
channel, such as brightness or saturation.

Use icons and symbols to
aid in understanding and
limit unnecessary tagging
Symbols and icons are another avenue for visualizing
information that goes beyond merely being
decorative. They draw strength from their ability to
exhibit a general context in an attractive, precise
way. Icons illustrate concepts. Viewers can
understand what the information is about by just
glancing at the illustration.
Alexander Skorka (2018), chief evangelist for
theDapresy Group, recommends using symbols and
icons because they simplify communication. Symbols
are self-explanatory, and our mind can process
icons more easily than text. It is important to
consider that an icon’s success depends largely on
cultural context, so it is important to select universally
understandable images.
Lifestyle products
That said, they certainly should not be complex illustra-
tions. An icon with too many details could hinder
viewers’ understanding. Keep it simple: icons’
meaning should be immediately clear, even when
they’re very small.
The ease with which we recognize icons enables us
to process data faster than we can process
information conveyed textually. Therefore, when
designing information, it is wise to use both
graphics and icons to convey proportions in
greaterdetail.
Singles
Notebooks
Couples
Entertainment
Families
Single
s
23
3
82%
55%
77
%
76%
64%
73%
Couples
63%
88%
54%
Familie
s
Notebooks
Entertainment
Lifestyle products
82% 76% 63%
55% 64% 88%
77% 73% 54%

2
4
netquest.co
m
The typography in our
reports: effective
applications
Typography plays an important role in the
design of reports and statements. Selecting the
right font
strengthens your message and captures the
audience’s
attention. Müller-Brockmann (1961), a graphic
designer,
defines typography as the proper visual element for
composition. He notes that “the reader must be able
to read the message from a text easily and
comfortably. This depends largely on the size of the
text, the length of the lines, and the spacing between
the lines”.8
Typography is an art form in and of itself, in
which every font has its own characteristics,
which should be strategically combined.
For people outside the world of graphic design,
choosing a font and setting other typographical
features can be tricky, but it doesn’t have to be. Let’s
take a practical look at the steps you should take
when determining your typography, and then
consider the images and visual elements that best
accompany your text. Consid- erations when setting
your typography:
• Determining the goal of your report’s content.
• Select a font that strengthens that goal.
Fonts come in two types: with serifs or without
(sans)
serifs. Serif fonts have an extra stroke that
conveys a
8 The Graphic Artist and his Design Problems (Gestaltungsprobleme
des Grafikers), Teufen, 1961
sense of tradition, security, history, integrity,
author- ity, integrity, and other such concepts.
Sans-serif fonts stand out because they have a
more polished, sophisticated feel; they convey a
sense of modernity, order, cleanliness, elegance,
avant-garde, and style.
• Pay attention to legibility. Remember that
screen type does not appear in the same way as
print type.
It is best to choose a more responsive (sans-serif)
font
for on-screen texts, and fonts with serifs for
printed reports. That said, there’s an exception to
every rule,
and today there is a bounty of fonts that are
perfectly
suitable for both digital and print media.
• Watch your weight (light, regular, bold).
When it comes to bolding your text, a value of
two or three
should be plenty. It is better to reserve the
heaviest
weight for headlines and then apply a stylistic
hierarchy based on your content. Avoid fonts
that only offer
one weight or style, since their applications are
limited.
• Don’t forget that some fonts use more
memory than others. Fonts with serifs generally
monopolize more of your computer’s brain power
than sans-serif fonts. This is an important
consideration in interactive reports, since a
document that occupies more RAM will be
lessresponsive.
Fonts have personalities that help us establish a
more attractive visual tone for our audience.
Familiarizing yourself with a few can go a long
way. There are:
• Professional
fonts
• Fun font
• Handwritten
fonts
• Minimalist fonts

Prioritize patterns inyourvisualizations: Gestalt
The basic elements of the visualization process also involve preattentive attributes. Preattentive attributes are
visual features that facilitate the rapid visual perception of a graphic in a space. Designers use these
characteristics to better uncover relevant information in visuals, because these characteristics attract the eye.
Colin Ware, Director of the Data Visualization Research Lab at the University of New Hampshire, has
highlighted that preattentive attributes can be used as resources for drawing viewers’ immediate
attention to certain parts of visual representations (2004). According to Ware, preattentive processing
happens very quickly—typically in the first 10 milliseconds. This process is the mind’s attempt to rapidly
extract basic visual characteristics from the graphic (stage 1). These characteristics are then consciously
processed, along with the perception of the object, so that the mind can extract patterns (stage 2), ultimately
enabling the information to move to the highest level of perception (stage 3). This makes it possible to find
answers to the initial visual question, utilizing the information saved in our minds. Colin Ware, cited in
Meirelles (2014), explains it as follows:
Preattentive attributes enhance object perception and cognition processes, leveraging our mind’s visual
capacities. Good data visualizations deliberately make use of these attributes because they boost the mind’s
discovery and rec- ognition of patterns such as lines, planes, colors, movements, and spatial positioning.9
9 Dondis, D.A. (2015). La sintaxis de la imagen: introducción al alfabeto visual. Editorial Gustavo Gili:
Barcelona Meirelles, I. (2014). La información en el diseño. Barcelona: Parramón.
28
Bottom up information contributes to the pattern creation process
Top down process reinforces relevant
information

The visual below lists preattentive attributes that
represent aspects of lines and planes when visualizing
and analyzing graphic representation: shape, color, and
spatial position.
Shape
Orientation
Orientation
Added
marcks
Shape
Shape Added Marks
Line
Width
Thickness
Color
Sharpness
Line
Length
Line
Length
Enclosur
e
Size
Size
Curvatur
e
Intensity/
value
Numerosity
Hue
Curvatur
e
Enclosur
e
Colo
r
Intensity
Spatial
Position
2-D Position
29

Detecting patterns is fundamental to structuring and
organizing visual information. When we create
visuals, we often want to highlight certain patterns
over others. Preattentive attributes are the alphabet
of visual language; analytic patterns are the words
that we write
by using them. When we see a good visualization,
we immediately detect the preattentive attributes
and rec-
ognize analytic patterns in the visualization. The
follow-
ing table summarizes a few basic analytic patterns:
Analytic patterns
30

According to Dondis (2015), Gestalt’s principles
help describe the way we organize and merge
elements in our minds. They quiet the noise of the
graphics so that we relate, combine, and analyze
them. These principles come into play whenever
we analyze any sort of visualization. Only position
and length can be used to accurately perceive
quantitative data. The other attributes are useful
for perceiving other sorts of data, such as
categorical and relational data.
We’ll close this section with one piece of practical
advice on how to effectively visualize data. Colin
Ware in The Visual Thinking: for Graphic Design
(2008) summarizes the importance of always
being mindful of preattentive attributes and
patterns when designing a visualization:
Gestalt’s principles
28
8
Good design optimizes the visual thinking
process. The choice of patterns and symbols is
important so that visual queries can be
efficiently processed by the intended viewer.
This means choosing words and patterns each
to their best advantage.”
We have seen how preattentive attributes and
patterns make it possible to process and analyze
visual information; they also enable us to improve
pattern discovery and perceptive inferences and
provide processes for solving visualization problems.
Gestalt’s principles are the principles that enable us
to understand the requirements posed by certain
problems so that we see everything as an integral,
coherent whole. It involves proximity, similarity,
shared destiny, “pragnanz” or pithiness, closure,
simplicity, familiarity, and discernment between
figure and ground.

The Process of Visualization
The process of understanding data begins with a set of numbers and a
question. The following steps form a path to the answer:
❑ Acquire
❑Parse
❑Filter
❑Mine
❑Represent
❑Refine
❑Interact

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