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Data Visualization Techniques:
Traditional Data to Big Data
Parul Gandhi and Jyoti Pruthi
Abstract Data visualization is one of the interactive ways that lead to new innovation
and discovery. It is a dynamic tool that opens new ways of research which facilitate
the scientific process. With extensive use of the Internet and Web, a large amount of
data is generated every day. There is a need to understand large and complex data.
When the data is available in large volume, it has to be processed by using various
data processing methods and need to present it with different types of techniques
and methods. Data visualization is a key to the success of any enterprise as it helps
enterprises to control the data in an effective manner and make the best utilization of
that data to convert it into knowledge. It is a process of converting data and numbers
into visual form. Data visualization techniques use different effects of computer
graphics. It helps the stake holders to make an effective and fast decision making. It
also provides the better understanding for pattern recognition, analysis of trends, and
to extract the appropriate information from the visuals. Visualizing data may be a
challenge but it is much easier to understand data in the visual form rather than in the
form of text, numbers, and large tables with lots of row and columns. One can choose
the data visualization technique wisely by understanding data and its composition.
Keywords Data visualization Agile methodology Line chart Pie chart Bar
chart Bubble chart Symbol maps Portfolio wall Kanban board Epic and
story
1
Introduction
The world is throng with growing data on daily basis and there is a need to handle
and display data in an understandable form. Visualization is a technique that is
P. Gandhi (B)
Department of Computer Application, Manav Rachna International
Institute of Research and Studies, Faridabad, India
J. Pruthi (B)
Department of Computer Science and Technology, Manav Rachna University,
Faridabad, India
e-mail: jyoti@mru.edu.in
© Springer Nature Singapore Pte Ltd. 2020 53
S. M. Anouncia et al. (eds.), Data Visualization,
https://doi.org/10.1007/978-981-15-2282-6_4
Data Visualization Techniques: Traditional Data to Big Data
5
54
Fig. 1 Benefits of data visualization
used to analyze the data in variety of ways to make effective decision making. Data
visualization is an effective process to present data and information graphically or
pictorially. It is emerging like a powerful and widely applicable and acceptable tool
for interpreting and analyzing large and complex data. It is becoming easy and quick
way of conveying data along with concepts in a universal format (Fig. 1).
Data visualization is concerned with development, design, and application of
graphical representation of data and makes it easy to understand the sense of data. It
is also known as scientific visualization or information visualization.
Using pictures, graphs, charts, and maps, to understand the data and information
have been used for centuries. Due to the advancement in computers, now it is pos-
sible to handle and process huge amount of data at a very high speed. Today data
visualization is becoming a blend of art and science that is going to bring a visible
change over the few coming years.
Visualizing data may be a challenge but it is much easier to understand data in
visual form rather than in form of text, numbers, and large tables with lots of row and
columns. One can choose the data visualization technique wisely by understanding
data and its composition. All visualizations techniques are trying to solve the same
problem but in a different way.
Broadly, there are two categories of data visualization with different purpose:
explanation and exploration. Exploration data visualization is useful when data is
available in quantity but knowledge about data is very little and goals are vague.
Explanatory data visualization when again data is available in quantity but we know
what exactly the data is. Both the categories help in the presentation of data visually.
This chapter provides an overview of data visualization, why it is important,
factors involved in data visualization, different visualization techniques for big data,
Discovery
Analysis of
data
Data Insight
Decision
Making
Understanding
Data Visualization Techniques: Traditional Data to Big Data
5
55
comparison between various techniques, related tools and software, Visualization
for Agile software development, and selecting appropriate visualization technique.
2
Importance of Visualization
With extensive use of Internet and Web, a large amount of data is generated every
day. There is a need to understand large and complex data. Each organization which
keeps record basically deals with data and has to take decision. When the data is
available in large volume, it has to be processed by using various data processing
methods and need to present it with different types of techniques and methods.
It is a key to success of any enterprise as it helps enterprises to control the data
in an effective manner and make the best utilization of that data to convert it into
knowledge. It is a process of converting data and numbers into visual form. Data
visualization techniques use different effects of computer graphics. Data visualization
enhances learning, understanding, and reasoning and helps the stake holders to make
effective and fast decision making. It also provides the better understanding for
pattern recognition, analysis of trends, and to extract the appropriate information
from the visuals.
Data visualization actually helps in communicating complex data with accuracy,
clarity, and efficiency. It actually absorbs the data in a new and more constructive
ways that help the organizations to take appropriate and useful decisions. It visualizes
the relations and patterns among operational activities. In another words, we can say
that data visualization is a new business language.
With the help of visual trends, one can easily understand what can be the best
next step within very less time frame. It makes the data less confusing and more
sharable and accessible. It would be easy to memorize and remember the data if it
is in graphical format. Moreover, a very little modification can give the new look to
the information and helps to formulate different strategies for different situations.
3
Factors Affecting Data Visualization
The successful visualizations have a few properties in common. They all included a
specific and clear objective; they have only relevant information, focused data, and
present the data in a way that projects the patterns and relations in the data.
One needs to be careful before adopting a specific visualization technique. To
achieve the objective, there must be some points that need to be keeping in mind
every time before going to visualize the data. Firstly, an objective of data visualiza-
tion should be defined. Second, focused data should be selected. Third, a suitable
technique must be identified. And after that, other options of colors, fonts, and other
visuals can be chosen.
Data Visualization Techniques: Traditional Data to Big Data
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56
Fig. 2 Properties of data
If the purpose is clear and specific, it would be better to articulate the data in
effective way. Before going for visualization, one should be clear about the stake
holders of the end product. Always create the visuals that are valuable to the user
(Fig. 2).
A good practice is to start with a question “What value the data visualization is
going to contribute in decision making?” Data visualization with good quality comes
in different sizes and shapes but all have some specific features that ensure to give
something with proper insights of the data. Generally, a good visualization output
must be meaningful, user uses it on regular basis and will be able to take effective
decisions by comprehensive scenario; Desirable, it must be pleasant to use; Usable,
user can use it to meet their objective very quickly and easily.
The visualization must be visually excellent appealing and the quality of the
output should be good. The visualization should be scalable. As the data size would
increase, the visualization application has to perform the same way. Therefore, the
system should ensure the scalability for the future modifications.
4
Traditional Data Visualization Techniques
There are various tools and techniques which are used to convert the data in its visual
form which cannot be directly converted by human being. Microsoft Word, Microsoft
Excel, Microsoft Spreadsheet, and PowerPoint are some popular multipurpose tools
with database connectivity as well to serve the purpose of data visualization and
yields great results. These softwares are effectively used by the organizations which
do not require highly specialized visualization of data.
Some of the traditional data visualization techniques to represent data are pie
chart, line chart, bar chart, area chart, graphs, map, heat map, etc.
Desirable
Usable
Effective
Mesareable
Scalable
Data Visualization Techniques: Traditional Data to Big Data
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57
4500
4000
3500
3000
2500
2000
1500
1000
500
0
TVRF CLC CVRF TLC LC
Tos
JAN
FEB
MAR
Service Service
Fig. 3 Line chart for AC service in three months
4.1
Line Charts
It is one of the basic techniques to make the data more appealing and visualized.
It shows the relationship between two patterns. It is also very effective to compare
several values at the same time interval. It is the most effective approach when change
in a variable or variables needs to be displayed (Fig. 3).
4.2
Pie Charts
It is also named as circle graph. The data is represented in the form of pie slice. The
big slice shows the big amount of data. It is basically used to show the components
percentage of the whole. Two popular variations of pie charts consist donut chart and
exploding pie chart (Fig. 4).
Fig. 4 Pie chart for AC
service JAN
TVRF
CLC
CVRF
TLC
LC Service
Tos Service
Data Visualization Techniques: Traditional Data to Big Data
5
58
Tos Service
LC Service
TLC
CVRF
CLC
TVRF
0
2000 4000 6000 8000 10000
JAN
FEB
MAR
Fig. 5 Bar chart for AC service
4.3
Bar Charts
It is also referred to as column chart which makes the use of both horizontal and
vertical bars. It is used to compare items of different groups. It is not very effective
when the amount of data is very huge. It is mainly used by industries to compare
their sales, cost, etc. (Fig. 5).
4.4
Area Chart
Area chart is the best choice when there is a need to show the trend over time. Line
chart and area chart are similar in nature as in both the charts, the data points are
plotted and connected through a line except that in area chart, the whole area between
axis and line is filled in with color or shading (Fig. 6).
JAN
R FEB
MAR
Fig. 6 Area chart for AC service
6000
4000
2000
FEB
JAN
MA
Data Visualization Techniques: Traditional Data to Big Data
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59
Fig. 7 Bubble chart for AC
service
5000
4000
3000
2000
1000
0
-1000
TVRF
CVRF
LC Service
4.5
Bubble Chart
It is one of the variations of scattered plots in which the makers are replaced by the
bubbles. It needs at least three measures, two for the plot axes and third for the size of
bubbles to show the relationship. It is a good choice for the large set of data (Fig. 7).
4.6
Scattered Plot
It is a two-dimensional chart which is used to display the variation between two data
items. A scatter plot is also called a scatter chart, scatter diagram, and scatter graph.
It helps mainly to know how closely the data is related to each other by showing how
the data points are scattered or spread over a graph area (Fig. 8).
Fig. 8 Scattered plot for AC
service 1200
1000
800
600
400
200
0
0 2 4 6 8
JAN
FEB
MAR
Data Visualization Techniques: Traditional Data to Big Data
5
60
Fig. 9 Tree map for education system
4.7
Tree Maps
This approach is used to show the data in hierarchical form. The data is repre-
sented
in the layered rectangular form to show the structure of hierarchies at different
depth.
The objects can be divided into various divisions and subdivision as per the
requirement (Fig. 9).
4.8
Heap Maps
It is used to represent and compare the data using different colors. As an example,
it can be used to show the best cases in green color, average cases in yellow color,
and worst cases in red color which helps the end user to compare the performance
of things in one go (Fig. 10).
These data visualization techniques suit both traditional as well as big data to some
extent. These tools also save time to great extent as very less human intervention was
Computer
Science
Electronics
Engineering
Mechanical
Civil
Electrical
Ph.D Program
Management
Applied Science
Non Engineering
Humanities
Journalism
Commerece and
Business Studies
Data Visualization Techniques: Traditional Data to Big Data
6
1
Fig. 10 Heap maps for AC
service
there for visualization process and helps the researchers as well to conduct their
research with the help of effective visual form of the data.
But when talking about advanced level visualization of huge amount of data, we
require some specialized visualization tools, which give an entirely new insight to
data and also have the capability to apply all permutation and combination to the vari-
ety of data and eventually results in the relevant and significant visual representation
as compare to the traditional one.
5
Visualizing Big Data—Tools and Techniques
Visualization is not just a convenient but it is the one of the prominent features for
big data. It is a big challenge to handle variety of big data as each data has different
speed, size, and diversity that should be taken into account in order to visualize.
There are three vs that affect the operation of data and must be taken into account
while doing data processing and visualization. These three vs are volume, velocity,
and variety of data.
Volume refers to the size of the data that is accessible to any organization and can
be in terabytes, petabytes, etc. Variety refers to the representation of data in audio,
video, text, and images forms and it also refers to structured and unstructured data.
Velocity means the frequency of changing the data. It also refers to the factoring and
aggregating the data.
The modern data visualization techniques to represent and handle big data are
word clouds, symbol maps, connectivity chart, etc. These techniques are specially
designed to handle semistructured and unstructured data.
5.1
Word Clouds
Word cloud is a technique for visual representation of text data. It is useful for
analyzing sentiment analysis of the post done by people in social media. It highlights
the most frequently used keywords on a Web page. The importance of each word is
indicated by using different font size or color. This technique helps in finding the
JAN
FEB
MAR
TVRF
578
635
858
CLC
660.42
897
1036.18
CVRF
212
201
302
TLC
370
527
1016
LC Service
260
444
650
Tos Service
123
124
221
2204
2828
4083
Data Visualization Techniques: Traditional Data to Big Data
6
2
Fig. 11 Word cloud for frequent terms
most prominent words in a quick manner. Word clouds are the most widely used
technique due to its readability, understandability, and simplicity. It can be easily
shared and are very impactful. Word clouds are used by almost all fields of life. It
can be used by researchers, marketers, educators, politicians, journalists, and social
media sites.
Tool
Many freeware softwares are available which helps to process text given by user and
results in the significant word clouds. Tagul is one of the tools that can be used to
create word cloud based on the text already available and new text imported by the
client or user. The resultant data of Tagul tool is highly customizable and animated
with the features to specify different color, shape, and size (Fig. 11).
5.2
Symbol Maps
It is same as word cloud except that we represent symbols in place of words. The
symbols will be of different sizes which make them easy to compare. To create a
symbol map, there is requirement of quantitative value or location names. The large
variation in the data is advisable to see the difference in symbols, otherwise all the
symbols will appear of same size and will be difficult for business user to distinguish
them and find the optimized results of visualization.
Tool
Many freeware softwares are also available which helps to process text given by user
and results in the significant symbol maps.
Tableau v4.0 is the most effective tool for creating symbol map. Before this, people
have daunting experience to create symbol maps. Tableau v4.0 makes the symbol
Data Visualization Techniques: Traditional Data to Big Data
6
3
Fig. 12 Symbol maps
maps more effective for visualization purpose and also helps the business users to
categories data for decision making (Fig. 12).
5.3
Connectivity Charts
This visualization technique is used to show the connection between action and their
triggers. It also shows the strength of connection between them (Fig. 13).
Effective visualization of huge data is not possible without analytics. In order to
achieve the optimization, the data should be preprocessed to lessen the complexity
Fig. 13 Connectivity charts
15000
5000
10000
Data Visualization Techniques: Traditional Data to Big Data
6
4
of big data in terms of time and space. Analytics facilitate the big data visualiza-
tion in a great manner. The tight integration of visualization and analytics plays an
important role to achieve the effective output for various big data applications. To
serve the purpose, various big data visualization tools are also available that run on
the Hadoop platform. Hadoop Common, Hadoop Distributed File System (HDFS),
Hadoop YARN, and Hadoop MapReduce are some of the tools that help to effi-
ciently analyze the big data. Pentaho, Flare, Jasper Reports, Dygraphs, Datameer
Analytics Solution and Cloudera, ManyEyes, Platfora, and Tableau are some of the
softwares developed for data visualization which can handle ZB (zettabytes) and
PB (petabytes) data quite naturally but the major problem with these tools is the
inadequate visualization.
6
Visualization in Agile Software Development
In Agile, analysts help to define a specification and it is all about division of work
among teams. More interaction would be there between team members and teams.
Data visualization allows organization to see the progress of teams to achieve the
objective. The traditional way to do the same is “Waterfall Life Cycle” (Fig. 14).
Waterfall life cycle process is bit lengthy and time consuming too. There is a lot
of time gap between the requirement gathering and completion of project. The role
of the consumer is only at the beginning and at the end. There would not be any
communication in between the first and the last stage. There may be a chance that
requirement could be change because of long time gap.
Moreover, there would be a very less interaction among the team members of
different stages. Therefore, there could be a chance of different understanding of
Fig. 14 Working of waterfall model
Problem
Requirement
Less Interaction
with Consumer
Solution Design
Develop
Collaboration
Testing
Implementation
Data Visualization Techniques: Traditional Data to Big Data
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5
different teams for a same problem statement. Developer would not be in touch with
consumer. So there is always a gap between what is expected and what is delivered.
To overcome all the above-mentioned issues, an iterative model can be introduced
where in more team collaboration and more consumer interaction would take place.
Agile software development is one of the solutions for the same. It allows the quick
delivery, quick feedback, quick review, and changes can be done which result in
desirable output and ultimately meeting the expectations of the consumer. It also has
the ability to fetch data from different sources and display data by using visualization
technique (Fig. 15).
In Agile, there is daily stand-up meeting to fix the target for the day and different
iteration would be there as per the communication and feedback of the consumer on
the deliverables. Sprint planning and release would be done on regularly basis as per
the requirements and lastly, strategy would be formed to make output a better
version (Fig. 16).
In Agile, to show the progress and the status of work done, on daily basis is
presented through some visualization techniques. These techniques are very different,
powerful, and useful to convey the progress. Some of the techniques are
(a)
The portfolio wall
(b)
The kanban Board
Fig. 15 Agile methodology
Strategy
Release
Iteration
Daily
Data Visualization Techniques: Traditional Data to Big Data
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6
Fig. 16 Collaborative way in Agile
(c)
Burndown chart
(d)
Epic and story Mapping.
6.1
The Portfolio Wall
The portfolio wall is the very powerful tool to visualize the work done by the team
to achieve the pre-defined targets. It tracks and treats each cycle as “Iteration.” All
the completed tasks remain in the current iteration and the pending one moves to the
next iteration. This technique allows team to work toward the combined release and
to visualize the progress. In other words, it will create a visual that projects the status
of milestone achieved till now.
The portfolio wall uses the color codes to represent the different teams and their
integration. Each team has its own color scheme and shows their milestone that they
have to meet for the successful release. It increases the transparency across teams
and removes the deadlocks (Fig. 17).
A portfolio wall takes a matrix-like structure with time on horizontal axis and
goals on vertical axis. Card on each column actually represents the work to be done
by a particular team in a definite time. Lines, colors, cards, and other attributes are
used to represent team and their dependencies.
Nominated team members from each team generally meet every day in front of
the wall in order to monitor and discuss the progress of respective teams.
Product
Master
RELEASE
RELEASE
Data Visualization Techniques: Traditional Data to Big Data
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7
Fig. 17 Product backlog-task and associated user stories
When a team creates a plan in collaboration and it will be displayed on the wall,
their engagement increases, and improves the output. There are a number of benefits
of using the portfolio wall:
1.
By making an active participation in maintaining and creating the plan for work
to achieve common objective, the team sees some control over the work.
2.
A portfolio wall allows the team to visualize the big and clear picture of their
work progress.
3.
The target seems more achievable when items move from one section to another.
4.
The wall is visible to everyone that increases the level of commitment of team
members.
5.
The portfolio wall actually encourages the team members to work harder to
achieve the goals and meet the objective.
Some of the major disadvantages of the portfolio wall are:
1.
The historical data cannot be projected on the wall.
2.
There is a lack of visibility in projecting unallocated work but actually needs to
be done.
6.2
The Kanban Board
Kanban is a method or a technique that is used in Agile to manage the creation of
products with an emphasis on continuous delivery but at the same time, not over-
burdening the team. It is a process to help teams to work effectively in collaborative
manner. In this process, each member is able to see the progress and always be
Data Visualization Techniques: Traditional Data to Big Data
6
8
informed what needs to be done, what is in progress, and what is already completed
or done.
Kanban always encourages ongoing and active learning, promotes collaboration,
and improves the efficiency of the teams by defining the best workflow. However, it
requires highly motivated and self-managed teams.
It is a real-time framework that promotes real-time communication and trans-
parency of work among teams. Tasks are represented visually on the board and team
members can see the status of the every task at any time. Kanban is very prominent
among the Agile software teams. The work of all teams actually revolves around the
kanban board that visualizes and optimizes the flow of work.
The kanban major function is to ensure the visualization of the work done by the
team. All hurdles and deadlocks are identified and resolved. The kanban methodology
ensures the real-time communication and full transparency of work, therefore it can
be seen as the source to visualize the progress of teams.
Kanban offers the task planning and project the throughput of all teams of different
sizes. It is the most popular software development methodologies nowadays. Kanban
helps to visualize who is responsible for what. It increases the focus of the team to
complete the work and achieve the objective (Fig. 18).
The Kanban board is basically divided into three sections
1.
To-Do
2.
In-Progress
3.
Done.
The task that needs to be performed at starting comes under the To-Do column.
The task on which team is currently working on comes under the column In-Progress.
The task completed by the team to achieve the target comes under the Done category.
Therefore, with the help of visualization through Kanban technique, all teams come
to know about their regular performance and the task done by the team within a
limited time frame. The entire team gets focus on completing the tasks that are in
Fig. 18 Team-wise progress record and checkpoints working of waterfall model
Data Visualization Techniques: Traditional Data to Big Data
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9
progress. The main idea behind the Kanban wall is to promote “
Stop Starting, Start
Finishing.”
The major benefits of the Kanban are:
1.
It helps to shorter cycle time and faster the delivery of the features.
2.
The team can visualize the performance on daily basis and they are ready to adapt
the new strategy to achieve the target and get the delivery done within the time
limits.
3.
Kanban is ideal where priorities are changing very frequently.
4.
The feedback is projected on the wall that improves the chances of more
motivation in a team and more empowered performing team members.
5.
Kanban provides the better transparency for each task that needs to be performed.
Some of the drawbacks of the kanban are:
1.
The outdated boards can mislead the team at development stage. The wrong or
duplicate issues will float to the development process.
2.
There may be a chance that teams make the overcomplicated kanban boards that
result in wastage of time in understanding the board instead of finishing the work.
3.
Sometimes, there is no time associated with each phase that leads the team in
wrong direction.
Most of the kanban disadvantages are due to mishandling of kanban board.
Therefore, proper understanding of how to use kanban board is very necessary.
6.3
The Burndown Chart
The Burndown chart is a graph that represents the progress of team over time while
doing the project. It tracks daily progress of the team and is considered as an essential
tool to track the progress. When the task is completed, the graph burns down to Zero
(Fig. 19).
The burndown charts represent the relation between the amount of work (x-axis)
and the time (y-axis). Time is shown with days when the work started but in case of
Agile, it may be represented in terms of sprit also. The vertical axis represents the
wok left to complete the task is the sum of estimation.
The burn down chart has two lines:
•
Estimated task (Defined)
•
Actual task (Deviation).
Estimated task (Defined) is a straight line, starting from the start point up to the
end finishing point. It is the actual path that needs to be followed by the team to
achieve the set targets. This line represents the tasks need to be completed in one
day. There may be a chance that the team takes longer or short time span to complete
the defined tasks. Actual Task (Deviation) is the actual work at given point of time.
This is the line that shows the actual progress of the team or in other words, it shows
Data Visualization Techniques: Traditional Data to Big Data
6
10
60
50
40
30 Defined
Deviation
20
10
0
Fig. 19
0 2 4 6 8 10 12 14 16 18
Time (in Days)
Burndown chart—progress tracker
Finish
the deviation from the actual estimated. The team can understand the deviation from
the defined path on daily basis.
The reading of burndown chart is very important as it shows the actual perfor-
mance. When the actual task or deviation line is below the defined line, it means that
the team is ahead of schedule and completed the task before the estimated time. In
contrast, when the actual task line is above the defined line, it shows that team is
behind the time schedule and is not able to complete the task in defined period of
time.
The balance between both the lines is very important. It also helps in identifying
the potential of the team and helps the team to so estimation in appropriate manner
so that the objective can be meet as per time frame.
Big visible charts play very important role and are considered as a powerful tool to
visualize the performance of the teams. It makes the process and progress transparent
to
everyone. It also helps the team to identify their strength and weakness through
retrospective and, therefore, the team can work on the items that need to be improved.
Some of the major benefits and drawbacks of burndown charts:
1.
Burndown chart forces team to evaluate their performance on daily basis and
make the strategy as per retrospect.
2.
It helps to maintain the accuracy of sprint and ensure the timely delivery of work.
3.
Sometimes, it is difficult to maintain the burndown chart and one wrong projection
on the chart may lead to break down the efficiency of the team.
Tasks (In Days)
Data Visualization Techniques: Traditional Data to Big Data
71
6.4
Epic and Story Mapping
Story mapping is a technique introduced by Jeff Patton. It involves building a grid
of user stories that is actually representing the experience of the users. User story
arranges user stories according to how people think and do work on daily basis. It
represents the actual thinking and real experiences of the users and tries to make
strategy to proceed for work and ultimately draws conclusion what is to be done and
how it is to be done.
The main aim to create a story map is to ensure that all teams and their members
are on the same page from the start of the development and commit to achieve the
same objective. The story map is considered as a collaborative practice that guides
the team to create the product backlog and visualize the target.
The first step to create the story map is to decide the flow of activities by the user.
This should be considered as the core flow of the user activities. The team should
create the set of activities and need to arrange the same in chronological order.
After creating the pool of activities and arranging them in order, the second step
is to identify the task with each activity and group them with respective one. The
task has to be identified keeping in mind the time needed to complete the activity.
The task should be associated with activity.
There may be possibility the number of tasks may vary from activity to activity.
It is not necessary that each activity should have equal number of tasks under it (Fig.
20).
The next step is to map the user stories with corresponding activities and task.
The mapping of user story needs to be done very carefully as per the requirement
and the previous experiences of the user.
The format to write a story is:
As a user, I want (define goal), so that I can achieve (mention objective or reason)
(Fig. 21).
Fig. 20 Tasks associated with each activity
Activity 1
Activity 2
Activity 3
Activity 4
Activity 5
Activity n
Data Visualization Techniques: Traditional Data to Big Data
72
Tasks
User Stories
Fig. 21 Story framework
The story mapping is considered as the backbone that map and convert the user
stories into backlog and create the visual to achieve the objective. In this way, a
product can be developed that value the user and meet the expectations of the user
in mass.
Story maps can be seen as the great information radiators. But it requires great
space to capture the entire stories.
Looking at all the major visualization techniques under Agile software devel-
opment, the team has to understand what to choose and when to choose. As these
visuals can do a paradigm shift and are able to introduce more efficient and productive
processes to achieve the objective within a prescribed time frame.
All methodologies have some pros and cons. So select it very carefully and as per
the requirement of the organization. The combination of two or three visualization
techniques can also be used and it may prove to be more effective technique.
7
Challenges of Big Data Visualization
Two major challenges of big data visualization are scalability and dynamics. Speed
is a prominent factor of big data visualization. Designing the visualization tool is
a very tedious task for efficient visualization. Parallelization is another challenge
of visualization due to the size of huge amount of data. While doing the big data
visualization, the following problems can be faced:
•
Loss of information
•
Noise recognition
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73
•
Image sensitivity
•
High performance requirement
•
High rate of image change.
8
Choosing Appropriate Visualization Method
Obtaining optimized visualization of data totally depends on how effectively you
find the best underlying visualization method that suits the requirement as well as
displays the data appropriately. Because at different times different visualization
techniques must be used to accomplish variety of tasks. This is a big challenge these
days as most professionals still do not know which is the best technique to use to
achieve a goal or accomplish a task and eventually end up getting wrong results for
correct data. Therefore, it is quite interesting and challenging as well to find out the
best one for your code and end up with the optimized results.
Both the above-explained visualization techniques for big data and traditional
data can be used for big data. The only thing matters that it must result in easily
understandable output that helps the business users to take wise decision. SAS visual
analytics is one of the important approaches that enable us to explore the data using
various visualization techniques. Exploration data visualization is useful when data
is available in quantity but knowledge about data is very little and goals are vague.
Box plots and correlation matrices are such techniques that help us quickly under-
stand the data and its composition irrespective of its size. SAS visual analytics works
in two phases: At the front end, it helps large number of users to view and interact
with the report to take further decision, while at the back end, it concern is also
available which ensures the security of the underlying data and also controls other
aspects as well directly or indirectly related to the security of data.
This results in the fast-track processing of data, makes it available to the hands
of decision makers, and makes them more productive and collaborative with the
optimized results.
9
Conclusion
To understand the information in visual form is much more easy as compared to the
information in the traditional manner like in the form of table, text, etc. Visualization
is an area which makes it easy to interpret the data and go for corrective decision and
its composition. Visualization techniques are available to handle both traditional and
big
data. Various visualization choices are available but some of the techniques may end
up with the wrong visualization presentation. Thus, it is important to choose the
appropriate visualization method to better understand the data for further business
Data Visualization Techniques: Traditional Data to Big Data
74
analysis and many more. In this chapter, we tried to show all the alternatives available
that conveys the data more
clearly as well as truly understand the data.
10
Summary
Data visualization is one of the interactive ways that leads to the new innovation and discovery. It is a dynamic
tool that opens new ways of research which facilitate the scientific process. The main objective of this chapter
was to explain:
•
Factors affecting the data visualization
•
Visualization techniques for traditional data
•
Visualization techniques for big data
•
Tools and software available for visualization
•
Visualization for Agile software development
Choosing appropriate visualization t