Visualization quickly 7) Faster and more accurate

Visualization

Is the process of creating animations, diagrams or images to
deliver and transmit a message in a way that is more logic and understandable.
It is an effective way to communicate complex ideas. In the past people used
for example mural paintings, But today visualization is based on computer graphics.

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Main Criteria of Data
Visualization       

1)    
It communicates data: visualization process the information that
comes in many forms that are not immediately visible into something that is
more visible and understandable and ready to be used and processed.

 

2)    
It produces an image: the data being visualized should communicate
an image about the specific situation that needs to be studied.

 

 

3)    
The result is evident and clear: it is the most important criteria.
Visualization should allow the audience to detect important trends and patterns
of the data set and they should be able to learn something about the data being
visualized.

 

Why using data visualization?

Using
visualization, we can convert complex, high volume data and reports into more
understandable graphs and charts that are more adapted to the way that human
brain analyze and interprets information, allowing managers to better
understand how the business is doing and identify important patterns that help
in generating business plans and strategies leading to successful and more
accurate decisions.

1)     Rapid assimilation of information

2)     Improve business planning process and steps

3)     Capture audience attention and interest

4)     Increase data accessibility

5)     Focus on important concepts

6)     Detect problems quickly

7)     Faster and more accurate decisions

 

 

 

 

 

Four Ways that Data Visualization Can Help your
Organization

 

Visualizing
huge amount of data in a comprehensive way helps managers to empower employees
and let them feel more engaged in decision making process by knowing what is
really going on, answers questions and gives them more direction towards their
goal.

1. Data integration

Data visualization helps to show the
summary of a huge amount of data in one place which makes it as a reference
when making a decision, this helps managers to:

A.    Detect on
going trends in an organization.

B.    Demonstrate
relationships between variables or operations.

C.    Get one view
of multiple dimensions data set.

 

2. Establish a clear correlation
between business operations and activities

It helps managers to compare actual
performance with desired one, this makes better monitoring to business
operations by detecting pattern of performance; for example, comparing between
daily sales performance with the desired performance, which help managers to
make action plan accordingly to cover up gaps needed to achieve desired
performance.

 

3. Improving response rate

Visualizing data helps managers to forecast
upcoming trends in the market in order to cope with changes and plan
accordingly to keep stability and stay competitive in the market, this enables
managers to plan accordingly and built up strategies to follow in the near
future. This

 

4. Presenting more adapted data

Data visualization makes complex data
easier to read and interpret from different levels in the organization, this
makes people more engaged thus improves team work between employees by emerging
skills and experiences from different levels and functions to solve problems or
think of new ideas.

 

Information and Knowledge
Visualization

Information Visualization

Representing data visually so users can gain insights from abstract
data. For example, the use of dashboards or scatter plots.

Uses of Information
Visualization

·      
Presentation

Representing data in order to make hard concepts
and the relationship between their aspects easier to understand.

Figure 1 Presentation

·      
Explorative Analysis

Taking a broad look at patterns and understand
the relationship between data.

Figure 2 Explorative Analysis

Knowledge
Visualization

Knowledge
visualization: represent the knowledge without the help of computers. It is
based on the abilities of the human perception system, which is able to process
visual representations very effectively. It aims at supporting cognitive
processes in generating, representing, structuring retrieving sharing and using
knowledge.

Differences and Similarities

Information
Visualization focuses only on computer-based visualizations. On the other hand,
Knowledge Visualization focuses on anything that cannot be put into a digital
carrier. For example, sketches and knowledge types.

 

Table 1 Differences
and Similarities between Information and Knowledge Visualizations

Aspect

Information
visualization

Knowledge
visualization

Goal

Users
computer supported applications on large amounts of data to get new insights

Uses visual
representation to improve the transfer and the creation of knowledge

Benefit

Improves information access,
retrieval and exploration of large data sets

Augments knowledge intensive
processes

Content

Explicit data
like facts and numbers; complex information structures

Knowledge
types like experiences, insights, instructions or assumptions; social
structures, relationship between knowledge and a human actor

Recipients

Individuals

Individuals or groups to transfer
and collaborative settings to create knowledge

Influence

New insights
for information science, data mining, data analysis, information exploration
and problems such as information exploration, information retrieval, human
computer interaction, interface design

New insights
for visual communication science, knowledge management and problems such as
knowledge exploration, transfer, creation, application, information overload,
learning, design, interface design, visual communication

Proponents

Researcher with background in
computer science

Researcher with background in
knowledge management, psychology, design, architecture

Means

Uses computer
supported methods

Uses computer
supported and non-computer supported visualization methods

 

 

 
 
 

TYPES OF KNOWLEDGE
VISUALIZATION

Sketch

Sketches are simple drawings that help to visualize the key
features and the main idea very quickly. They are relatively old since already
Leonardo da Vinci used them to visualize his insights and investigations.

Figure 3 Sketch

 

Diagram

Diagrams are abstract, schematic representations that are used to
display, explore and explain relationships. They reduce complexity, make
abstract concepts accessible and amplify cognition. Unlike sketches they are
precise and determined. Examples for diagrams are bar- and pie charts, Gantt-,
Fenn- or process diagrams.

Figure 4 Diagrams

Map

Maps or plans are in the architectural context used to present
entities on a different scale and to bring three-dimensional objects into a
two-dimensional visualization. Maps present overview and detail at the same
time, help to structure information, motivate and activate employees, establish
a common story and ease access to information.

Figure 5 Map

Images

Images can be renderings, photographs or paintings that may
represent the reality but can also be artistic. They are able to address
emotions and can inspire, motivate or energize the audience and thus often used
for advertisements.

 

 

 

 

 

 

 

 

 

 

 

Elements of Successful Data
Visualization

Story telling

To be effective, data
visualization should present data coming from different sources in a simple,
sequential and clear manner that can help the audience to understand and make
sense of the business situation and the company’s performance and enable them
to reach an effective decision.

 Easily understandable

Data visualization should be able
to display the information to the target audience in an easily understandable
format adapted to their background.

Adapted to the target
audience

It is important to consider the
type of audience/decision makers that we target, their expectations and what
type information they need before selecting the data visualization solution
that is most adapted to the decision situation and that will help them to
understand information better.

Honesty and usefulness

Data that is represented using
visualization should be integrated, accurate and consistent. It should present
clear and useful data that are usually used by professionals and analysts for
problem definition and decision making. Data Visualization should be as honest
and effective as possible in revealing the current state of your business.

Adaptable

Data
visualization should be user friendly and the process of updating with new data
can be done frequently and easily without the need of IT professionals or
re-building everything from the scratch. This will minimize the time and cost
of update and it will make the data available to decision makers on time.

 

 

 

 

 

 

 

Four Questions to Ask When
Choosing the Right Chart to For the Data Set

Do we need to compare values?

The ideal chars that can be used to compare one or multiple value
sets are:

Figure
6
Bar Chart

Figure
7
Line Chart

Figure
8
Scatter Plot Chart

Figure
9
Circular Area

 

 

 

 

 

 

 

Do we need to show the
architecture of something?

The perfect charts that can show how a single part can create the
totality of something are:

Figure
10
Pie Chart

Figure
11
Waterfall Chart

Figure
12
Stacked Column Chart

Figure
13
Area Chart

 

 

 

 

 

 

 

 

Do we need to analyze
patterns and trends in our data sets?

If we need to see how data sets perform during a particular time
period we should use:

Figure
14
Line Chart

Figure
15
Dual Line Axis

Figure
16
column chart

 

 

Do we need to understand the
interesting relationship between data sets?

We can see the similarities and the relations between variables,
and how variables affect each other using these charts:

Figure
17
Bubble Chart

Figure
18
Scatter Plot Chart

Figure
19
Line Chart

 

 

 

 

 

 

 

Real Life Example

 

The following case shows the use of visualization for decision
making by New Zealand smoking regulations and legislation.

A scatter graph was generated to demonstrate the relation between
smoking and lung damage.

The graph showed that as the number of years that some individual
smokes increase, lung damage will increase too as result it could lead to lung
cancer.

Figure 20 Degree of Lunge Damage

 A second graph was generated
by the government that shows the tragic increase in lung cancer deaths after the
World War II.

Figure 21 Lunge Cancer and Smoking

 

 

Using the data in both graphs in addition to a report from the
Surgeon General in 1964, led to a radical change in smoking policy in New
Zealand, they made a decision to stop the promotion and the free disposition of
tobacco to prisoners and to arm services personnel, and they also started a
Smoke Free Environment Act. 

Time series tobacco consumption graphs continue to be used by the
government to monitor the effectiveness of government regulations and
incentives and anti-smoking campaigns in the country.