Decision making based on information has been the single most important objective of a data warehousing or big data pursuit. No matter how big, fast and varied data are generated and processed; decision makers are only concerned with the consumption of its end result – data visualization. Data visualization simply means representing data in a visually appealing manner to enable understanding of the context in which we operate. Data visualization is a “moment of truth” that stems from a data management initiative. It is a very linear process of decision making; and hence, critical to its success. However, data visualizations also possess the potential to put an end to such initiatives; especially, when they are either heavily biased on just the design or contain information overload. This webinar on the art and technique of data visualization focuses sharply on the one thing that matters most to qualify for effective data visualization: the truth that comes out from data. We have facilitated the discussion with the help of our 3D framework: Design, Discovery & Data. After registering, you will receive a confirmation email containing information about joining the webinar.

1. Welcome to the webinar on
The Art & technique of Data
Visualization
Presented by
&

2. This webinar aims to cover the following
1
Why BI projects fail?
2
What is Data Visualization?
3
Who needs Data Visualization?
4
The 3D Framework
5
Lets hear it from you

3. Why BI projects fail?

5. Data Visualization - Defined
Data Visualization is the art and technique of representing data in a graphical and pictorial format
It is the moment of truth resulting from any DWH / Big Data initiatives
Why is it important?
Human
Brains
Are equipped to perceive
meaningful patterns, outliers, and
structures to form a judgment.
Decision
Making
No 1 priority : support decision
making.
Adding value to the volume, variety
and velocity of data that is
generated and processed.
Communication
Inform : What & Why
Educate : What If, What Next &
What Can
Collaborate : Who, What Else & How
Exactly.

6. Data Visualization Eco-System
Business
Users
IT
Executives
Decision
Making
Operational
Efficiencies
Regulators
Who needs
Data
Visualization
Why?
Regulatory
Compliance
Customers
Suppliers
Monetize
existing data
Mind Share
Analysts
Progress in
value chain

7. Primary users of Data Visualization
77% are
Business executives and management
58%
are
Business Analysts
55% are
Departmental Managers
38%
IT Executives
37%
Data Analysts or Scientists
24%
25%
Operations / SCM
Front line employees
14%
Customers
8%
Partners & Suppliers
TDWI research : Based on answers from 388 respondents

8. Who in your organization develops & deploys Visualization?
The gap is fast reducing.. Thanks to the
New self-service and personalization
Technologies.
Business executives are the largest
Consumers of Data Visualization
This era is characterized by business analyst /
users making and also consuming their own
data through visualizations..
Can the IT developers make themselves more relevant?
TDWI research : Based on answers from 388 respondents

9. Components
Our 3D Framework
Principles
Enablers
BUSINESS KNOWLEDGE
Data Accuracy
Visual Querying
Multidimensional
Personalization
DESIGN
Know your
audience
Personalization
Collaboration
DISCOVERY
Keep it simple
Highlight
DATA VISUALIZATION PRODUCTS
INFORMATION DELIVERY METHODS
DATA

10. You’ve got to start with the customer
experience and work back toward the
technology – not the other way around
STEVE JOBS

11. Know your audience
Best Practices
Conduct business workshops to finalize the
requirements document
Make sure you understood the data that is required
Challenges
Get a sign off first on the design and layout
Lack of participation from business users
Break it down to individual parts / graphs /
quadrants and take a sign off
Low / No awareness about the business or domain
Data Visualization created in silos

12. How not to do it – CEO Dashboard for a manufacturing co.
Is this for the CEO or Production head?

13. How best can it be done – CEO Dashboard for a manufacturing co.

14. One more way to do it..
KPI Map

15. Highlight
Recommendations
Color : Contrast
Position
Length
Color : Intensity
Motion
Width
Alert
Challenges
Limited space for utilization
Limited or Excess data to show
Size
Harvey Balls
Shape

18. Demo – (Alerts)
Disbursement Dashboard

19. Visual Querying
Best Practices
Information relevance
Sequence of clicks
Challenges
Present the Metadata
How many drill-downs?
Parent child relationship
How to showcase correlations?
How to avoid information chaos?

20. Demo – Excel Illustration

21. Demo – NPA Analysis

22. Personalization

23. Personalization

24. Personalization

25. Personalization

26. Choosing the right visualization: few examples
Visualization Type
Description
Chart Type
Comparison
Many Items
Horizontal Bar Chart
Comparison
Over time: Many periods
Circular Area Chart
Comparison
Few Periods: Many Categories
Line Chart
Relationship
Two Variables
Scatter Chart
Relationship
Two + Variables
Bubble Chart
Distribution
Few Data Points
Column Histogram
Three Variables
3D Area Chart
Composition
Few periods: Changing over time
Stacked column chart
Composition
Static: simple share
Pie Chart
Composition
Universe of content
Tree Map
Distribution

27. Repository of best practices
Avoid Scroll bar as far as possible
Facilitate definition of the visualization
Convert decimal points to a perfect integer
Make navigation really easy for the end user
All axes should be properly labelled
Good idea to show data quality % in the visualization
Avoid using special characters or short forms for labels
While displaying bar charts, order data in descending order

28. Interested in knowing more?
Interested in knowing more about our 3D Framework for Data Visualization
& how it can add value to your clients?
Then, our dedicated training workshops on the “The Art & Technique of
Data Visualization” is the best forum to learn more practical and industry
accepted methods on improving data visualization.
Contact:
info@ellicium.com
info@compulinkacademy.com
Stay tuned for our next webinar on “Text Analytics”

29. Let’s hear it from you