This month, we will dive into the world of data analysis and visualization. As data continues to proliferate our lives and work, the question of how to make sense of it and turn it into information and knowledge becomes more and more challenging. At the same time, powerful tools are becoming available to help analysts sift through data and present it in a way that draws attention to key bits of knowledge than can be derived. As such, the skills related to using these tools effectively have become highly sought-after as organizations seek to dig out the treasures hidden in their data troves. Presentation by Stephen Lett (Procter & Gamble)

1. Insights from Data
Visualization
STEPHEN LETT
19 NOVEMBER 2014

2. Introduction
 Procter and Gamble
Global Business Services,
Global Service Manager
 15 Years Career
 3 Years – Planning and
Manufacturing Business
Intelligence Service
Manager.

3. Data Visualization Discussion Points
What is it?
Brief overview of what data
visualization is and why it is
important.
What to Know First?
Questions to consider when
deciding how to best leverage
Data Visualization.
Visualization Design
Few golden tips to remember to
make your visualization stand
out.

4. Quick Survey
When was the first line chart and bar
graph produced?
1639 1786 1801 1920
William Playfair,
Scottish Engineer
1759-1823

5. Data Visualization Transformation
Reporting
Visualizing
Business Intelligence

6. Data
Visualization
WHAT IS IT?

7. What is Data Visualization?
 Simple Definition: Visual
Representation of
Information
 A means of communicating
information clearly and
effectively through graphical
displays

8. Edward Tuft, 1983
 At their best, graphics are instruments for reasoning about quantitative
information.
 Often the most effective way to describe, explore, and summarize a
set of numbers - even a very large set - is to look at pictures of those
numbers.
 Well-designed data graphics are usually the simplest and at the same
time the most powerful.

9. Why is Data Visualization Important in
Business?

10. Why is Data Visualization Important in
Business?
 VUCA World
 Data is the consistent tool that simplifies the VUCA into where we need to
focus.
 Translates into COST SAVINGS
 Getting to the right answers faster is the only differentiator in competitive
markets.
 Fosters PRODUCTIVITY and COLLABORATION
 Good graphical representations of data communicates complex ideas with
clarity, precision, and efficiency.
 Getting teams on the same page faster, and individual contributors on a
head start for where to focus.

11. Data
Visualization
WHAT TO
KNOW FIRST?

12. 3 Factors to Consider:
1.Who’s the “Who”?
2.What’s the “What!”?
3.How to Motivate towards
ACTION?

13. Who’s the Who?
 Know Their Background:
 Experts in the Field/Subject?
 Analysts – using the data for projections / transformations?
 Management – strategic directions?
 Define Consumerization of Data
 Instructor Driven
 Self-Discovery
 Set the Right Expectations
 Basic Overview
 Category Glance
 Deep Discovery in 1 Vector

14. Communication vs Analysis
Visualization for
Communication
Visualization for Analysis
Audience General Public
Senior Leaders
Analysts
Intent Summary & Conclusions
Explain the Situation
Explorations and Observations
Data Consumption Immediate Understanding Required
Complexity SIMPLE Situational Based
Time to Generate Fairly Quick Real-Time**

15. What’s the Message?
Background / General Knowledge?
Summary to Support a Decision?
Deep Analysis to Drive a New Activity?
Communicating new insights?

16. Plan
Accordingly!
Good Rule of Thumb:
Work on Audience and Message
understanding and intent
BEFORE building any visualizations!

17. Motivating Towards ACTION
Based on the AUDIENCE and the MESSAGE  Pick the visualization that will lean towards ACTION!
- If you can communicate the message clearly and efficiently in a simple sentence, DO IT;
- If Data Tables are required, use them – but don’t lean on visual perception alone. Manage the message!

18. Data
Visualization
VISUALIZATION
DESIGN TIPS

19. Key Steps in Designing your Visualization
Data Preparation
Clean Data is a MUST:
- Eliminate “Noise” (e.g.
nulls, missing values)
- Clarify data (full set,
representative sample,
etc)
Normalize and Transform
Upfront
- Aggregate
- Filter
- Primary / Secondary Keys
Choose Your Graph
• Amount of Data
• Type of Data
• Data Relationships
• Conclusion for
Audience
Good Design Principles
1. Avoid “Chartjunk”
2. Use Colors Wisely
3. No Misleading Scales
4. Dual Axis Charts are
for Experts

20. Good Design Principle 1 – Chartjunk
What is It?
 Visual Content that:
 Adds little / no value
 Serves little / no purpose
 Distracts from real data
 Examples
 Shadows / Color Effects

21. Good Design Principle #2 - Colors
If the point is: Consistent performance at 40%+, which chart uses color most effectively ?
• Use the same color, except when color differences make a difference
• Use a single, neutral background color (if needed at all)

22. Good Design Principle #3 - Scaling
How are we doing with our budget forecast vs actuals over last 6 months?
Notice the “Y-Axis”? Always NOTE if an axis doesn’t start with 0

23. Good Design Principle #4 – Dual Axes

24. Summary
Data Visualization is:
Communicating clearly and
effectively through graphics.
Know Your Plan
1. Who’s the Who?
2. What’s the “What!”?
3. Motivate to ACT!
Golden Design Tips
1. Avoid Chartjunk
2. Use Colors Wisely
3. Beware of Scaling
4. Dual Axes Needed?

25. Foundational Principles
 If the message is simple, keep it simple.
 If the message is complex, make it look simple.
Always tell the truth – don’t use graphs to distort the
data.

26. Questions?

27. Backup Slides

28. William Playfair Graphs
William Playfair,
Scottish Engineer
28
1759-1823

29. Q3. Is visualization the best way to share the data, show the findings, and/or
reveal the insight?
Does it provide insight or understanding that was not obtainable with the original representation (text, table, etc)?
Tables Graphs
Data are arranged in columns and rows Data are displayed in relation to one or more
29
axes along which run scales that assign meaning
to the values
work best when the display will be used
to look up individual values or the
quantitative values must be precise.
work best when the message resides in the shape
of the data (that is, in patterns, trends, and
outliers).