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How to Unlock Value in Data Using
Data Visualizations[#VIZ]
Chaitanya Sagar “CS”
cs@perceptive-analytics.com
646.583.0001
Data Analytics Spreadsheet Solutions|
2
New York HyderabadSan Bruno Boston
Analytics
Spreadsheet
Solutions
Data Visualizations
Marketing
Marketing Mix Modeling Price Promotion Analysis Catalogue Optimization
Segmentation Web Analytics Churn Analysis
Risk Management
Credit Risk Management Liquidity Risk Management Capital Allocation Analysis
Collateral Management Fraud Detection
Supply Chain
Inventory Optimization Demand Analytics Distribution Network Optimization
Sourcing Analytics Freight Lane Analytics
Verticals
Consumer Packaged Goods
Retail
Healthcare
Our Services
3
Analytics
Spreadsheet
Solutions
Our Services
4
Spreadsheet Applications
Contract Negotiation
Litigation Modeling
Decision Support Tools
Dashboards
Reporting
Simulations
Financial Modeling
Location Strategy to Improve
Effectiveness of a Branch Network
perceptive-analytics.com/tag/case-study/
Reinventing Coupons: Strategies for
Successful Coupon Campaign
perceptive-analytics.com/tag/case-study/
Questions?
Use ask-a-question feature in
GoToWebinar
cs@perceptive-analytics.com 646.583.0001 #VIZ 8
Which industry do you work in?
• Retail and Consumer Packaged
Goods
• Health Care
• Banking, Financial Services and
Insurance
• Information Technology /
Consulting/Others
cs@perceptive-analytics.com 646.583.0001 #VIZ 9
Which Function Do You Work In?
• Analytics
• BI
• Sales and Marketing
• IT
• Finance/Operations / Human
Resources
cs@perceptive-analytics.com 646.583.0001 #VIZ 10
Overview
PicyHoriaVarlan
cs@perceptive-analytics.com 646.583.0001 #VIZ 11
The need for visualizations
How visualizations help unlock value
How to build visualizations
-Purpose
-Design
Tools
Q&A
The Need for Visualizations
cs@perceptive-analytics.com 646.583.0001 #VIZ
Our Needs Outgrew Charts
More data!
high dimensional
ˠ
cs@perceptive-analytics.com 646.583.0001 #VIZ
Humans are Visual
Brain can absorb large amounts of information
and find patterns (and deviations!)
PicbyDanFoy
cs@perceptive-analytics.com 646.583.0001 #VIZ
●
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“Mind is a Pattern-Matching Machine”
Edward De Bono
Mechanism of the Mind (1969)
cs@perceptive-analytics.com 646.583.0001 #VIZ
cs@perceptive-analytics.com 646.583.0001 #VIZ
x y x y x y x y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
Mean 9 7.5 9 7.5 9 7.5 9 7.5
Variance 11 4.122 11 4.122 11 4.122 11 4.122
Correlation
Linear Regression y = 3.00 + 0.500x y = 3.00 + 0.500x y = 3.00 + 0.500x y = 3.00 + 0.500x
Anscombe's Quartet
I II III IV
0.816 0.816 0.816 0.816
Anscombe’s Quartet
How are the Data Sets Different?
• All four data sets are identical
• Distribution is different
• Median and Mode could be different
• Not Sure
cs@perceptive-analytics.com 646.583.0001 #VIZ
Statistics May Hide Something
cs@perceptive-analytics.com 646.583.0001 #VIZ
Statistics and bikinis show a lot,
but not everything.
- Toby Harrah
American baseball player
cs@perceptive-analytics.com 646.583.0001 #VIZ
Where do Data Visualizations Fit
in Data Analytics Process?
cs@perceptive-analytics.com 646.583.0001 #VIZ
Acquire Data
Clean Data
Explore Data
Data
Modeling
Insights
Communicate
Where does Data Visualization Fit in Data Analytics
Process?
cs@perceptive-analytics.com 646.583.0001 #VIZ
How Visualizations Help Unlock
Value
cs@perceptive-analytics.com 646.583.0001 #VIZ
Make Sense of Vast Data Quickly
cs@perceptive-analytics.com 646.583.0001 #VIZ
Make Sense of Vast Data Quickly
cs@perceptive-analytics.com 646.583.0001 #VIZ
Elicit Questions You Did Not Ask Before
cs@perceptive-analytics.com 646.583.0001 #VIZ
What should we do to
make India a land of equal
opportunity for all, free of
prejudice and
discrimination?
- Ratan Tata
cs@perceptive-analytics.com 646.583.0001 #VIZ
Sample Responses
• @RNTata2000 in all democracies there is a gap on what ple want and what
politicians r delivering,they r not doing the right thing, lobbying?
• @bangaarm @RNTata2000 Budget 2012: This year is Tax Holiday. No
income tax on your earnings. This is to bring back all the black money to
India
• @sri_v22 @RNTata2000 1. Kill corruption 2. Electoral reforms so that
honest ple can get into politics 3. Media & activists should increase their
role
• @joseaaa @RNTata2000 Can't be articulated with 140 characters. Quality
education for the masses is magic potion that can address most of the
problem.
• @dharmeshsharma8 @RNTata2000 Could we have your view on this
topic?
cs@perceptive-analytics.com 646.583.0001 #VIZ
Elicit Questions You Did Not Ask Before
cs@perceptive-analytics.com 646.583.0001 #VIZ
Discover New Data Relationships
cs@perceptive-analytics.com 646.583.0001 #VIZ
Discover New Data Relationships
cs@perceptive-analytics.com 646.583.0001 #VIZ
Show Others What You See
cs@perceptive-analytics.com 646.583.0001 #VIZ
Show Others What You See
http://guns.periscopic.com
How to Create Visualizations
cs@perceptive-analytics.com 646.583.0001 #VIZ
Data
Analyst
Tool
Insights
cs@perceptive-analytics.com 646.583.0001 #VIZ
Data
Analyst
Tool
Domain /
Situation
Imagination
cs@perceptive-analytics.com 646.583.0001 #VIZ
Purpose Design
cs@perceptive-analytics.com 646.583.0001 #VIZ 45
Purpose
PicbyMerviEskelinen
TasksAudience Answers
cs@perceptive-analytics.com 646.583.0001 #VIZ 46
Guidelines
• Understand your goals
• Determine the most important dimensions of
your data
• Determine key data relationships
• Show data close to reality e.g. maps, time lines
etc.
• Choose encoding wisely “Function first, suave
next”
• What questions do you want answered?
cs@perceptive-analytics.com 646.583.0001 #VIZ
Design
cs@perceptive-analytics.com 646.583.0001 #VIZ 48
Visual Encoding
cs@perceptive-analytics.com 646.583.0001 #VIZ
cs@perceptive-analytics.com 646.583.0001 #VIZ
What Do You Think About This Chart?
cs@perceptive-analytics.com 646.583.0001 #VIZ
What’s Wrong with this Chart?
• Too Big
• Poor colors
• Nothing wrong, looks good
• It’s just wrong
• No comment
cs@perceptive-analytics.com 646.583.0001 #VIZ 53
39.0%
19.5%
9.8%
7.4%
3.1%
21.2%
U.S. SmartPhone Marketshare
RIM
Apple
Palm
Motorola
Nokia
Other
Edward Tufte
cs@perceptive-analytics.com 646.583.0001 #VIZ
$450
$650
$850
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Monthly Revenue
470
900
700
550
800
600
473
610 600
300
500
450
$0
$200
$400
$600
$800
$1,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Monthly Revenue
ThousandsThousands
Avoid Chart Junk
$0
$200
$400
$600
$800
$1,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Monthly Revenue
Thousands
400
900
700
550
800
500
780
610 600
450
500
300
$0
$200
$400
$600
$800
$1,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Monthly Revenue
Avoid Chart Junk
Thousands
Maximize Data Ink Ratio
Data-ink/Total ink used
Maximize Data Density
(# entries in data matrix)/(area of graphic)
Colors
cs@perceptive-analytics.com 646.583.0001 #VIZ 60
Colors
cs@perceptive-analytics.com 646.583.0001 #VIZ 61
Create Color Harmony
ColorBrewer2.org
Tools
cs@perceptive-analytics.com 646.583.0001 #VIZ
cs@perceptive-analytics.com 646.583.0001 #VIZ
cs@perceptive-analytics.com 646.583.0001 #VIZ
Resources
Designing Data Visualizations (Noah Iliinsky, Julie Steele)
Visual Encoding
complexdiagrams.com/properties
richardbrath.wordpress.com
Edward Tufte
edwardtufte.com
D3JS.org
Processing.org
Principles of Visualization Design
D3 Visualizations
cs@perceptive-analytics.com 646.583.0001 #VIZ
Q&A
cs@perceptive-analytics.com 646.583.0001 #VIZ
Your Feedback on this Webinar
• Below Expectations
• Met Expectations
• Above Expectations
cs@perceptive-analytics.com 646.583.0001 #VIZ 70
Thank you!
71
cs@perceptive-analytics.com
+1.646.583.0001

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Editor's Notes

  1. http://www.tableausoftware.com/public/gallery/new-jersey-test-score-analysis-visualization http://www.tableausoftware.com/public/gallery/geography-diabetes http://www.tableausoftware.com/public/gallery/beautiful-and-dangerous-history http://bl.ocks.org/tjdecke/5558084 http://mbostock.github.io/d3/talk/20111116/pack-hierarchy.html
  2. http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html