Title: Unlock Value from Data Visualizations
1How to Unlock Value in Data Using Data
VisualizationsVIZ
Chaitanya Sagar CScs_at_perceptive-analytics.com
646.583.0001
2Spreadsheet Solutions
Data Analytics
3Our Services
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
4Our Services
Analytics
Spreadsheet Solutions
Spreadsheet Applications Contract
Negotiation Litigation ModelingDecision Support
Tools
Dashboards Reporting Simulations Financial
Modeling
5Location Strategy to Improve Effectiveness of a
Branch Network
perceptive-analytics.com/tag/case-study/
6Reinventing Coupons Strategies for Successful
Coupon Campaign
perceptive-analytics.com/tag/case-study/
7Financial Forecasting Tool for a Silicon Valley
Startup
Kina, Inc. is a hi-tech company based in Silicon
valley. The companys operations were growing and
wanted to track the cash flows in end-to-end
business process. The CFO of the company wanted
to project future cash requirements.
Cash Position Is the company investing or
accruing cash?
Net Sales How fast are revenues increasing?
Net Margin Is business profitable?
Avg. Selling Price Is the average sales price
picking up?
A comprehensive financial model which integrated
the working of different departments was built.
The model performed scenario analysis at various
levels of sales and inventory investments to
estimate the cash requirement. This model was
presented to the executive board of XYZ, Inc.
8Questions?
- Use ask-a-question feature in GoToWebinar
cs_at_perceptive-analytics.com 646.583.0001
VIZ
9Which industry do you work in?
- Retail and Consumer Packaged Goods
- Health Care
- Banking, Financial Services and Insurance
- Information Technology / Consulting/Others
cs_at_perceptive-analytics.com 646.583.0001
VIZ
10Which Function Do You Work In?
- Analytics
- BI
- Sales and Marketing
- IT
- Finance/Operations / Human Resources
cs_at_perceptive-analytics.com 646.583.0001
VIZ
11Overview
- The need for visualizations
- How visualizations help unlock value
- How to build visualizations
- -Purpose
- -Design
- Tools
- QA
Pic y Horia Varlan
cs_at_perceptive-analytics.com 646.583.0001
VIZ
12The Need for Visualizations
cs_at_perceptive-analytics.com 646.583.0001
VIZ
13Our Needs Outgrew Charts
cs_at_perceptive-analytics.com 646.583.0001
VIZ
14Humans are Visual
- Brain can absorb large amounts of information and
find patterns (and deviations!)
Pic by Dan Foy
cs_at_perceptive-analytics.com 646.583.0001
VIZ
15?
16(No Transcript)
17(No Transcript)
18(No Transcript)
19(No Transcript)
20(No Transcript)
21(No Transcript)
22Mind is a Pattern-Matching Machine
Edward De Bono Mechanism of the Mind (1969)
cs_at_perceptive-analytics.com 646.583.0001
VIZ
23cs_at_perceptive-analytics.com 646.583.0001
VIZ
24Anscombes Quartet
25How are the Data Sets Different?
- All four data sets are identical
- Distribution is different
- Median and Mode could be different
- Not Sure
cs_at_perceptive-analytics.com 646.583.0001
VIZ
26Statistics May Hide Something
cs_at_perceptive-analytics.com 646.583.0001
VIZ
27Statistics and bikinis show a lot, but not
everything.
- Toby Harrah
- American baseball player
cs_at_perceptive-analytics.com 646.583.0001
VIZ
28Where do Data Visualizations Fit in Data
Analytics Process?
cs_at_perceptive-analytics.com 646.583.0001
VIZ
29Where does Data Visualization Fit in Data
Analytics Process?
cs_at_perceptive-analytics.com 646.583.0001
VIZ
30How Visualizations Help Unlock Value
cs_at_perceptive-analytics.com 646.583.0001
VIZ
31Make Sense of Vast Data Quickly
cs_at_perceptive-analytics.com 646.583.0001
VIZ
32Make Sense of Vast Data Quickly
cs_at_perceptive-analytics.com 646.583.0001
VIZ
33Elicit Questions You Did Not Ask Before
cs_at_perceptive-analytics.com 646.583.0001
VIZ
34cs_at_perceptive-analytics.com 646.583.0001
VIZ
35Sample Responses
- _at_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? - _at_bangaarm _at_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 - _at_sri_v22 _at_RNTata2000 1. Kill corruption 2.
Electoral reforms so that honest ple can get into
politics 3. Media activists should increase
their role - _at_joseaaa _at_RNTata2000 Can't be articulated with
140 characters. Quality education for the masses
is magic potion that can address most of the
problem. - _at_dharmeshsharma8 _at_RNTata2000 Could we have your
view on this topic?
cs_at_perceptive-analytics.com 646.583.0001
VIZ
36Elicit Questions You Did Not Ask Before
cs_at_perceptive-analytics.com 646.583.0001
VIZ
37Discover New Data Relationships
cs_at_perceptive-analytics.com 646.583.0001
VIZ
38Discover New Data Relationships
cs_at_perceptive-analytics.com 646.583.0001
VIZ
39Show Others What You See
cs_at_perceptive-analytics.com 646.583.0001
VIZ
40(No Transcript)
41Show Others What You See
http//guns.periscopic.com
42How to Create Visualizations
cs_at_perceptive-analytics.com 646.583.0001
VIZ
43Analyst
Data
Tool
Insights
cs_at_perceptive-analytics.com 646.583.0001
VIZ
44Analyst
Data
Tool
Domain / Situation
Imagination
cs_at_perceptive-analytics.com 646.583.0001
VIZ
45Purpose
Design
cs_at_perceptive-analytics.com 646.583.0001
VIZ
46Purpose
Pic by Mervi Eskelinen
Tasks
Audience
Answers
cs_at_perceptive-analytics.com 646.583.0001
VIZ
47Guidelines
- 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_at_perceptive-analytics.com 646.583.0001
VIZ
48Design
Pic Ecotrust Canada
cs_at_perceptive-analytics.com 646.583.0001
VIZ
49Visual Encoding
cs_at_perceptive-analytics.com 646.583.0001
VIZ
50cs_at_perceptive-analytics.com 646.583.0001
VIZ
51(No Transcript)
52What Do You Think About This Chart?
cs_at_perceptive-analytics.com 646.583.0001
VIZ
53Whats Wrong with this Chart?
- Too Big
- Poor colors
- Nothing wrong, looks good
- Its just wrong
- No comment
cs_at_perceptive-analytics.com 646.583.0001
VIZ
54(No Transcript)
55Edward Tufte
cs_at_perceptive-analytics.com 646.583.0001
VIZ
56Thousands
Thousands
57Avoid Chart Junk
Thousands
58Avoid Chart Junk
Thousands
59Maximize Data Ink RatioData-ink/Total ink used
Maximize Data Density ( entries in data
matrix)/(area of graphic)
60Colors
cs_at_perceptive-analytics.com 646.583.0001
VIZ
61Colors
cs_at_perceptive-analytics.com 646.583.0001
VIZ
62Ideas for Color Harmony
Complementary
cs_at_perceptive-analytics.com 646.583.0001
VIZ
63ColorBrewer2.org
64cs_at_perceptive-analytics.com 646.583.0001
VIZ
65Tools
cs_at_perceptive-analytics.com 646.583.0001
VIZ
66cs_at_perceptive-analytics.com 646.583.0001
VIZ
67cs_at_perceptive-analytics.com 646.583.0001
VIZ
68Resources
- 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_at_perceptive-analytics.com 646.583.0001
VIZ
69QA
cs_at_perceptive-analytics.com 646.583.0001
VIZ
70Your Feedback on this Webinar
- Below Expectations
- Met Expectations
- Above Expectations
cs_at_perceptive-analytics.com 646.583.0001
VIZ
71Thank you!
cs_at_perceptive-analytics.com 1.646.583.0001