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Visual Decision Making . Information Visualization. Ajit Nema Director, Deloitte Consulting. Information Visualization: What is it? . Interactive visual analysis of information to yield more insight, more action, and more effective decisions.

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visual decision making

Visual Decision Making

Information Visualization

Ajit Nema

Director, Deloitte Consulting

slide2

Information Visualization: What is it?

Interactive visual analysis of information to yield more insight, more action, and more effective decisions

slide4

Total Fertility by life expectancy in China, India and the USA, 1950-2050

  • Huge amount of data
  • Multidimensional data analysis
    • Total fertility rate
    • Life expectancy at birth
    • Across 50 years
    • Co-relate across countries
  • Traditional ways will requires multiple pivots and big spreadsheet or multiple graphs
slide5

Total Fertility by life expectancy in China, India and the USA, 1950-2050

Source: World Population Prospects, the 2008 Revision. United Nations, Department of Economic and Social Affairs (DESA), Population Division, New York, 2009. See: www.unpopulation.orgNote: Total Fertility (TFR) is the average number of births per woman

information visualization evolved over a period of time
Information Visualization: Evolved over a period of time
  • Pre 17th Century
    • Maps and Navigation
    • Informational Diagrams
  • 17th – 18th Century
    • World Maps
    • Astronomical Visuals
  • 19th – 20th Century
    • Polar Area Charts
    • Bar and Pie Charts
  • Current Day
    • Visualization Methods for Real Time Data
    • Explorative Data Analysis
    • Tree-Maps and Network Graphs
information visualization success stories
Information Visualization: Success Stories
  • Usability Redefined
  • Innovative User Interface: Touch and gesture
  • Rich User Experience: Icons, Apps
  • Apple now dominates the mobile and tablet market
  • Interactive Multi-media Gaming
  • Intuitive motion controls and social gaming experience
  • Engaged people of all ages
  • Wii captured 47% of market in ’09 (source: IBIS Capital)
  • Personalized shopping
  • Puts shopper in the driver seat and allowed custom designs
  • E-Commerce sales ran ahead of all other channels in ’10
    • Online sales increased 25%, surpassed 100m
information visualization what s different
Information Visualization: What’s Different?

Increased Scale

Dramatic increase in computing horsepower coupled with advancement in computer interaction techniques enables handling larger data sets

  • Reports and analysis which used to take months to aggregate can be run in minutes
  • Modern visualization techniques can handle complex cardinalities and joins
  • More powerful computing enables multivariate analysis
information visualization what s different1
Information Visualization: What’s Different?

Democratization of Data

Visualization puts the power of data analysis in the hands of more organizational stakeholders

Worldwide Growth Projection

2008 - 2012

  • More intuitive drill-paths allow greater depth of analysis
  • Knowledge workers become more empowered as visual discovery tools become more powerful
  • Information becomes more pervasive and transparent across the organization

Users

Data

Exabytes

Devices

(Units)

Interactions

(B-per day)

1.24B

~2.5E

416M

475B

3X

4.5X

7.7X

8.5X

Servers

PC Clients

442M

~4.7E

56B

54M

Internet-Connected Devices

information visualization what s different2
Information Visualization: What’s Different?

User Autonomy

Information is making shift from being presented in a predetermined fashion to a malleable, versatile tool which stakeholders can readily use for decision making

  • Mixed environment of partially ‘pre-paved’ information but mostly self exploration of data by manipulating GUIs
  • Stakeholders who are closer to the front-lines have more access to analysis tools
  • Overall shift from passive presentation to autonomous, active discovery
slide14

Information Visualization

Approach & Techniques

slide15

Traditional ways: Excel, Pivots, Reports

May lead to information fatigue

PIVOTS

Multi-dimensional pivoting leads to an explosion of combinatory possibilities for analyzing data.

A 3-D space with an average of 100 values leads to a whopping one million combinations to browse through.

EXCELS

Reams of rows and columns must be analyzed in order to interpret what are usually a few main business decisions.

REPORTS

These are much like Excel spreadsheets, but without the functionality. Some are designed to provide just-in-case information, they rarely address a business problem

DASHBOARDS:

Dashboards suffer from data aggregations which balance out important variations in the underlying data.

slide16

Mashup: Hurricane Irene web monitoring

  • Mashup of disparate data required to analyze the complete information and make effective decisions
  • Pull in multiple feeds that will allow the client to see how their stores and logistics will be impacted over the next 72 hours
slide17

Relationships in the data: patient drug prescriptions

Each row represents the drugs a patient took in a specific quarter

500

1000

1500

2000

2500

3000

Patients

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Drugs

slide18

The same patient drug data, co-clustered

Here the rows (patients) and columns (drugs) are clustered to reveal usage patterns

Patients on Reyataz, Truvada, & Norvir

Patients on Atripala only

Patients on broad mix of Truvada, Norvir, Isentress, Intelence, & Prezista

Norvir

Reyatez

Truvada

Atripla

Isentriss

Intelence

Prezista

slide19

Network Analysis –who are the influencers

Each doctor is a blue circle whose size is proportional to the amount of drugs prescribed

Doctors are linked if they share an organizational affiliation or have common patients

The thickness of the edge is proportional to the number of shared patients

The three red dots are individuals thought to be key influencers

This network graph shows that others may be equally or more influential

information visualization additional techniques
Information Visualization: Additional techniques

Tree Map

Newsmap is a live visualization of Google news: a tree map algorithm fills the available screen space, as size denotes the importance of specific headlines, colour distinguishes news categories, and brightness the story novelty.

Heat Map

Explore the behavior of your visitors with a heat map. More popular sections, which are clicked more often, are highlighted as “warm” – in red color.

MindMaps

Informationarchitects.jp presents the 200 most successful websites on the web, ordered by category, proximity, success, popularity and perspective in a mindmap. Apparently, web-sites are connected as they’ve never been before.

trends in information visualization technology spectrum
Trends In Information Visualization – Technology Spectrum

JAVA

Microsoft .Net

E.g. WPF, Silverlight

Animated

IOS4

MOSS Excel Services

Adobe Flash/Flex

Data Management &

Statistical Packages

SQL Reporting Services

Custom RIA Dev

E.g. ECB’s FlexCB

MS Office & Add-Ons

E.g. Excel & IMF Map tool

HTML 5

Level of Interaction

BI & Data Viz Tools

E.g. Xcelsius, Tableau

Dynamic

Web Design Tools

E.g. Adobe CS

Publishing Software

E.g. Adobe InDesign

Authoring Tools

E.g. Freelance

Static

Paper

Web

Desktop

Physical Media

information visualization challenges
Information Visualization: Challenges
  • Handling complex and heterogeneous data formats and data sets
  • Combining available techniques for specific tasks in a canonic way for clustering, filtering, dimensionality reduction.
  • How to effectively present more than three dimensions of information in a visual two dimensional graphical display
  • How to effectively visualize data that is changing rapidly (as fast as several hundred thousand times per second)
information visualization where to start
Information Visualization: Where to start?
  • Define the Business Purpose First
  • Know Your Audience
  • Invest in Data Quality
  • Explore Several Platforms
  • Assemble Specialized Team with Expertise in User Experience
references
References
  • Deloitte Technology Trends 2011
  • http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/
  • http://download.intel.com/pressroom/kits/events/idffall_2009/pdfs/2009_IDF_Otellini.pdf

Analytics and Life Sciences