This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.

1. Big Data & Business Analytics:
Understanding the Marketspace
Prof. Bala Iyer
@BalaIyer
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2. Agenda
 Big Data Basics
 Understand why Big Data is
increasingly important to the business
 Ecosystem Analysis
 Key Recommendations
 Next Steps
2

3. ―we now uncover as much data in
48 hours – 1.8 zettabytes (that's
1,800,000,000,000,000,000,000
bytes) – as humans gathered from
"the dawn of civilization to the year
2003."
3

4. Trends
 Hyper-competition
 Customer expectations
 Big data around business
 Science Envy?
 Analytical organization
4

5. Environment
Decisions
Your
Data
Models
Decisions
5

6. Enter the Data Scientist
 A data scientist is an engineer who employs the
scientific method and applies data-discovery tools to
find new insights in data. The scientific method—the
formulation of a hypothesis, the testing, the careful
design of experiments, the verification by others—is
something they take from their knowledge of
statistics and their training in scientific disciplines.
Data Scientists: The Definition of Sexy, Forbes 2013 link
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7. Opportunities
 Shortage of data scientists
 Huge technical challenges
 India advantage
 Use cases emerging
According to Wikibon the market is expected
to reach USD53.4 billion in 2016
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8. Averages
Tier 1: $323MM
Tier 2: $26 MM
8

9. Categories
 People
 Machine
 Social
 Transactional
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10. Offerings
 Consulting
 Implementation/change
 Technology Platforms
 Methodologies and Frameworks
 Assessment
 CoE, Incubators, Labs, Co-innovation
networks
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11. Problems
 Structured
 Semi-structured
 Un-structured
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12. Skills
 Domain knowledge
 Math
 Statistics
 Heuristics
 Machine learning
 Programming
 Scientific method
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13. Technology Impact on Client Business
Impact Framework by Prof. Venkatraman
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14. What do we mean by
―Analytical‖?
 Analytical Decision-making: the use of
data, analysis, models & systematic
reasoning to make decisions
 Questions to answer:



What decisions or business areas should
analytics be applied?
What kind of data do we have now & do we
need?
What kinds of analysis do we do?
Source: ―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from
Analytics at Work: Smarter Decisions, Better Results, 2010.
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15. Big Data, Predictive Analytics
 Software and/or hardware solutions
that allow firms to improve business
performance or mitigate risk by
analyzing big data sources.
 Predictive analytics uses algorithms to
find patterns in data that might predict
similar outcomes
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16. Applied analytics
 Monitoring to help us track real time behaviors;
 Insight to help us understand what is going wrong;
 Prediction to show us what minor issue today is a
precursor to catastrophic failure tomorrow;
 Optimization to achieve operational targets for yield,
throughput, or energy consumption; and
 Machine learning to help us discover new trends or
patterns of failure or optimization
Analytics in each category requires different volumes
and velocities of data to be effective
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17. Competencies or Stack
Change Management
Insights
(Experimentation/Visualization)
Domain Knowledge
(best practices)
Model Building
(tools and techniques)
Infrastructure
(Data, Models/architecture)
T
O
O
L
S
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18. How are decisions made today?
 Intuition & Experience
 The HiPPOs (Highest-paid person’s opinion)1
 Outcomes?


Decisions that are not-optimized or ill-informed
We introduce biases, prejudice, unaided intuition, &
self-justification to the process2
Shift towards analytics: 44% of executives feel analytics
are strongly supporting or driving strategy3
Source: 1 ―Big Data: The Management Revolution, McAfee & Brynjolfsson, HBR, Oct. 2012.
2―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from Analytics at Work:
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Smarter Decisions, Better Results, 2010.
3―Preparing for Analytics 3.0‖, Davenport, CIO Journal in The Wall Street Journal, 2/20/2013.
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19. Traditional Analytics (Analytics
1.0)
Structured Data
• Focus on purchase transactions (retail & online)
• Product insights (rich): best-selling products, most profitable
• Customer insights (limited): loyalty programs & online
behaviors lead to segmentation, personalization
• Inputs are structured data & outputs are reports
Image Source: http://ecommercecenter.net/management/what-is-a-data-warehouse.html
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20. We’re now doing Analytics 2.0!
Multichannel
Social
Data Integration (unstructured + structured)
Location
Sensor
Image Source: http://dkidiscussion.blogspot.com/2012/03/were-facebook-friends.html; http://www.apple.com/ipod/nike/;
http://www.accontrols.com/ge-sensing.html; http://howto.cnet.com/8301-11310_39-20070819-285/7-fun-ways-to-interact-with-your20
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foursquare-account/; http://bigdata.pervasive.com/Products/Hadoop-Data-Integration.aspx;

21. Why is Big Data new?
 Volume: 2.5 exabytes of data created each day
 Velocity: real time or near real-time data provides agility
over competitors
 Variety: unstructured data (in addition to
structured/transactional) & external (as well as internal)
 Social
 Location-based/mobile
 Personal interests
 Sensor
 Video/audio
Value
Source:
―Big
Data: The Management Revolution, McAfee & Brynjolfsson, HBR, Oct. 2012.
21

22. What are the sources of data?








ERP/CRM Transactional Systems
Point-of-Sale/Scanner at Retail
Customer Loyalty Programs
Financial Transactions
Click-Stream Data
Social Data
Mobile
External Data Aggregators (e.g., AC Nielson)
22

23. Key Questions Addressed by Data
Analytics
Past
What happened?
Present
Future
What is happening
now?
(Alerts)
What will happen?
How and why did it
happen?
What’s the next best
action?
What’s the best/worst
than can happen?
(Modeling,
experimental design)
(recommendation)
(Prediction,
optimization,
simulation)
Information(Reporting)
Insight
(extrapolation)
Source: ―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from
Analytics at Work: Smarter Decisions, Better Results, 2010.
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23

24. Data analytics can focus on
different areas of the business
Business Area
Benefits
• More focused segmentation
• Understand customer behavior
Customer
• Etc.
Operations
Talent
Management
• Optimize inventory levels & delivery routes
• Build facilities in best locations
• Etc.
• Hire & retain employees based on right skillset
• Identify valuable skillsets by job role
• Etc.
Source: ―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from
Analytics at Work: Smarter Decisions, Better Results, 2010.
24
24

25. Target used data mining to predict buying habits
of customers going through major life events
 Target was able to identify 25 products (e.g., vitamin
supplements) that when analyzed together helped
determine a ―pregnancy prediction‖ score
 Sent baby-related promotions to women based on this
score
 Outcome:


Sales of Target’s Mom and Baby products sharply
increased soon after new advertising campaigns
Privacy concerns: Target had to adjust how it
communicated the new promotions
Source: ―How Companies Learn Your Secrets‖, Duhigg, The New York Times, Feb. 16, 2012.
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26. Whirlpool monitors social to
discover what their customers are
saying…
 Used Attensity360 to discover where online people were
discussing Whirlpool & what they were saying
 Used customer feedback for better product development,
planning & customer service
 Metrics:
 Size of the overall online appliance conversation
 Sentiment analysis by brand (Whirlpool & competitors)
 Time elapsed between complaint/comment &
contacting the customer
 # of customers contacted
 # of satisfied completed interactions
Source: IDC Customer Spotlight, Whirlpool Corporation’s Digital Detectives:
Attensity Provides the Lens, IDC 2011 report.
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27. Many industries using data analytics
for improving value disciplines
 General Electric using Big Data to optimize the service
contracts & maintenance1 The industrial internet.
 Netflix used Big Data to predict if a TV show will be
successful- ―House of Cards‖ series, Director & promotions2
 LinkedIn used Big Data to develop ―People You May Know‖
products – 30% higher click-thru-rates3
Source: 1―What’s Your Strategic Intent for Big Data?‖, Davenport , CIO Journal in The Wall Street Journal,
1/23/2013.
2‖The Future of Entertainment is Analytical‖, Davenport , CIO Journal in The Wall Street Journal, 3/6/2013.
Source: ―Data Scientist: The Sexiest Job of the 21st Century‖, Davenport & Patil, HBR, Oct 2012.
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28. How do companies build an
analytics capability?
 Meet the Data Scientist (need analytical +
social + communication skills)
 Bring structure to large unstructured data &
make analysis possible
 Help decision-makers shift from ad-hoc analysis
to ongoing conversations with data
 Suggest business direction implications
 Include process and technology
Source: ―Data Scientist: The Sexiest Job of the 21st Century‖, Davenport & Patil, HBR, Oct 2012.
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29. Create a culture of data-driven
decisions
 Leadership needs to set direction on how to make
decisions
 Shift mindset from What do we think? To What
do we know?
 Understand underlying models & its assumptions
 Domain subject-matter experts still critical: they
know what questions to ask
Source:
―Big
Data: The Management Revolution, McAfee & Brynjolfsson, HBR, Oct. 2012.
29

30. That are many analytics
tools/techniques
Technique
Description
Data Mining
• Extract new patterns in large data sets
Predictive
Modeling
• A model is created to best predict the probability of an
outcome
A/B Testing
• A control is compared to test groups.
• Often used in website design to test for higher conversion
rates
Textual Analysis
• Topics can be extracted along with their linkages
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31. So, should we all use data-driven
decisions?
 Performance Implications:

Companies in top of their industries with datadriven decisions were:
 More productive & profitable
 Had a higher stock market evaluation1

External business intelligence (e.g., analyzing social media
data) boosts innovation & profits2
Source: 1―Big Data: The Management Revolution, McAfee & Brynjolfsson, HBR, Oct. 2012.
2‖Survey: External Business Intelligence Boosts Innovation and Profits‖, Schectman, CIO Journal in The Wall Street Journal,
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2/26/2013.

32. Why not?





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No time – decision needs to be made now
Invalid/outdated assumptions
No precedent (lacking past data)
Historical data misleading
Use analytics to rationalize pre-determined decisions
Don’t have the capabilities!


Major shortage of data scientists or skills associated with
data analytics
Culture not suited (e.g., privacy, regulatory concerns)
Source: ―What it Means to Put Analytics to Work‖, Davenport, Harris & Morrison, chapter from
Analytics at Work: Smarter Decisions, Better Results, 2010.
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33. Ecosystem Analysis
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34. Analytics Ecosystem (840 nodes)
Component
Platform
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Platform with high brokerage

35. High brokerage nodes
Cloudera
Pentaho
IBM
Fractal
MuSigma
Rapidminer
SAS
Cognizant
MTECH
Accenture
Tableau
SPSS
Infosys
AbsolutData
Capgemini
Genpact
KXEN
Oracle
Wipro
Opera
TCS
HCL
LatentView
Guavus
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36. Potential Models
 Augmentation or spot services
 SaaS Decision Environment
 Consultant
 Strategic partner
 Digital thought leadership

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Training for data scientists
Smart Lab
CoE
 Infrastructure and libraries
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37. Key competencies
 Technical

Modeling
 Strategic
 Talent management
 Change management
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38. Client Recommendations
 Prepare the organization: to become analytics-driven.
 Understand the problem:.
 Sourcing intent:



complement existing initiatives
setting up an R&D or experimentation center,
creating your own centers for excellence on analytics
 Build absorptive capacity:
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39. Vendor recommendations

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Lab environments:
Decision styles and other biases
Location of Analytics organization
Data scientists
Model management Capabilities:
Connectors or boundary spanners
Order takers vs. scientists
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40. Overall
 The business analytics market is still at
its infancy. Like many other industries,
much is yet to be learned. As others
have opined, profitability and risk
mitigation awaits the fast learners.
40

41. Organization chart
 R&D
 Sales
 In-bound marketing
 Client-partners
 Connectors (SWAT teams)
 Data Scientists


Programmers
analysts
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42. Investments
 Training/Recruitment


Sales
Data Scientist
 Certification based on competency and project
experience


Techniques
Domain knowledge
 Product/platforms
 Visualization lab
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43. Risks
 Privacy and ethics of data
 ―Big brother‖
 New skills for production and selling
 Managing a pool of modelers
 Communication between modelers,
programmers and scientists
 Model management
 Installed base of engineers
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44. Questions?
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