Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow. Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney

1. Mohan Sawhney
McCormick Tribune Professor of Technology
Kellogg School of Management
mohans@kellogg.northwestern.edu
Big Data Analytics 2012 - Chicago
June 28, 2012

2. What is Big Data?
What is the Big Deal?
How does Big Data link to business outcomes?
What are the use cases for Big Data?
What can we learn from the Big Data leaders?

3. NOW
SO WHAT?
WHAT?
WHAT?

4. Understanding Big Data
Relating Big Data to Business Advantage
Industry Use Cases for Big Data
Putting Big Data to Work for you

5. The technologies and practices of handling
structured and unstructured datasets so
large, diverse and dynamic that they cannot
be processed and analyzed with existing data
management systems.

6. Data moves from structured to unstructured
Sources of data proliferate
Real-time creates too much information
Quantity does not trump quality
Data becomes contextual based on roles,
processes, location, time, and relationships.

7. The “what” is shifting from “transaction
processing” to “interaction processing” with
social media services like Facebook, Twitter and
LinkedIn.
The “how” of computing is adapting from
desktop computers to context and location-
aware mobile devices.
The “where” is moving from on-premise
computing to cloud computing

8. Zettabytes
Volume
Semi-structured Streaming

10. E-Business
ERP Suite
Functional
Systems Extended
EDW Data
Warehouse
Data
Marts

11. Understanding Big Data
Relating Big Data to Business Advantage
Industry Use Cases for Big Data
Putting Big Data to Work for you

12. • Big Data is a response to the evolution of the
Social, Local and Mobile data-driven enterprise
that will be required to sense and respond in
“right-time” to events in its ecosystem.
• Big Data leads to business advantage through
faster, smarter and more cost-effective
decisions
• Big Data’s ultimate business outcome is Agility

13. • Smarter decision making comes from the ability
to combine new sources of data to enhance
existing analytics and predictive models in
operational systems and data warehouses.
• New insights emerge from synthesis of multi-
structured data from sensors, system and web
logs, social computing web sites, text
documents, etc. that are difficult to process
using traditional analytical processing
technologies.

14. Unstructured Data
Embedded CPUs
Quality
Embedded
Extended Part Failure
Sensors Data Warehouse Performance
Analysis
Structured Data
CRM Systems
Safety
Airbag data
Dealer Crash data
Systems
Product Design
Systems

15. Faster decisions are enabled because big data
solutions support the rapid analysis of high
volumes of detailed data.
Analysis at this scale is been difficult to date
because it takes too long or is too costly
Traditionally, enterprises have had to aggregate
or sample the detailed data before it can be
analyzed, which adds to data latency and
reduces value of the results.

16. Faster time to value is possible because
organizations can now process and analyze data
that is outside of the enterprise data
warehouse.
Enterprises can integrate large volumes of
machine-generated data from sensors and
system and web logs into the enterprise data
warehouse for analysis.

17. Function Big Data Application
Marketing • Cross-selling
• Location-based advertising
• In-store behavior analysis
• Customer micro-segmentation
• Sentiment analysis
• Attribution analysis
Merchandising • Assortment optimization
• Pricing optimization
• Placement and design optimization
Operations • Performance transparency
• Labor inputs optimization
Supply Chain • Inventory management
• Logistics optimization
New Business Models • Price comparison services
• Web-based markets
• Usage and location-based pricing

18. Analyze performance variation
Operations and Enable automated decision making
Finance Optimize operations
Detect and reduce fraud
Discover customer insights
Marketing and Predict customer behavior
Sales Optimize marketing campaign ROI
Fine-tune customer segmentation
Analyze product performance
Optimize product features
Product Develop personalized offerings
Development
Innovate business models

19. LinkedIn uses data from its more than 100 million users
to build new social products based on users’ own
definitions of their skill sets.
Silver Spring Networks deploys smart, two-way power
grids for its utility customers that allow homeowners to
send information back to utilities to help manage
energy use and maximize efficiency.
The Camden Coalition mapped the city’s crime trends
to identify problems with its healthcare system,
revealing services that were both medically ineffective
and expensive.

20. Insurance : Individualize auto-insurance policies based on vehicle telemetry data.
More accurate assessments of risks
Individualized pricing based on actual individual customer driving habits;
Influence and motivate individual customers to improve their driving habits
Travel: Optimize buying experience through web log and social
media analysis
Gain insight into customer preferences and desires;
Up-sell by correlating current sales with subsequent browsing behavior Increase
browse-to-buy conversions via customized offers and packages
Personalized travel recommendations based on social media data
Gaming: Collect gaming data to optimize spend within and
across games
Gain insight into likes, dislikes and relationships of its users
Enhance games to drive customer spend within games
Recommend content based on analysis of player connections and similar “likes”

21. Target analyzed its baby-shower registry to observe
changes in shopping habits changed as a woman
approached her due date.
Target analysts found interesting patterns. For instance,
women buy larger quantities of unscented lotion
around the beginning of their second trimester. In the
first 20 weeks, pregnant women buy supplements like
calcium, magnesium and zinc. They also buy hand
sanitizers and washcloths close to their due date.
Target identified 25 products that, when analyzed
together, allowed them to assign each shopper a
“pregnancy prediction” score and an estimated due
date. Target can target women at very specific stages of
a woman’s pregnancy.
Target can also optimize the purchase funnel from
emailed coupons to online buying and store visits.

22. Understanding Big Data
Relating Big Data to Business Advantage
Industry Use Cases for Big Data
Putting Big Data to Work for you

24. • A business use case describes what a
technology or product does. It describes the job
to be done by end-users to achieve their
business goals.
• The business use case describes a process that
provides business value to the end-user

25. Merchandizing and market basket analysis.
Campaign management and customer loyalty
programs.
Supply-chain management and analytics.
Event- and behavior-based targeting.
Market and consumer segmentations.

26. Customer Experience Optimization: Deliver consistent cross-
channel customer experiences; harvest customer leads from
sales, marketing, and other sources
Increase basket size: Increase average order size by
recommending complementary products based on predictive
analysis for cross-selling.
Cross-channel Analytics: Sales attribution, average order value,
lifetime value
Event Analytics: What series of steps (golden path) led to a
desired outcome (e.g., purchase, registration).
Next Best Offer: Deploy predictive models in combination
with recommendation engines that drive automated next best
offers and tailored interactions across multiple interaction
channels.

27. Compliance and regulatory reporting
Risk analysis and management
Fraud detection and security analytics
CRM and customer loyalty programs
Credit risk, scoring and analysis
High speed Arbitrage trading
Trade surveillance
Abnormal trading pattern analysis

28. Threat detection: Federal law enforcement
agencies monitor threat (or criminal) behaviors
and communications in order to raise
awareness of interdiction opportunities while
also exposing non-obvious relationships
between terrorist actors/agents
Infrastructure Threats: As utilities in the U.S.
add information technology to their grids, new
threats are emerging. Efficiency is also making
the grid even more vulnerable to security
concerns as the grid could be hacked

29. Understanding Big Data
Relating Big Data to Business Advantage
Industry Use Cases for Big Data
Putting Big Data to Work for you

30. What are the questions that need to be asked?
What are the answers that help us move from
data to decisions?
Can we shift insight into action?
How do we tie information to business process?
Who needs what information at what right
time?
How often should this information be updated,
delivered, and shared?

31. Educate:
Identify people who are both technically adroit and
analytically creative.
Combine business, analytical and technical expertise
Develop the team through training and certifications in Big
Data Analytics and Data Science.
Acquire:
Bring in individuals from outside your four walls and
outside your industry
Diversity ensures complementary skills and the ability to
challenge existing mental models
Empower
Challenge the team with creating measurable impact
Provide the team with support of senior management.
Protect the team when it runs into resistance

32. Big Data is characterized by volume, variety and
velocity
Big Data analytics “extends” the Data
Warehouse with new data types and new
analytics techniques
Big Data creates business advantage through
smarter, faster decisions and faster time to value
Big Data should be leveraged with a clear
understanding of business use cases
Big Data teams should combine creativity and
analytics