From the Predictive Analytics Innovation Summit Video here: https://www.youtube.com/watch?v=PdKUt0zK0UY With the avalanche of data about operations, customers, and products, leading companies are utilizing Big Analytics to better understand historical patterns and predict what may come next to create sustained competitive advantage. Dan Mallinger, who leads Think Big Analytic's data science team, will focus on practical examples of where companies are implementing new analytics approaches over big data. Dan will discuss how these efforts differ from traditional analytic approaches, the organizational and business impact, and how our clients are creating new value in areas such as marketing, services, sales and product development.

1. November 2013
BIG ANALYTICS
THE GOOD & THE VALUE

2. About Think Big Analytics
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Formed in 2010 to help clients launch and scale-out Big Data solutions
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Services include Big Data strategy, training, engineering and data science
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Management Background: Quantcast, Cambridge Technology, Oracle, Sun
Microsystems, Accenture
Blue chip clients, including:
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Internet Transactions Security Global #1
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Retail 2 of Global Top 5
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Banking 4 of Global Top 1; Financial Services 2 of Global Top 5
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Asset Management Global #1
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Disk Manufacturing Global #1
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Social Networking Global #1
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3. Think Big Integrated Value
Integrated Value
Advisory
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Understand true
business needs
Evaluate suitability of
new technologies
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Provide perspective on
market ideas
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Ensure engineering
and analytics support
business goals
Help establish realistic
and attainable
objectives
Drive client-specific
innovation
Implement
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Understand technology
preferences and
limitations
Assess talent skills and
development needs
Develop deep knowledge
of the data and tools
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4. Big Analytics
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New data
Yielding new opportunities
Enabled by new approaches
With supporting organization
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5. New Data
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6. Nontraditional Formats
Ÿ Unstructured data != text
- Call logs
- Raw video
- Satellite photos
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7. Exhaust
Ÿ Byproduct data
Ÿ Driving interest in the Internet
of Things
Ÿ Our machines tell a story about
us
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8. Data about Data
Ÿ Data usage patterns
Ÿ Driving next generation
organizations
- Data access patterns as KPI
- Systems access as employee
engagement
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9. New Opportunity
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10. Fingerprinting
Ÿ Unintentional patterns define us
- ATM rhythm
- Botnet synchronization
Ÿ More connected world exposes
more fingerprints
- Mobile installs and settings +
NFC
- Sensory data at shopping mall
displays
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11. Dark Data Insights
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It’s back from the dead!
Audit data
Fund manager predictions
Employee logs
Architectural records
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12. New Approaches
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13. Unstructured Analysis
Ÿ Non-traditional structures
- Path models
- High dimensionality
Ÿ Text
- POS
- Classification
Ÿ Images
- Object recognition
- Time differentials
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14. Deep Learning
Ÿ MapReduce built for
- Bootstrapped models
- Partitioning data by complex
logic
Ÿ Backpropagation is hard
Ÿ Feature learning isn’t (always)
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15. Challenges Incorporating Data
Science
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16. Organizational Integration
Ÿ Traditionally under engineering
Ÿ Integrated with data creators,
not data consumers
Ÿ Disconnected from business
priorities
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17. Success Loops
Ÿ We take BI for granted
- Analysts find novel patterns
- Business sees new trends
- Statistics is balanced by
domain knowledge
- Integration of actors aware of
feasibility, cost, and impact
Ÿ Where does your data scientist
sit?
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18. Successful Incorporation of Data
Science
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19. Partnership
Ÿ Business is a partner, not a
customer
Ÿ New insights, capabilities, and
products are not born in a
vacuum
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20. Cross Functional Teams
Ÿ Data science is a process, not
a job role
- Engineering
- Research
- Statistics
- Business
- Salesmanship
Ÿ Successful Big Analytics blends
skills, perspectives, and pushes
boundaries
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21. Measurement
Ÿ Requires KPI/KRI
Ÿ Performance metrics
- Direct actions
- Create purpose
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22. Client Success
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23. Example Client Phase 1
Ÿ First phase: Big Analytics
execution
Ÿ New methods of Botnet
detection
Ÿ Led to patent
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24. Example Client Phase 2
Ÿ Further analysis
- Improvement of Botnet models
Ÿ Expansion of cross functional
Big Analytic team
- Tool selection
- Training
- Early win identification
- Self-selected group
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25. Example Client Phase 3
Ÿ Cross-Functional Analytic
Organization
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Governance
Ownership and accountability
Process
Roadmap
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26. Questions?
www.thinkbiganalytics.com
www.linkedin.com/in/danmallinger
@danmallinger
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