November 2013

BIG ANALYTICS THE GOOD & THE VALUE

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About Think Big Analytics

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¨  Formed in 2010 to help clients launch and scale-out Big Data solutions

¨  Services include Big Data strategy, training, engineering and data science

¨  Management Background: Quantcast, Cambridge Technology, Oracle, Sun Microsystems, Accenture

¨  Blue chip clients, including:

Ø  Internet Transactions Security Global #1

Ø  Retail 2 of Global Top 5

Ø  Banking 4 of Global Top 1; Financial Services 2 of Global Top 5

Ø  Asset Management Global #1

Ø  Disk Manufacturing Global #1

Ø  Social Networking Global #1

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Think Big Integrated Value

Advisory Implement

¨  Understand true business needs

¨  Evaluate suitability of new technologies

¨  Provide perspective on market ideas

¨  Ensure engineering and analytics support business goals

¨  Help establish realistic and attainable objectives

¨  Drive client-specific innovation

¨  Understand technology preferences and limitations

¨  Assess talent skills and development needs

¨  Develop deep knowledge of the data and tools

Integrated Value

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�  New data �  Yielding new opportunities �  Enabled by new approaches �  With supporting organization

Big Analytics

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New Data

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�  Unstructured data != text - Call logs - Raw video - Satellite photos

Nontraditional Formats

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�  Byproduct data �  Driving interest in the Internet

of Things �  Our machines tell a story about

us

Exhaust

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�  Data usage patterns �  Driving next generation

organizations - Data access patterns as KPI - Systems access as employee

engagement

Data about Data

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New Opportunity

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�  Unintentional patterns define us - ATM rhythm - Botnet synchronization

�  More connected world exposes more fingerprints - Mobile installs and settings +

NFC - Sensory data at shopping mall

displays

Fingerprinting

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�  It’s back from the dead! �  Audit data �  Fund manager predictions �  Employee logs �  Architectural records

Dark Data Insights

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New Approaches

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�  Non-traditional structures - Path models - High dimensionality

�  Text - POS - Classification

�  Images - Object recognition - Time differentials

Unstructured Analysis

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�  MapReduce built for - Bootstrapped models - Partitioning data by complex

logic �  Backpropagation is hard �  Feature learning isn’t (always)

Deep Learning

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Challenges Incorporating Data Science

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�  Traditionally under engineering �  Integrated with data creators,

not data consumers �  Disconnected from business

priorities

Organizational Integration

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�  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 �  Where does your data scientist

sit?

Success Loops

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Successful Incorporation of Data Science

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�  Business is a partner, not a customer

�  New insights, capabilities, and products are not born in a vacuum

Partnership

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�  Data science is a process, not a job role - Engineering - Research - Statistics - Business - Salesmanship

�  Successful Big Analytics blends skills, perspectives, and pushes boundaries

Cross Functional Teams

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�  Requires KPI/KRI �  Performance metrics

- Direct actions - Create purpose

Measurement

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Client Success

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�  First phase: Big Analytics execution

�  New methods of Botnet detection

�  Led to patent

Example Client Phase 1

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�  Further analysis - Improvement of Botnet models

�  Expansion of cross functional Big Analytic team - Tool selection - Training - Early win identification - Self-selected group

Example Client Phase 2

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�  Cross-Functional Analytic Organization

�  Governance �  Ownership and accountability �  Process �  Roadmap

Example Client Phase 3

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Questions? www.thinkbiganalytics.com www.linkedin.com/in/danmallinger @danmallinger