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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.
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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
