This talk will introduce a paradigm for enabling access to large, unstructured, and novel datasets in enterprises, while retaining value from existing tools and staff. By following a real world example, the discussion will walk through how small, central data science teams can make data discoveries and data value accessible to others. We will also review the tools, data science approaches, and best practices to uncovering, polishing, and digesting signal in data to support analytics at the front lines of business.

1. Organizing for Data Science
Dan Mallinger
Data Science Practice Manager
September 2014

2. CONFIDENTIAL | Dan Mallinger
• Data Science Practice Manager
− Think Big Analytics
• Working with clients across
− Financial Services
− Advertising
− Manufacturing
− Social
− Network Providers
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3. CONFIDENTIAL | Today
• Define Data Science in the Organization
• Look at Current Perspectives on Organization
• Discuss Shortcomings
• Review a Real World Solution
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4. Ÿ Use Data to Improve Our
Business
Ÿ Better Understand Customers
Ÿ Act Proactively, Not Reactively
CONFIDENTIAL | What Do We Hope to Do?
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5. CONFIDENTIAL | Ÿ Scale
Ÿ Robustness
Ÿ Repeatability
Why Organize?
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6. Ÿ Revolutionizing Ad Targeting
Ÿ Automating Deals and
Recommendations
Ÿ Alerting Admins to New Network
Attacks
CONFIDENTIAL | Perception: What Does Data Science Do?
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7. CONFIDENTIAL | Ÿ Specific Data Expertise
Ÿ Exploratory Analysis
Ÿ Modeling
Ÿ Creativity
Ÿ Programming
Ÿ Big Data
Ÿ Communication
Ÿ Ability to Target Impact
Ÿ Unstructured Analysis
Ÿ Organizational Politics
Ÿ Visualization
Ÿ …
What Does It Take?
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8. CONFIDENTIAL | The New Toy: A Center of Excellence
Ÿ Centralized
- Brings data, analysis, and
processing together
- Data scientists support one
another
Ÿ Distributed
- Data scientists close to
business
- Multiple models for rotating
data scientists into lines of
business
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Line of
Business A
CoE
Line of
Business B
Line of
Business C

9. CONFIDENTIAL | Ÿ Specific Data Expertise
Ÿ Exploratory Analysis
Ÿ Modeling
Ÿ Creativity
Ÿ Programming
Ÿ Big Data
Ÿ Communication
Ÿ Ability to Target Impact
Ÿ Unstructured Analysis
Ÿ Organizational Politics
Ÿ Visualization
Ÿ …
What Does It Still Take?
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10. CONFIDENTIAL | Ÿ Designed a great home for unicorns
Ÿ But they are still unicorns
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If You Build It, They Will Come?

11. Ÿ Unravel Capability
Ÿ Map Activities to Functional Roles
Ÿ Align Functions with Process,
Not Individuals
Ÿ Don’t Forget to Scale
CONFIDENTIAL | Working with Horses, Not Unicorns
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12. Ÿ Identify Fraudulent Sessions
Ÿ Cross Channel Analysis
Ÿ Next Best Action
Ÿ Optimize Pathways
Ÿ Determine Session Interest
Ÿ Customizing Experience
Ÿ Proactive Outreach
Ÿ Search Analysis
Ÿ Content Optimization
CONFIDENTIAL | CLIENT EXAMPLE
Clickstream Data in Action
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13. Ÿ Billions of clicks
Ÿ Unstructured data
Ÿ How do we model it?!
CONFIDENTIAL | Ÿ Model the SIGNAL
Ÿ Not the data
CLIENT EXAMPLE
Scaling Data Science
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14. MPP Web
CONFIDENTIAL | CLIENT EXAMPLE
Clickstream Data Science in Action
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Hadoop 1.0
Feature Selection &
Dimensionality Reduction

15. CONFIDENTIAL | Ÿ Feature Selection
- Forests
- Clustering
Ÿ Dimensionality Reduction
- SVM
Ÿ Challenges
- Job Latency
- Limited Iterations
CLIENT EXAMPLE
Extracting Signal: Hadoop 1.0
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16. CONFIDENTIAL | CLIENT EXAMPLE
Extracting Signal: Hadoop 2.0
• Spark
− Faster response in exploration
− Better Support for Iterative Models
• Genetic Algorithms
• Neural Networks
• Challenges
− In memory: costly and limiting
− MapReduce does not go away
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17. Ÿ Focus on Technical Skills
- EDA
- Modeling
- Programming / Big Data
Ÿ Communication Skills
- Capturing signal needs
- Iterating with stakeholders
CONFIDENTIAL | CLIENT EXAMPLE
Horses, Not Unicorns
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Hadoop 1.0

18. CONFIDENTIAL | CLIENT EXAMPLE
CoE Next Steps
• Continue to make signal available to analysts
− Next up: Extracting signal from text
• Act as a capability search party
− Sprints of new insights and tools
• Finalize operating model
− Funding structure
− Engagement model with lines of business
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19. CONFIDENTIAL | Discussion Over Drinks
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