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Dan Mallinger – Data Science Practice Manager, Think Big Analytics at MLconf ATL
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Transcript of Dan Mallinger – Data Science Practice Manager, Think Big Analytics at MLconf ATL
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Organizing for Data Science
Dan Mallinger Data Science Practice Manager
September 2014
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• Data Science Practice Manager − Think Big Analytics
• Working with clients across − Financial Services − Advertising − Manufacturing − Social − Network Providers
Dan Mallinger
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• Define Data Science in the Organization • Look at Current Perspectives on Organization • Discuss Shortcomings • Review a Real World Solution
Today
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� Use Data to Improve Our Business
� Better Understand Customers � Act Proactively, Not Reactively
What Do We Hope to Do?
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� Scale � Robustness � Repeatability
Why Organize?
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� Revolutionizing Ad Targeting � Automating Deals and
Recommendations � Alerting Admins to New Network
Attacks
Perception: What Does Data Science Do?
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� Specific Data Expertise � Exploratory Analysis � Modeling � Creativity � Programming � Big Data � Communication
� Ability to Target Impact � Unstructured Analysis
� Organizational Politics
� Visualization
� …
What Does It Take?
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� Centralized - Brings data, analysis, and
processing together - Data scientists support one
another � Distributed
- Data scientists close to business - Multiple models for rotating
data scientists into lines of business
The New Toy: A Center of Excellence
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CoE
Line of Business A
Line of Business B
Line of Business C
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� Specific Data Expertise � Exploratory Analysis � Modeling � Creativity � Programming � Big Data � Communication
� Ability to Target Impact � Unstructured Analysis
� Organizational Politics
� Visualization
� …
What Does It Still Take?
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� Designed a great home for unicorns � But they are still unicorns
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If You Build It, They Will Come?
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� Unravel Capability � Map Activities to Functional Roles � Align Functions with Process,
Not Individuals
� Don’t Forget to Scale
Working with Horses, Not Unicorns
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� Identify Fraudulent Sessions � Cross Channel Analysis � Next Best Action � Optimize Pathways � Determine Session Interest � Customizing Experience � Proactive Outreach � Search Analysis
� Content Optimization
CLIENT EXAMPLE Clickstream Data in Action
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� Billions of clicks � Unstructured data � How do we model it?! � Model the SIGNAL � Not the data
CLIENT EXAMPLE Scaling Data Science
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CLIENT EXAMPLE Clickstream Data Science in Action
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Hadoop 1.0
MPP Web
Feature Selection & Dimensionality Reduction
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� Feature Selection - Forests - Clustering
� Dimensionality Reduction - SVM
� Challenges - Job Latency - Limited Iterations
CLIENT EXAMPLE Extracting Signal: Hadoop 1.0
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• 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
CLIENT EXAMPLE Extracting Signal: Hadoop 2.0
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� Focus on Technical Skills - EDA - Modeling - Programming / Big Data
� Communication Skills - Capturing signal needs - Iterating with stakeholders
CLIENT EXAMPLE Horses, Not Unicorns
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Hadoop 1.0
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• 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
CLIENT EXAMPLE CoE Next Steps
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Discussion Over Drinks
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