The document discusses how CareCore National evolved to utilize data-driven transformations, highlighting EMC's analytics platforms, tools, and services that can assist organizations in building their data science capabilities and teams to leverage big data and drive business value through predictive analytics and data mining. It also outlines the key components needed for a successful analytics transformation, including establishing a clear vision, understanding platform dependencies, embracing unified analytics platforms, building data science skills, and delivering initial wins to socialize analytics.

1. Disruptive Data
Science
Annika Jimenez
David Dietrich
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2. Agenda
 CareCore National’s Evolution
 Data-Driven Transformations
 EMC’s Assist
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3. CareCore’s Role in Health Care
• Optimized Compensation
• Legal Actions
• Accurate Payments Insurance
Companies
(Carriers)
• Providers’ Performance
• Optimized Price
• Fraud Detection
• Legal Action
• Reduced Liability
• Customized Plans
• Claims • Sources of Variability
• Better Services
• Diagnosis • Industry Statistics
• Treatments
• Procedures
• Employees Analysis • Legal Action
Clinics, • Cost
• Industry’s Statistics • Optimal Site
Hospitals, • Patient’s Profiles
• Potential of Savings Redirection
Plan
HC • Improved Benefits
Customers
Centers, and Value
• Cost-Effective
Providers • Better Services
Treatment
• Knowledge
Dissemination
• Reduced Liability • Industry’s Statistics
• Clinic’s Performance • Legal Action
• Sources of Variability
• Providers’ Performance • Improved Employee’s
• Industries Statistics
Satisfaction
• Providers’ Performance
• Better prices and options
Patients
• Better Selection of Providers (Members)
• Better Selection of Clinics
• Improved Expenses Planning
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4. Pathway Traversal Prediction
Patient
Provider
Patient CPT
Pathway Traversals
Claims Profile Plan
History
Prior
Provider
Pathway
Profile
Traversals
Predicted
Actual
Plan
Details
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5. Predictive Modeling In the Call Center
Actual traversal
Traversal anomaly:
Triage the call
95% CI
Predicted traversal
Patient
profile
Plan
details
Provider
profile
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6. CareCore National Analytics Phases
Customer = CCN Customer = External
3.0 AaaS @ Scale
1.5 CCN Analytics
• Data Exchange
(BI + Predictive)
• Workflow Management
2.0 Analytics as a Service
Time
• New Customer Orientation
• New Solutions Development
1.0 CCN Analytics • Data Source = Carrier+
• 2.0 Batch 2.5 Data Exchange
• Greenplum Platform
• Data Source = CCN+
• BI-Centric
• Operationally Oriented
• Customer is CCN
Data Value
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7. Big Data
Key Characteristics Implications for the Enterprise
• Large Volumes • New Platforms
• New Sources • New Roles
• Low Latencies • New Techniques
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8. Big Data Requires New Approaches
to Analytics
Data Science & Big Data Analytics Predictive Analytics & Data Mining
(Data Science)
Typical • Optimization, predictive modeling,
Techniques forecasting, statistical analysis
& Data • Structured/unstructured data, many
Types types of sources, very large data sets
High Common • What if…..?
Questions • What’s the optimal scenario for our
business ?
• What will happen next? What if these
trends continue? Why is this
happening?
Data
Science Business Intelligence
BUSINESS Typical • Standard and ad hoc reporting,
Techniques & dashboards, alerts, queries, details
VALUE Data Types on demand
Business • Structured data, traditional sources,
manageable data sets
Intelligence
Common • What happened last quarter?
Questions • How many did we sell?
• Where is the problem? In which
situations?
Low
Past TIME Future
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9. Industries Are Broadly Embracing Data Science
Retail Advertising & Public Relations
•CRM – Customer Scoring •Demand Signaling
•Store Siting and Layout •Ad Targeting
•Fraud Detection / Prevention •Sentiment Analysis
•Supply Chain Optimization •Customer Acquisition
Financial Services Media & Telecommunications
•Algorithmic Trading •Network Optimization
•Risk Analysis •Customer Scoring
•Fraud Detection •Churn Prevention
•Portfolio Analysis •Fraud Prevention
Manufacturing Energy
•Product Research •Smart Grid
•Engineering Analytics •Exploration
•Process & Quality Analysis
•Distribution Optimization
Government Healthcare & Life Sciences
•Market Governance •Pharmaco-Genomics
•Counter-Terrorism •Bio-Informatics
•Econometrics •Pharmaceutical Research
•Health Informatics •Clinical Outcomes Research
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10. Data-Driven
Transformations
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11. Transformation Catalysts
Process
& Change Mgt
Org
Alignment
DS Skill
Availability
Analytics
Tools &
Platform
Distributed
Computing
Data
Availability
Time
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12. Transformation Catalysts
Process
& Change Mgt
Org
Alignment
DS Skill
Availability
Analytics
Tools &
Platform
Distributed
Computing
Data
Availability
Time
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13. Big Data Analytics Is Now
Central to Enterprise Strategy
• Unauth. User Access
Detection
• Web Server Attack
Detection
• Malware Protection
• Advanced Persistent
Threat Detection
• KPI Definition
• Online Behavioral • Risk Modeling
Analyses: • Compliance
• Other product-sourced
data & behavioral CSO
analytics
o Mobile devices CFO
o DVRs • IT Log Analytics
o Smart meters
CPO • Error/event Logs
CIO
o Other electronics Analytics
Big Data
• Vertically Specific • Network Analytics
Product Development
o Genetic mining
o Imaging
CMO
Analytics COO
o Oil/gas exploration
• Unified User Profiling
• Segmentation
• Churn Prediction • Fraud Detection
• Lifecycle Management • Error Log Analysis
• Purchase Funnel Analysis • Complaint Data Analysis
• Brand Analytics • Call Center Data Analysis
• Campaign Analytics • Demand
o Media Mix Modeling Planning/Forecasting
CEO
o SEM Optimization • Quality/ Reliability
o Behavioral/Social Analysis
Targeting • Fault/Service Failure
o Attribution (Detection/Prediction)
o ROI Optimization
o Social Effects
• Pricing
• Demand Forecasting
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14. Transformation Catalysts
Process
& Change Mgt
Org
Alignment
DS Skill
Availability
Analytics
Tools &
Platform
Distributed
Computing
Data
Availability
Time
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15. Big Data Analytics Is Different
from Traditional BI
“TRADITIONAL BI”
Repetitive
“BIG DATA ANALYTICS”
Experimental, Ad Hoc Structured
Mostly Semi-Structured Operational
External + Operational GBs to 10s of TBs
10s of TB to Pb’s
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16. Data Science Team as
Your New Source of Innovation
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17. Traditional Analytics Process
Time-to-Insights
Data Prep sample.csv spec.docx DB Extract scores.csv DB Import
Not a Scalable Process!
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18. Analytics with Greenplum
Marketing optimization with MADlib
> SELECT householdID, variables
FROM households
ORDER BY RANDOM()
Time-to-Insights LIMIT 100000;
> SELECT run_univariate_analysis (
'households_training',
'variables');
WHERE pvalue<.01 AND r2>.01;
> SELECT run_regression(
Data Prep MADlib
'univariate_results',
'households_training');
> SELECT householdID,
madlib.array_dot(
coef::REAL[],
xmatrix::REAL[])
FROM coefficients, households;
© Copyright 2012 EMC Corporation. All rights reserved. 18

19. People & Skills
Three Key Roles of the New Data Ecosystem
Role
Data Scientists
Deep Analytical Talent Projected U.S. talent
gap: 140,000 to 190,000
Projected U.S. talent
Data Savvy Professionals gap: 1.5 million
Technology & Data Enablers
Note: Figures above reflect a projected talent gap in US in 2018, as shown in McKinsey May 2011 article Big Data: The next frontier for
innovation, competition, and productivity
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20. Profile of a Data Scientist
Quantitative
Curious &
Technical Creative
Skeptical Communicative
& Collaborative
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21. Specific Data Science Skills & Traits
1
2
3
EDW
4
5 Apply data science methods in their current roles
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22. The Greenplum Data Science Team
 Senior Director, Data & Insights Services,  Data Mining in Healthcare
Yahoo! (MIA, UCSD) (Ph.D. and Postdoctoral Fellow, Australian
National University)
 Principle Scientist at RSA, Fraud
Detection, Speech and Language • Biomedical Informatics (Ph.D., Stanford)
Processing • Research Engineer at Fox Interactive
(M.S. in Signal Processing) Media, eHarmony (M.S. in Applied
 Marketing optimization Mathematics)
(Ph.D. in Operations Research) • Quantitative modeling and risk
management in trading and finance
 Stochastic machine learning
(Ph.D. in Economics, Princeton, M.S. in
(Ph.D., Australian National University)
Mathematics, Courant Institute)
 Director of Analytics at M-Factor, • Mechanical Engineering (Ph.D., Stanford)
DemandTec (M.S., Berkeley)
• Statistician, Bayesian Analysis
(M.S., Statistics, Stanford)
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23. Biggest Obstacles to
Data Science Adoption
Q25 : The biggest obstacle to Data Science adoption in our organization is: (Coded for Total)
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24. The Question of Org
A B
Central Decentralized
Model Model
• Data Science sits within IT • Data Science sits with LOB
• Close proximity to data platform • Distanced from data platform
• Cross-initiative scalability • Limited scalability across org
• Establishment of best practices • Lack of best practices
• Jump-start ―data driven‖ culture • Slower ―data driven‖ culture
• Centrally defined prioritization • LOB defined prioritization
• Lack of domain expertise • Strong domain expertise
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25. The Question of Org
A B
Central Hybrid Decentralized
Model Model Model
• Data Science in IT with physical
LOB placement
• Close proximity to data platform
• Cross-initiative scalability
• Establishment of best practices
• Jump-start ―data driven‖ culture
• LOB driven prioritization
• Strong domain expertise
© Copyright 2012 EMC Corporation. All rights reserved. 25

26. Process & Change Management
Translate Vision
into Concrete
Projects
Explore Project
the Workspaces with
Prioritize
Deliver Wins!
Data Stakeholders
Collaboration
Publish
Socialize Roadmap and
Data
and
Progress Resource
Analysis
Iterate
Solidify
Integration
Points with
Dependencies
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27. New Roles Required Beyond Core
DS Team
+ Analytics Executive
– ―Chief Analytics Officer‖?
+ Engagement Managers
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28. EMC’s Assist
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29. Greenplum Unified Analytic Platform
 Agile Big Data Analytics
 Rich Capabilities for Complex
Analytics
 Extends Leading Tools
 Fully Customizable for
Analytics
 Increases Analytical
Productivity & Results
 Extends Insight By Combining
All Data
 Augmented by the Greenplum
Data Science Team
 Developed, Packaged,
Supported by EMC
© Copyright 2012 EMC Corporation. All rights reserved. 29

30. GREENPLUM ANALYTICS LABS
Analytics Solutions: Goals
 Overcome the analytics gap
 Generate continuous insights by
developing re-usable models on massive
data sets
 Produce actionable, ready-to-deploy
models
 Build collaborative relationships among
data stakeholders
 Educate users on the development of
tools and best practices
 Establish a strategic vision for on-going
analytics development
© Copyright 2012 EMC Corporation. All rights reserved. 30

31. GREENPLUM ANALYTICS LABS
Analytics Lab: Packages
LAB PRIMER LAB 100 LAB 600 LAB 1200
(1-Day Workshop) (Analytics Bundle) (6-Week Lab) (12-WEEK LAB)
• Analytics Roadmap • On-site MPP • Analytics • Analytics
Analytics Roadmap Roadmap
• Prioritized Training
Opportunities • Prof. services • Prof. services
• Analytics tool-kit on GPDB* on GPDB*
• Architectural
Recommendations • Quick insight • Ready-to-deploy • Ready-to-deploy
(2 weeks) model(s) model(s)
*GPDB priced separately
© Copyright 2012 EMC Corporation. All rights reserved. 31

32. Skills Matrix, Based on Recent
Students
Quantitative Analysts,
Statisticians, Data
Business and data Scientists
analysts
Quantitative
Skills
Business
Recent STEM
Intelligence
Grads
Professionals, IT
Technical Ability
© Copyright 2012 EMC Corporation. All rights reserved. 32

33. Data Science and Big Data Analytics
Course and EMCDSA Certification
Course Overview Details
• ―Open‖ curriculum
• Practitioner’s approach
• Enables immediate
participation on analytics
projects
• Prepares for EMC Proven
Professional Data Science
Associate (EMCDSA)
Certification
© Copyright 2012 EMC Corporation. All rights reserved. 33

34. Summary –
Transformation Success Criteria
 Establish a clear vision for the role of
Big Data Analytics
 Understand end-to-end platform dependencies
 Embrace the UAP paradigm
 Educate & build your Data Science Dream Team
 Organize to your contextual reality
 Initiate smart process
 Deliver one concrete ―win‖
 Socialize, socialize, socialize
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35. Q&A
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36. Other Relevant Greenplum Sessions
Session Presenter Times
Unified Analytics Platform Introduction Brian Wilson Tues 10:00-11:00 Thurs 1:00-2:00
Greenplum Database Overview Michael Crutcher Mon 8:30-9:30 Wed 10:00-11:00
Greenplum Hadoop Overview Susheel Kaushik Mon 10:00-11:00 Wed 4:15-5:15
Greenplum DCA Overview Hanxi Chen Mon 4:00-5:00 Thurs 10:00-11:00
Greenplum Analytics Workbench Apurva Desai Wed 8:30-9:30 Thurs 10:00-11:00
Analytics on Hadoop Don Miner Tues 11:30-12:30 Thurs 8:30-9:30
Optimizing Greenplum Database on VMware Kevin O’Leary Mon 4:00-5:00 Tues 4:15-5:15
Virtualized Infrastructure
Big Data Driven Businesses in Action: Mike Maxey Wed 4:15-5:15 Thurs 11:30-12:30
Creating Real Business Value Using
Greenplum UAP (Panel w/4 Customers)
Analytics for Business Value: Collaboration Josh Klahr Mon 10:00-11:00 Wed 2:45-3:45
Disruptive Data Science — How Data Annika Jimenez Tues 4:15-5:15 Thurs 11:30-12:30
Science and Big Data are Transforming David Dietrich
Business, IT and People
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37. Thank You
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