2013 10 cu leeds school big data conference - bill jacobs - revolution analytics
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Big Data Analytics Examples Bill Jacobs Revolution Analytics Presented at: CU Leeds School of Business Analytics Conference - September 2013TRANSCRIPT
Big Data Analytics: Lessons From The PioneersRecommendations For New Leaders
CU Leeds School of Business Analytics Conference September 2013Boulder, Colorado#LeedsAnalytics
Bill JacobsDirector, Product Marketing - Revolution Analytics@bill_jacobs
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My Talk Today: Big Data and Big Analytics War Stories: Good, Bad and Ugly Lessons and Recommendations To Consider
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Direct outgrowth of Business Intelligence
First generation predictive analytics
Confidential to Revolution Analytics
What is Big Data?
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Volume Variety Velocity
Confidential to Revolution Analytics
What is Big Data?
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Big Data is big.Data set so large it cannot be managed in conventional database with acceptable performance and at acceptable cost.
Volume
Confidential to Revolution Analytics
What is Big Data?
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70-90% of all data generated lacks predefined structure or is difficult to map into a conventional data model.
Big Data is messy.
Variety
Confidential to Revolution Analytics
What is Big Data?
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Big Data moves.ICU: predict patient eventsFICO: flag suspect transactions
Velocity
Oreo: Superbowl ad from TweetsRetail: push in-store offers
Confidential to Revolution Analytics 9
+ Computing Power + Data + Pace of Business+ Customer Expectations
Big Data meets Big Math = New Business Outcomes
+Data Science+Computer Science +Management Science
Better Business Decisions
New Business Outcomes
THE PERFECT STORM
Big Data
2nd Generation Predictive Analytics
Machine Learning
Quick to Fail / Experimentation
Continuous Model Improvement = Value
Real Time / Nr Real Time
Second generation predictive analytics
Confidential to Revolution Analytics
Big Data vs. Big Data Analytics
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Volume
Variety
Velocity
The More Important V’s:
Assuring Veracity while delivering Value,
and embracing of Volatility.
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Typical Challenges Facing Analytical Organizations
Big Data• New Data
Sources• Data Variety
& Velocity• Data
Movement, Memory Limits
Complex Computation • Innovative
Models• Experiments• Many Small
Models• Ensemble
Methods• Simulation
Enterprise Readiness• Many platform
choices• Production
Support• Deploy to
Business Users
Speed & Production Efficiency• Model Life• Many Models• Long Cycle
Time• Faster
Decisions• Big Hardware
Talent• Finding data
scientists• Training• Creating an
Analytical culture
Confidential to Revolution Analytics
Analytical Competitors of Tomorrow
Kaizen Process Excellence
Parts Optimization / Pricing
Warranty Analytics
Better Decisions
More Models More Quickly
Big Data & Big
Analytics
Supply Chain AnalyticsPredictive Asset Analytics
Sustainability Analytics Customer / Marketing Analytics
HR Analytics
14Machine Learning Algorithms in R
Tools: Incredible Visualization, Descriptive and Predictive Statistics, and Machine Learning
Confidential to Revolution Analytics
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Stories: The Bad, The Ugly and The Good The Ugly: Abuse. The Bad: Missteps and Missed Opportunities. The Good: Big Analytics Doing Good
The Ugly: Governmental Overreach Using Big Data
WikiLeaks, Edward Snowden, NSA…
And now the CBP:
Customs and Border Protection are Stopping and Searching Private Flights.
Aircraft interceptions & searches after the flights stopped in Colorado [where Pot has been legalized].
Was a law broken? Was an unreasonable search conducted? How were the flights selected?
Bigger Question: Was the Data Legally Obtained?
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The Ugly: Commercially - Even LinkedIn!
“”When users sign up for LinkedIn they are required to provide an external email address as their username and to setup a new password for their LinkedIn account. LinkedIn uses this information to hack into the user’s external email account and extract email addresses. LinkedIn is able to download these addresses without requesting the password for the external email accounts or obtaining user’ consent.”
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The Bad: Beware How You Use It.
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Stories: Big Analytics Doing Good in the World The Ugly. The Bad. The Good: Big Analytics Doing Good
– Kaiser and Vioxx
– Google Flu
– Medicare and the Big Insurers
– Jepessen and Cost Containment in Airline Operations
– NYC Building Inspectors Save First Responder Lives
– Netflix and My Movie Watching
– Identity Resolution & Healthcare Fraud
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The Bad: Becoming Better Sensors – Google Flu
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The Good: Addressing Drug Outcomes & Side Effects Retroactively Vioxx and Celebrex were both approved medications Kaiser Permanente Studied Outcomes for 1.4M Members Vioxx was proven to be linked with increased heart attacks
– 27,000 Heart Attacks over 4 years. Result: Vioxx Pulled from Market. Lives Saved.
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The Good: Center for Medicare 5 Star Program Incents Big Data Analysis To Huge Gains Improvement Incentives + Business Gains Projected to Equal CMS Incentives Pay Higher Rates for Programs with Higher Satisfaction
Ratings. Major Insurer Estimates $20B Revenue Improvement for a ½ Star
Increase.
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The Good: Making Air Travel More Cost Effective Jeppesen Tail Assignment Automated Aircraft Routing and Assignment Found $10M In First Analysis of One Airline’s Data
Optimize Aircraft Assignment: Fuel Costs Fuel Consumption Maintenance Needs Operational Profile Passenger Traffic
Additional Opportunities: Predictive Maintenance Speed vs. Cost Planning Regulatory Compliance Maintenance Period
Adjustments
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Stories: Big Analytics Doing Good in the World The Ugly. The Bad. The Good: Big Analytics Doing Good
– Vioxx, Celebrex in the Court of Kaiser Permanente
– Google Flu
– Medicare and the Big Insurers
– Jepessen and Cost Containment in Airline Operations
– NYC Building Inspectors Save First Responder Lives
– Netflix and My Movie Watching
– Identity Resolution & Healthcare Fraud
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LessonsBig Gets Bigger. New Data Sets, New Methods, New
Audiences
Today: Social Networks and Media, Tomorrow: Internet of Everything
No Business Is Immune Diverse Businesses Are Capitalizing
from Big Data Analytics
HR Has a Huge Challenge Talent Pool Governs Outcomes
Attraction, Cultivation and Retention of Once-Obscure Talents
Veracity Demands Vigilance; Volatility Demands Investment Stale Predictions Put Companies On
The Line
Humans Often Represent The Greatest Inertia
Regulation Trails Abuses NSA on FISA Wiretaps: “We Only
Collect Metadata”
Organizational Change Is Critical Build a Shared Big Data Culture
Adapt Business & IT Practices Accord
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Recommendations Change Your Culture Fail Fast; Learn From Failure Engage a Broader Audience
– Identify Profiles of Stakeholders & Adapt To Them
– Develop a Career Path For Prediction’s Stakeholders Treat Predictions as Products; Data Infrastructure As a Prediction Factory Life’s Too Short…
Thank youRevolution Analytics is the leading commercial provider of software and support for the popular open source R statistics language.
www.revolutionanalytics.com, 1.855.GET.REVO, Twitter: @RevolutionR
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