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

1

My Talk Today: Big Data and Big Analytics War Stories: Good, Bad and Ugly Lessons and Recommendations To Consider

3

Direct outgrowth of Business Intelligence

First generation predictive analytics

Confidential to Revolution Analytics

What is Big Data?

5

Volume Variety Velocity

Confidential to Revolution Analytics

What is Big Data?

6

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?

7

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?

8

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

11

Volume

Variety

Velocity

The More Important V’s:

Assuring Veracity while delivering Value,

and embracing of Volatility.

12

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

15

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?

17

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.”

18

The Bad: Beware How You Use It.

19

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

20

The Bad: Becoming Better Sensors – Google Flu

21

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.

22

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.

23

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

24

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

25

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

26

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

27