Silly Bandz, Jibbitz, & Copernicus: Driving Innovation ... · Silly Bandz, Jibbitz, &...

Silly Bandz, Jibbitz, & Copernicus:

Driving Innovation Through Analytics

Bill Franks, Chief Analytics Officer, Global SAS Program, Teradata Kyle Cheek, Vice President, Emdeon April 2011

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Silly Bandz, Jibbitz, & Copernicus

Copernicus

Jibbitz

Silly Bandz

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The Rise Of Crocs & Jibbitz…

Crocs

Holes

Jibbitz

$20MM!!!!!

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Do You Have A Culture Of Innovation & Discovery?

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The Three Key Principles

On your journey to a culture of innovation and discovery you must:

1) Break Out Of Your Box

2) Ride The Ripple Effects

3) Align All Eyes On The Target

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Let’s Talk About Fleas…

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Principal #1: Break Out Of Your Box

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Copernicus…

Geocentric Heliocentric

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What Is The Center Of Your Solar System?

Tool Centric People Centric

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0``0``

Break Out Of Your Box With In-Database Analytics!

0

10

20

30

40

50

Time

Elapsed

In Database Out Of Database

40X, 50X, 100X Faster!!

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Break Out Of Your Box With In-Database Analytics!

Do You Want Your Team Driving This??? Or This???

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Principal #2: Ride The Ripple Effects

Innovation To The Power Of 2!

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Telephones & Phone Lines…

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The Internet Led To Ripple Effects…

“Now that I do know what Facebook is, I have to say it sounds like a HUGE waste of time!”

- Betty White on Saturday Night Live 2010

Social Networking

E-Commerce

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The Internet Led To Ripple Effects…

Geo-Spatial Capabilities

Massive Multi-Player

Games

What’s Next?????

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Ride Ripple Effects Of In-Database Analytics!

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Innovation: Making Sense of the Data Deluge

Web 2.0, healthcare, telco, other “digital” industries are driving an explosion in volumes of data

In recent memory, information scarcity posed the biggest challenge to decision-making

Today, optimal decision paths are a “needle in a haystack” problem as data volumes outpace the growth of analytic capacity

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Data Proliferation Over Time

0 C.E.

2003

40,000 BCE cave paintings

bone tools 3500 writing

paper 105 1450

printing 1870

electricity, telephone transistor 1947 computing 1950

1993 The Web

Digital Cardiology

Electronic Medical Record

E-Health Initiatives/Linkages

Digital Radiology

1999

Late 1960s Internet

P e

t a b

y t e

s

More Data Over the Last

3 Years Than Previous

40,000 years Combined

Digital Pathology

Genomics

Source: UC Berkeley, School of Information Management and Systems.

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How Much Digital Data Exists?

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Is There Such a Thing as Too Much Data?

Barriers to Innovation Posed by Massive Data Volumes

> Vertically massive data

Storage – Current data volumes outpace storage capacity

Can we analyze what we can’t store?

> Horizontally massive data (“Curse of Dimensionality”)

Horizontal data growth presents unique inference problems

What inputs are really related to which outputs?

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The Healthcare Data Conundrum

We have exponentially more data than ever before about medical experience, outcomes, gaps in care, and reimbursement

> But we are more susceptible than ever to lapses in care coordination, health care fraud, etc ..

This is true across all aspects of healthcare

> The clinical space and the revenue cycle space are equally impacted

> Many inefficiencies in healthcare are due to disintegrated data and its barrier to innovation

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The Healthcare Data Conundrum

Why Does Healthcare Data Pose a Barrier to Innovation?

> Massive volumes

> Not structured (most new medical data is unstructured)

> Not conformed

> Not consistent

= Not Very Easy to Exploit for improved Outcomes/Effectiveness/Efficiencies!!

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23

Summary of Current State

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There Are Paths Through the Data Deluge!

Data Warehousing

Cloud Computing

Advanced Analytics

In-Database Execution of Analytics

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Just What Are In-Database Analytics?

An analytics lexicon:

>Two main categories of “analysis”

Rational – what we can understand through reason alone

Empirical – what we an understand through observation

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Areas of Focus for In-Database Analytics

Database-driven analysis (empirical)

Heuristic

– Apply intuition to data

– Reports, OLAP cubes, etc

– Already pretty well established

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Areas of Focus for In-Database Analytics

Database-driven analysis (empirical)

Complex

– Adhere to a more rigid criteria for inference

– Traditional statistics, machine learning, etc

– *This* is the area of greatest innovative potential

» Deeper analysis

» More quickly and effectively deployed

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28

Why Bother?

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Principal #3: Align All Eyes On The Target

Set a common vision

Establish clear priorities

Tie to incentives

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Define A Common Vision…

Making A Wall? Or Building A House?

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Your Team Needs The Full Picture…

Looks Like We’ve Got A Cowboy! Or Do We???

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Your Choice Of Priorities Changes Your Outcome…

Highest Revenue

Highest Margins

Most Customers

Most Loyal Customers

Your Choice?

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Restaurant…

Free

Time ----->

Lo

yal

Cu

sto

mer

Co

un

t

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Compensation Impacts Action…

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Align All Eyes On The Target Of In-Database Analytics

Establish clear priorities for

how to get there

Set a common vision around making in-database analytics

happen

Tie pay to making it happen

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Review Of The Three Key Principles

On your journey to a culture of innovation and discovery you must:

1) Break Out Of Your Box

3) Align All Eyes On The Target

2) Ride The Ripple Effects

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Netflix…

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The Netflix Prize…

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Take Action!

What can you each do to create a culture of innovation and discovery within your organization?

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Thank You

Silly Bandz, Jibbitz, & Copernicus:

Driving Innovation Through Analytics

Bill Franks Kyle Cheek Bill.Franks@Teradata.com KCheek@Emdeon.com