SlideShare a Scribd company logo
1 of 16
Download to read offline
Fourth Quarter 2014
INVESTOR PRESENTATION
2 12/4/2014 Teradata Confidential
Note to Investors
• Certain non-GAAP financial information regarding operating results
may be discussed during this presentation. Reconciliations of the
differences between GAAP and non-GAAP measures are available on
the Investor page at www.teradata.com/investor.
• Remarks and responses associated with this presentation include
forward-looking statements that are based on current expectations
and assumptions. These forward-looking statements are subject to
a number of risks and uncertainties that could affect our future
results and could cause actual results to differ materially from
those expressed in such forward-looking statements.
• These risks and uncertainties are detailed in Teradata’s Registration
Statement on Form 10 and in our SEC reports, including, but not
limited to, our periodic and current reports on Forms 10-K, 10-Q,
and 8K, as well as the company’s annual report to shareholders.
3 12/4/2014 Teradata Confidential
Unified Data Architecture
• Any User, Any Data, Any Analysis
Audio/
Video
Images Text
Web &
Social
Machine
Logs
CRM SCM ERP
Engineers Business AnalystsQuantsData Scientists
Java, C/C++, Pig, Python, R, SAS, SQL, Excel, BI, Visualization, etc.
Discovery Platform Integrated Data
Warehouse
Aster MapReduce Portfolio Teradata Analytics Portfolio
Capture, Store, Refine
SQL-H
4 12/4/2014 Teradata Confidential
Teradata Analytical Ecosystem
1000 series
2000 series
500 series
6000 series
2000 series
6000 series
5 12/4/2014 Teradata Confidential
Teradata Workload-Specific Platforms
Data Mart
Appliance
Extreme
Data
Appliance
Data
Warehouse
Appliance
Active
Enterprise
Data
Warehouse
Appliance
for Hadoop
Aster Big
Analytics
Appliance
600 Series 1000 Series 2000 Series 6000 Series
Scale Up to 8TB Up to 186PB Up to 1.6PB Up to 61PB Up to 10PB Up to 5PB
Workloads
Test /
Development
or Smaller
Data Marts
Analytical
Archive,
Deep Dive
Analytics
Strategic
Intelligence,
Decision
Support
System, Fast
Scan
Strategic &
Operational
Intelligence,
Real Time
Update,
Active
workloads
Appliance for
Storing,
Capturing and
Refining Data.
Hortonworks
HDP 1.1
Discovery
Platform for Big
Data Analytics
with embedded
SQL MapReduce
for new data
types & sources
6 12/4/2014 Teradata Confidential
Broadest, Most Flexible Hadoop Offerings in Market
Aster Big Analytics
Appliance
Appliance for Hadoop
Commodity Offering
with Dell
Software Only
Overview Premier Premier Commodity Commodity
Value
Proposition
Big data analytics in an
integrated package
Enterprise-ready Hadoop
with single vendor
support
Pre-designed, cost-
effective commodity
hardware with
Teradata software
support
Flexible hardware
options with
Teradata software
support
Value Add
 Single best analytics
& discovery platform
 SQL-MapReduce &
70+ analytics
 Integrated
hardware/software
 Software to simplify
operation of Hadoop
 Integrated
hardware/software
that is engineered,
staged, & delivered
complete.
 Data staging &
refining
 Software to simplify
operation of Hadoop
 Hardware staged,
delivered by Dell
 Teradata software
install & support
 Teradata software
install & support
Segment
Business Value Based
IT
“Buy it”
Value Based IT
“Buy it”
Cost-Based IT
“Buy it, Build it”
Engineering IT
“Build it”
100% Open Source, Community-Driven Enterprise Apache Hadoop
7 12/4/2014 Teradata Confidential
Continuous update
and time-sensitive
queries become
important
OPERATIONALIZING
WHAT IS
happening now?
Event-based
triggering
takes hold
ACTIVATING
MAKE it happen!
Primarily batch
and some ad hoc
reports
Increase in
ad hoc analysis
ANALYZING
WHY
did it happen?
REPORTING
WHAT
happened?
Analytical
modeling
grows
PREDICTING
WHAT WILL
happen?
WorkloadSophistication
Scalability
Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
Teradata’s Data Warehouse Evolution/Vision
8 12/4/2014 Teradata Confidential
Teradata’s Industry-Leading Technology
Technology Demands
• Extreme scalability
• Extreme performance
• Extreme availability
• Extreme data load and
access
Scalability Across Multiple Dimensions
Data Volume
(Raw, User Data)
Schema
Sophistication
Query
Freedom
Query
Complexity
Query
Concurrency
Mixed
Workload
Query Data Volume
Data
Freshness
Teradata can scale
simultaneously across
multiple dimensions.
Driven by business!
Competition scales one
dimension at the expense
of others.
Limited by technology!
Mission critical 7x24
9 12/4/2014 Teradata Confidential
Teradata – The Data Warehouse Leader
The Magic Quadrant is copyrighted March 2014 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time
period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service
depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the "Leaders" quadrant. The Magic Quadrant is intended solely as a research
tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or
fitness for a particular purpose.
Gartner Magic Quadrant for Data Warehouse Database
Management Systems
Magic Quadrant for Data Warehouse
Database Management Systems
Mark Beyer, Roxanne Edjlali 3/7/14
10 12/4/2014 Teradata Confidential
The Forrester Wave TM
Enterprise Data Warehouse Q4 2013
The Forrester Wave ™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave ™ are trademarks of Forrester Research, Inc. The Forrester Wave ™ is a graphical
representation of Forrester’s call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor,
product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.
11 12/4/2014 Teradata Confidential
Copyright © 2011 by Teradata Corporation
Marketing Operations
Workflow
& Rules
Plan
Spend
Marketing
Assets
Source Data
Product/
SKU
Inventory
Sales/
POS/
Franchise
Supplier
Promotional/
Loyalty Data
Strategic Insight and
Marketing Processes
Seamless Customer
Engagement
Call
Center
ATM/
Kiosk
Email
Mobile
Channels Customers
Sales
Consumer
Partner
Social
Media
Web
Campaign
Design
Multi-Channel
Campaign Management
Leads
Campaign
Automation
Teradata
Customer
Data
Warehouse
Aster Data Marketing Analytics
Mining
Modeling
Reporting
Trending
Profiling
Segmenting
Teradata
Aster
Customer-centric
Data Management
Assets
Big Data
Teradata Applications: An Integrated
Marketing Management Solution
12 12/4/2014 Teradata Confidential
Teradata positioned as Leader
Gartner 2014 MRM Magic Quadrant
Magic Quadrant for MRM MQ,
Kim Collins, 2/4/14
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available
upon request from Teradata. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those
vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner
disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose
13 12/4/2014 Teradata Confidential
95%
71%
57%
52% 50%
46%
24%
8%
39%
15% 16%
26%
23%
16%
7%
4%
Telecom Trav/Trans Media/Ent. Healthcare Retail Fin/Ins Manuf Energy &
Utilities
Revenue by Industry
Industry Penetration+
Financial
31%
Comms.
19%
Retail
14%
Manuf.
13%
Healthcare
8%
Gov't.
6%
Travel/Trans.
5%
Other
2%
Energy & Util.
1%
2013*
* Approximate percentages of product and consulting services revenue by industry.
Global Fortune 500 Global 3000
2007 2008 2009 2010 2011 2012 2013
Financial
Services
24% 29% 28% 28% 28% 30% 31%
Communications 28% 28% 23% 24% 24% 21% 19%
Retail 19% 16% 17% 16% 16% 16% 14%
Manufacturing 9% 11% 10% 13% 12% 12% 13%
Healthcare 5% 4% 8% 6% 7% 7% 8%
Travel &
Transportation
6% 6% 6% 6% 5% 6% 6%
Government 7% 5% 7% 7% 6% 6% 6%
Other 2% 1% <1% <1% 2% 2% 3%
+ Percentage of companies
within each industry
vertical that have begun
investing in Teradata.
14 12/4/2014 Teradata Confidential
96
124
199
284
302
320
333 338
415
456
580
532
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Operating Performance
1,217 1,203
1,349
1,467
1,547
1,702
1,762
1,709
1,936
2,362
2,665
2,692
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
$ in millions
(1)% 12% 9% 5% 10% 4% (3)% 13% 22% 13% 1%
Revenue
Operating Income
Operating Income
$ in millions
15 12/4/2014 Teradata Confidential
Revenue Mix & Operating Segments
Revenue Mix 2013
% of
Total
Products (SW/HW) $1,230M 46%
Consulting Services 818M 30%
Maintenance Services 644M 24%
Total Revenue $2,692M
Approximately 40% of Teradata’s
revenue is from recurring
revenue sources.
Americas
61% of total revenue
Gross margin 58%
International
39% of total revenue
Gross margin 50%
Operating Segments
16 12/4/2014 Teradata Confidential
Strong Balance Sheet & Cash Flow
Balance Sheet
($ in millions)
Dec. 31
2007
Dec. 31
2008
Dec. 31
2009
Dec. 31
2010
Dec. 31
2011
Dec. 31
2012
Dec. 31
2013
Sept. 30
2014
Cash & Short-Term
Investments 270 442 661 883 772 729 695 848
Working Capital 301 475 609 837 717 728 787 735
Total Assets 1,294 1,430 1,569 1,883 2,616 3,066 3,096 3,018
Debt 0 0 0 0 300 289 274 255
Total Deferred Revenue 246 285 280 290 363 405 415 402
Total Shareholders’ Equity 631 777 910 1,189 1,494 1,779 1,857 1,842
Cash Flow
($ in millions)
2007 2008 2009 2010 2011 2012 2013
Net Income 200 250 254 301 353 419 377
Cash from Operating Income 387 440 455 413 513 575 510
Free Cash Flow 292 369 367 403 403 427 372

More Related Content

What's hot

Q2 2015 investor presentation
Q2 2015 investor presentationQ2 2015 investor presentation
Q2 2015 investor presentationteradata2014
 
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...Elemica
 
Fractal analytics measuring, evaluating and predicting the social consumer
Fractal analytics  measuring, evaluating and predicting the social consumerFractal analytics  measuring, evaluating and predicting the social consumer
Fractal analytics measuring, evaluating and predicting the social consumerFractal Analytics
 
Predictive Data Analytics and Artificial Intelligence by 40°
Predictive Data Analytics and Artificial Intelligence by 40°Predictive Data Analytics and Artificial Intelligence by 40°
Predictive Data Analytics and Artificial Intelligence by 40°40° Labor für Innovation
 
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...Precisely
 
Liat 2019 positioning-apps_analytics_v7
Liat  2019 positioning-apps_analytics_v7Liat  2019 positioning-apps_analytics_v7
Liat 2019 positioning-apps_analytics_v7Inbalraanan
 
A New Day for Oracle Analytics
A New Day for Oracle AnalyticsA New Day for Oracle Analytics
A New Day for Oracle AnalyticsRich Clayton
 
Pentaho Healthcare Solutions
Pentaho Healthcare SolutionsPentaho Healthcare Solutions
Pentaho Healthcare SolutionsPentaho
 
BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...
BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...
BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...Alexandre Perrot
 
Embedded Analytics in Customer Success
Embedded Analytics in Customer SuccessEmbedded Analytics in Customer Success
Embedded Analytics in Customer SuccessPentaho
 
Embedded Analytics in CRM and Marketing
Embedded Analytics in CRM and Marketing Embedded Analytics in CRM and Marketing
Embedded Analytics in CRM and Marketing Pentaho
 
Hadoop: Revolutionizing Analytics AND Operations
Hadoop: Revolutionizing Analytics AND OperationsHadoop: Revolutionizing Analytics AND Operations
Hadoop: Revolutionizing Analytics AND OperationsMapR Technologies
 
Atgwork Brochure 1
Atgwork Brochure 1Atgwork Brochure 1
Atgwork Brochure 1cghanks2000
 
Logic fin - company analisis example -alteryx 2014-11
Logic fin - company analisis example -alteryx 2014-11Logic fin - company analisis example -alteryx 2014-11
Logic fin - company analisis example -alteryx 2014-11Diego Gutierrez
 
Pentaho Business Analytics for ISVs and SaaS providers in healthcare
Pentaho Business Analytics for ISVs and SaaS providers in healthcarePentaho Business Analytics for ISVs and SaaS providers in healthcare
Pentaho Business Analytics for ISVs and SaaS providers in healthcarePentaho
 
Embedded Analytics in Human Capital Management
Embedded Analytics in Human Capital ManagementEmbedded Analytics in Human Capital Management
Embedded Analytics in Human Capital ManagementPentaho
 

What's hot (19)

Q2 2015 investor presentation
Q2 2015 investor presentationQ2 2015 investor presentation
Q2 2015 investor presentation
 
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...
 
Fractal analytics measuring, evaluating and predicting the social consumer
Fractal analytics  measuring, evaluating and predicting the social consumerFractal analytics  measuring, evaluating and predicting the social consumer
Fractal analytics measuring, evaluating and predicting the social consumer
 
Predictive Data Analytics and Artificial Intelligence by 40°
Predictive Data Analytics and Artificial Intelligence by 40°Predictive Data Analytics and Artificial Intelligence by 40°
Predictive Data Analytics and Artificial Intelligence by 40°
 
Microsoft Power BI Overview
Microsoft Power BI OverviewMicrosoft Power BI Overview
Microsoft Power BI Overview
 
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...
 
Liat 2019 positioning-apps_analytics_v7
Liat  2019 positioning-apps_analytics_v7Liat  2019 positioning-apps_analytics_v7
Liat 2019 positioning-apps_analytics_v7
 
What is a Demand Signal Repository?
What is a Demand Signal Repository?What is a Demand Signal Repository?
What is a Demand Signal Repository?
 
A New Day for Oracle Analytics
A New Day for Oracle AnalyticsA New Day for Oracle Analytics
A New Day for Oracle Analytics
 
Pentaho Healthcare Solutions
Pentaho Healthcare SolutionsPentaho Healthcare Solutions
Pentaho Healthcare Solutions
 
BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...
BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...
BARC - QlikTech Ranks First in Collaboration and Performance Satisfaction Amo...
 
Embedded Analytics in Customer Success
Embedded Analytics in Customer SuccessEmbedded Analytics in Customer Success
Embedded Analytics in Customer Success
 
Embedded Analytics in CRM and Marketing
Embedded Analytics in CRM and Marketing Embedded Analytics in CRM and Marketing
Embedded Analytics in CRM and Marketing
 
Hadoop: Revolutionizing Analytics AND Operations
Hadoop: Revolutionizing Analytics AND OperationsHadoop: Revolutionizing Analytics AND Operations
Hadoop: Revolutionizing Analytics AND Operations
 
Atgwork Brochure 1
Atgwork Brochure 1Atgwork Brochure 1
Atgwork Brochure 1
 
Qlik datamarket messaging house
Qlik datamarket   messaging houseQlik datamarket   messaging house
Qlik datamarket messaging house
 
Logic fin - company analisis example -alteryx 2014-11
Logic fin - company analisis example -alteryx 2014-11Logic fin - company analisis example -alteryx 2014-11
Logic fin - company analisis example -alteryx 2014-11
 
Pentaho Business Analytics for ISVs and SaaS providers in healthcare
Pentaho Business Analytics for ISVs and SaaS providers in healthcarePentaho Business Analytics for ISVs and SaaS providers in healthcare
Pentaho Business Analytics for ISVs and SaaS providers in healthcare
 
Embedded Analytics in Human Capital Management
Embedded Analytics in Human Capital ManagementEmbedded Analytics in Human Capital Management
Embedded Analytics in Human Capital Management
 

Similar to Investor Presentation Q4 2014

Q3 2015 investor presentation
Q3 2015 investor presentationQ3 2015 investor presentation
Q3 2015 investor presentationteradata2014
 
Q4 2015 investor presentation web copy 11 5 2015 435pm
Q4 2015 investor presentation web copy 11 5 2015 435pmQ4 2015 investor presentation web copy 11 5 2015 435pm
Q4 2015 investor presentation web copy 11 5 2015 435pmteradata2014
 
The Revolutionary Impact of the Cloud by Arun Chandrasekaran
The Revolutionary Impact of the Cloud by Arun ChandrasekaranThe Revolutionary Impact of the Cloud by Arun Chandrasekaran
The Revolutionary Impact of the Cloud by Arun ChandrasekaranPoh Lee
 
Q1 2016 investor presentation 2 5 2016 web copy
Q1 2016 investor presentation 2 5 2016 web copyQ1 2016 investor presentation 2 5 2016 web copy
Q1 2016 investor presentation 2 5 2016 web copyteradata2014
 
Q2 2016 investor presentation 5 6 2016 final
Q2 2016 investor presentation 5 6 2016 finalQ2 2016 investor presentation 5 6 2016 final
Q2 2016 investor presentation 5 6 2016 finalteradata2014
 
Gartner For Product Management And Marketing
Gartner For Product Management And MarketingGartner For Product Management And Marketing
Gartner For Product Management And Marketingtmannes
 
Commvault - Il Dato è tratto - 09.11.2017
Commvault - Il Dato è tratto - 09.11.2017Commvault - Il Dato è tratto - 09.11.2017
Commvault - Il Dato è tratto - 09.11.2017Eurosystem S.p.A.
 
Aen004 Thorpe 091807
Aen004 Thorpe 091807Aen004 Thorpe 091807
Aen004 Thorpe 091807Dreamforce07
 
Microsoft - Data and AI
Microsoft - Data and AIMicrosoft - Data and AI
Microsoft - Data and AITareq Mandou
 
Engaging Your CFO in Business Analytics | Palestrante: Celso Chapinotte
Engaging Your CFO in Business Analytics  | Palestrante: Celso ChapinotteEngaging Your CFO in Business Analytics  | Palestrante: Celso Chapinotte
Engaging Your CFO in Business Analytics | Palestrante: Celso Chapinottesucesuminas
 
apidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartner
apidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartnerapidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartner
apidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartnerapidays
 
Overview of Operations
Overview of OperationsOverview of Operations
Overview of OperationsGlenture
 
CIO Agenda 2015 - Flipping Into Digital Leadership
CIO Agenda 2015 - Flipping Into Digital LeadershipCIO Agenda 2015 - Flipping Into Digital Leadership
CIO Agenda 2015 - Flipping Into Digital LeadershipDerek Mulrey
 
Teradata Aster: Big Data Discovery Made Easy
Teradata Aster: Big Data Discovery Made EasyTeradata Aster: Big Data Discovery Made Easy
Teradata Aster: Big Data Discovery Made EasyTIBCO Spotfire
 
Why our customers choose teradata.
Why our customers choose teradata.Why our customers choose teradata.
Why our customers choose teradata.Adam Swaney
 
(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...
(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...
(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...Amazon Web Services
 
CommVault Intro SureSkills Simpana 10 Event 2013
CommVault Intro SureSkills Simpana 10 Event 2013CommVault Intro SureSkills Simpana 10 Event 2013
CommVault Intro SureSkills Simpana 10 Event 2013Google
 
HP Networking - FlexNetwork Architecteure Customer 2013
HP Networking - FlexNetwork Architecteure Customer 2013HP Networking - FlexNetwork Architecteure Customer 2013
HP Networking - FlexNetwork Architecteure Customer 2013Procontact Informatique
 
Gartner Webinars: Top 5 Post-COVID Workforce Planning
Gartner Webinars: Top 5 Post-COVID Workforce Planning Gartner Webinars: Top 5 Post-COVID Workforce Planning
Gartner Webinars: Top 5 Post-COVID Workforce Planning Rustin Richburg
 

Similar to Investor Presentation Q4 2014 (20)

Q3 2015 investor presentation
Q3 2015 investor presentationQ3 2015 investor presentation
Q3 2015 investor presentation
 
Q4 2015 investor presentation web copy 11 5 2015 435pm
Q4 2015 investor presentation web copy 11 5 2015 435pmQ4 2015 investor presentation web copy 11 5 2015 435pm
Q4 2015 investor presentation web copy 11 5 2015 435pm
 
The Revolutionary Impact of the Cloud by Arun Chandrasekaran
The Revolutionary Impact of the Cloud by Arun ChandrasekaranThe Revolutionary Impact of the Cloud by Arun Chandrasekaran
The Revolutionary Impact of the Cloud by Arun Chandrasekaran
 
Q1 2016 investor presentation 2 5 2016 web copy
Q1 2016 investor presentation 2 5 2016 web copyQ1 2016 investor presentation 2 5 2016 web copy
Q1 2016 investor presentation 2 5 2016 web copy
 
Q2 2016 investor presentation 5 6 2016 final
Q2 2016 investor presentation 5 6 2016 finalQ2 2016 investor presentation 5 6 2016 final
Q2 2016 investor presentation 5 6 2016 final
 
Gartner For Product Management And Marketing
Gartner For Product Management And MarketingGartner For Product Management And Marketing
Gartner For Product Management And Marketing
 
Commvault - Il Dato è tratto - 09.11.2017
Commvault - Il Dato è tratto - 09.11.2017Commvault - Il Dato è tratto - 09.11.2017
Commvault - Il Dato è tratto - 09.11.2017
 
Aen004 Thorpe 091807
Aen004 Thorpe 091807Aen004 Thorpe 091807
Aen004 Thorpe 091807
 
Microsoft - Data and AI
Microsoft - Data and AIMicrosoft - Data and AI
Microsoft - Data and AI
 
Engaging Your CFO in Business Analytics | Palestrante: Celso Chapinotte
Engaging Your CFO in Business Analytics  | Palestrante: Celso ChapinotteEngaging Your CFO in Business Analytics  | Palestrante: Celso Chapinotte
Engaging Your CFO in Business Analytics | Palestrante: Celso Chapinotte
 
apidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartner
apidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartnerapidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartner
apidays Singapore 2023 - State of the API Industry, Manjunath Bhat, Gartner
 
Overview of Operations
Overview of OperationsOverview of Operations
Overview of Operations
 
CIO Agenda 2015 - Flipping Into Digital Leadership
CIO Agenda 2015 - Flipping Into Digital LeadershipCIO Agenda 2015 - Flipping Into Digital Leadership
CIO Agenda 2015 - Flipping Into Digital Leadership
 
Teradata Aster: Big Data Discovery Made Easy
Teradata Aster: Big Data Discovery Made EasyTeradata Aster: Big Data Discovery Made Easy
Teradata Aster: Big Data Discovery Made Easy
 
1415 gold sanford
1415 gold sanford1415 gold sanford
1415 gold sanford
 
Why our customers choose teradata.
Why our customers choose teradata.Why our customers choose teradata.
Why our customers choose teradata.
 
(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...
(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...
(ENT312) Should You Build or Buy Cloud Infrastructure and Platforms? | AWS re...
 
CommVault Intro SureSkills Simpana 10 Event 2013
CommVault Intro SureSkills Simpana 10 Event 2013CommVault Intro SureSkills Simpana 10 Event 2013
CommVault Intro SureSkills Simpana 10 Event 2013
 
HP Networking - FlexNetwork Architecteure Customer 2013
HP Networking - FlexNetwork Architecteure Customer 2013HP Networking - FlexNetwork Architecteure Customer 2013
HP Networking - FlexNetwork Architecteure Customer 2013
 
Gartner Webinars: Top 5 Post-COVID Workforce Planning
Gartner Webinars: Top 5 Post-COVID Workforce Planning Gartner Webinars: Top 5 Post-COVID Workforce Planning
Gartner Webinars: Top 5 Post-COVID Workforce Planning
 

Recently uploaded

RE Capital's Visionary Leadership under Newman Leech
RE Capital's Visionary Leadership under Newman LeechRE Capital's Visionary Leadership under Newman Leech
RE Capital's Visionary Leadership under Newman LeechNewman George Leech
 
8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCR8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCRashishs7044
 
8447779800, Low rate Call girls in Tughlakabad Delhi NCR
8447779800, Low rate Call girls in Tughlakabad Delhi NCR8447779800, Low rate Call girls in Tughlakabad Delhi NCR
8447779800, Low rate Call girls in Tughlakabad Delhi NCRashishs7044
 
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort ServiceCall US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Servicecallgirls2057
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaoncallgirls2057
 
Pitch Deck Teardown: NOQX's $200k Pre-seed deck
Pitch Deck Teardown: NOQX's $200k Pre-seed deckPitch Deck Teardown: NOQX's $200k Pre-seed deck
Pitch Deck Teardown: NOQX's $200k Pre-seed deckHajeJanKamps
 
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,noida100girls
 
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCRashishs7044
 
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607dollysharma2066
 
Digital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfDigital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfJos Voskuil
 
Call Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / Ncr
Call Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / NcrCall Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / Ncr
Call Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / Ncrdollysharma2066
 
BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,noida100girls
 
Organizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessOrganizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessSeta Wicaksana
 
Kenya’s Coconut Value Chain by Gatsby Africa
Kenya’s Coconut Value Chain by Gatsby AfricaKenya’s Coconut Value Chain by Gatsby Africa
Kenya’s Coconut Value Chain by Gatsby Africaictsugar
 
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCRashishs7044
 
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In.../:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...lizamodels9
 
Annual General Meeting Presentation Slides
Annual General Meeting Presentation SlidesAnnual General Meeting Presentation Slides
Annual General Meeting Presentation SlidesKeppelCorporation
 
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...lizamodels9
 
Market Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 EditionMarket Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 EditionMintel Group
 
India Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample ReportIndia Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample ReportMintel Group
 

Recently uploaded (20)

RE Capital's Visionary Leadership under Newman Leech
RE Capital's Visionary Leadership under Newman LeechRE Capital's Visionary Leadership under Newman Leech
RE Capital's Visionary Leadership under Newman Leech
 
8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCR8447779800, Low rate Call girls in Saket Delhi NCR
8447779800, Low rate Call girls in Saket Delhi NCR
 
8447779800, Low rate Call girls in Tughlakabad Delhi NCR
8447779800, Low rate Call girls in Tughlakabad Delhi NCR8447779800, Low rate Call girls in Tughlakabad Delhi NCR
8447779800, Low rate Call girls in Tughlakabad Delhi NCR
 
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort ServiceCall US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
Call US-88OO1O2216 Call Girls In Mahipalpur Female Escort Service
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
 
Pitch Deck Teardown: NOQX's $200k Pre-seed deck
Pitch Deck Teardown: NOQX's $200k Pre-seed deckPitch Deck Teardown: NOQX's $200k Pre-seed deck
Pitch Deck Teardown: NOQX's $200k Pre-seed deck
 
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
 
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
8447779800, Low rate Call girls in Uttam Nagar Delhi NCR
 
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
 
Digital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdfDigital Transformation in the PLM domain - distrib.pdf
Digital Transformation in the PLM domain - distrib.pdf
 
Call Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / Ncr
Call Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / NcrCall Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / Ncr
Call Girls in DELHI Cantt, ( Call Me )-8377877756-Female Escort- In Delhi / Ncr
 
BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
BEST Call Girls In Greater Noida ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
 
Organizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessOrganizational Structure Running A Successful Business
Organizational Structure Running A Successful Business
 
Kenya’s Coconut Value Chain by Gatsby Africa
Kenya’s Coconut Value Chain by Gatsby AfricaKenya’s Coconut Value Chain by Gatsby Africa
Kenya’s Coconut Value Chain by Gatsby Africa
 
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
8447779800, Low rate Call girls in New Ashok Nagar Delhi NCR
 
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In.../:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
/:Call Girls In Indirapuram Ghaziabad ➥9990211544 Independent Best Escorts In...
 
Annual General Meeting Presentation Slides
Annual General Meeting Presentation SlidesAnnual General Meeting Presentation Slides
Annual General Meeting Presentation Slides
 
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
Call Girls In Sikandarpur Gurgaon ❤️8860477959_Russian 100% Genuine Escorts I...
 
Market Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 EditionMarket Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 Edition
 
India Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample ReportIndia Consumer 2024 Redacted Sample Report
India Consumer 2024 Redacted Sample Report
 

Investor Presentation Q4 2014

  • 2. 2 12/4/2014 Teradata Confidential Note to Investors • Certain non-GAAP financial information regarding operating results may be discussed during this presentation. Reconciliations of the differences between GAAP and non-GAAP measures are available on the Investor page at www.teradata.com/investor. • Remarks and responses associated with this presentation include forward-looking statements that are based on current expectations and assumptions. These forward-looking statements are subject to a number of risks and uncertainties that could affect our future results and could cause actual results to differ materially from those expressed in such forward-looking statements. • These risks and uncertainties are detailed in Teradata’s Registration Statement on Form 10 and in our SEC reports, including, but not limited to, our periodic and current reports on Forms 10-K, 10-Q, and 8K, as well as the company’s annual report to shareholders.
  • 3. 3 12/4/2014 Teradata Confidential Unified Data Architecture • Any User, Any Data, Any Analysis Audio/ Video Images Text Web & Social Machine Logs CRM SCM ERP Engineers Business AnalystsQuantsData Scientists Java, C/C++, Pig, Python, R, SAS, SQL, Excel, BI, Visualization, etc. Discovery Platform Integrated Data Warehouse Aster MapReduce Portfolio Teradata Analytics Portfolio Capture, Store, Refine SQL-H
  • 4. 4 12/4/2014 Teradata Confidential Teradata Analytical Ecosystem 1000 series 2000 series 500 series 6000 series 2000 series 6000 series
  • 5. 5 12/4/2014 Teradata Confidential Teradata Workload-Specific Platforms Data Mart Appliance Extreme Data Appliance Data Warehouse Appliance Active Enterprise Data Warehouse Appliance for Hadoop Aster Big Analytics Appliance 600 Series 1000 Series 2000 Series 6000 Series Scale Up to 8TB Up to 186PB Up to 1.6PB Up to 61PB Up to 10PB Up to 5PB Workloads Test / Development or Smaller Data Marts Analytical Archive, Deep Dive Analytics Strategic Intelligence, Decision Support System, Fast Scan Strategic & Operational Intelligence, Real Time Update, Active workloads Appliance for Storing, Capturing and Refining Data. Hortonworks HDP 1.1 Discovery Platform for Big Data Analytics with embedded SQL MapReduce for new data types & sources
  • 6. 6 12/4/2014 Teradata Confidential Broadest, Most Flexible Hadoop Offerings in Market Aster Big Analytics Appliance Appliance for Hadoop Commodity Offering with Dell Software Only Overview Premier Premier Commodity Commodity Value Proposition Big data analytics in an integrated package Enterprise-ready Hadoop with single vendor support Pre-designed, cost- effective commodity hardware with Teradata software support Flexible hardware options with Teradata software support Value Add  Single best analytics & discovery platform  SQL-MapReduce & 70+ analytics  Integrated hardware/software  Software to simplify operation of Hadoop  Integrated hardware/software that is engineered, staged, & delivered complete.  Data staging & refining  Software to simplify operation of Hadoop  Hardware staged, delivered by Dell  Teradata software install & support  Teradata software install & support Segment Business Value Based IT “Buy it” Value Based IT “Buy it” Cost-Based IT “Buy it, Build it” Engineering IT “Build it” 100% Open Source, Community-Driven Enterprise Apache Hadoop
  • 7. 7 12/4/2014 Teradata Confidential Continuous update and time-sensitive queries become important OPERATIONALIZING WHAT IS happening now? Event-based triggering takes hold ACTIVATING MAKE it happen! Primarily batch and some ad hoc reports Increase in ad hoc analysis ANALYZING WHY did it happen? REPORTING WHAT happened? Analytical modeling grows PREDICTING WHAT WILL happen? WorkloadSophistication Scalability Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Teradata’s Data Warehouse Evolution/Vision
  • 8. 8 12/4/2014 Teradata Confidential Teradata’s Industry-Leading Technology Technology Demands • Extreme scalability • Extreme performance • Extreme availability • Extreme data load and access Scalability Across Multiple Dimensions Data Volume (Raw, User Data) Schema Sophistication Query Freedom Query Complexity Query Concurrency Mixed Workload Query Data Volume Data Freshness Teradata can scale simultaneously across multiple dimensions. Driven by business! Competition scales one dimension at the expense of others. Limited by technology! Mission critical 7x24
  • 9. 9 12/4/2014 Teradata Confidential Teradata – The Data Warehouse Leader The Magic Quadrant is copyrighted March 2014 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the "Leaders" quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner Magic Quadrant for Data Warehouse Database Management Systems Magic Quadrant for Data Warehouse Database Management Systems Mark Beyer, Roxanne Edjlali 3/7/14
  • 10. 10 12/4/2014 Teradata Confidential The Forrester Wave TM Enterprise Data Warehouse Q4 2013 The Forrester Wave ™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave ™ are trademarks of Forrester Research, Inc. The Forrester Wave ™ is a graphical representation of Forrester’s call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.
  • 11. 11 12/4/2014 Teradata Confidential Copyright © 2011 by Teradata Corporation Marketing Operations Workflow & Rules Plan Spend Marketing Assets Source Data Product/ SKU Inventory Sales/ POS/ Franchise Supplier Promotional/ Loyalty Data Strategic Insight and Marketing Processes Seamless Customer Engagement Call Center ATM/ Kiosk Email Mobile Channels Customers Sales Consumer Partner Social Media Web Campaign Design Multi-Channel Campaign Management Leads Campaign Automation Teradata Customer Data Warehouse Aster Data Marketing Analytics Mining Modeling Reporting Trending Profiling Segmenting Teradata Aster Customer-centric Data Management Assets Big Data Teradata Applications: An Integrated Marketing Management Solution
  • 12. 12 12/4/2014 Teradata Confidential Teradata positioned as Leader Gartner 2014 MRM Magic Quadrant Magic Quadrant for MRM MQ, Kim Collins, 2/4/14 This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Teradata. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose
  • 13. 13 12/4/2014 Teradata Confidential 95% 71% 57% 52% 50% 46% 24% 8% 39% 15% 16% 26% 23% 16% 7% 4% Telecom Trav/Trans Media/Ent. Healthcare Retail Fin/Ins Manuf Energy & Utilities Revenue by Industry Industry Penetration+ Financial 31% Comms. 19% Retail 14% Manuf. 13% Healthcare 8% Gov't. 6% Travel/Trans. 5% Other 2% Energy & Util. 1% 2013* * Approximate percentages of product and consulting services revenue by industry. Global Fortune 500 Global 3000 2007 2008 2009 2010 2011 2012 2013 Financial Services 24% 29% 28% 28% 28% 30% 31% Communications 28% 28% 23% 24% 24% 21% 19% Retail 19% 16% 17% 16% 16% 16% 14% Manufacturing 9% 11% 10% 13% 12% 12% 13% Healthcare 5% 4% 8% 6% 7% 7% 8% Travel & Transportation 6% 6% 6% 6% 5% 6% 6% Government 7% 5% 7% 7% 6% 6% 6% Other 2% 1% <1% <1% 2% 2% 3% + Percentage of companies within each industry vertical that have begun investing in Teradata.
  • 14. 14 12/4/2014 Teradata Confidential 96 124 199 284 302 320 333 338 415 456 580 532 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Operating Performance 1,217 1,203 1,349 1,467 1,547 1,702 1,762 1,709 1,936 2,362 2,665 2,692 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 $ in millions (1)% 12% 9% 5% 10% 4% (3)% 13% 22% 13% 1% Revenue Operating Income Operating Income $ in millions
  • 15. 15 12/4/2014 Teradata Confidential Revenue Mix & Operating Segments Revenue Mix 2013 % of Total Products (SW/HW) $1,230M 46% Consulting Services 818M 30% Maintenance Services 644M 24% Total Revenue $2,692M Approximately 40% of Teradata’s revenue is from recurring revenue sources. Americas 61% of total revenue Gross margin 58% International 39% of total revenue Gross margin 50% Operating Segments
  • 16. 16 12/4/2014 Teradata Confidential Strong Balance Sheet & Cash Flow Balance Sheet ($ in millions) Dec. 31 2007 Dec. 31 2008 Dec. 31 2009 Dec. 31 2010 Dec. 31 2011 Dec. 31 2012 Dec. 31 2013 Sept. 30 2014 Cash & Short-Term Investments 270 442 661 883 772 729 695 848 Working Capital 301 475 609 837 717 728 787 735 Total Assets 1,294 1,430 1,569 1,883 2,616 3,066 3,096 3,018 Debt 0 0 0 0 300 289 274 255 Total Deferred Revenue 246 285 280 290 363 405 415 402 Total Shareholders’ Equity 631 777 910 1,189 1,494 1,779 1,857 1,842 Cash Flow ($ in millions) 2007 2008 2009 2010 2011 2012 2013 Net Income 200 250 254 301 353 419 377 Cash from Operating Income 387 440 455 413 513 575 510 Free Cash Flow 292 369 367 403 403 427 372