3. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
The PDF for this Session presentation is available from our
Technical Session Catalog at the end of the day at:
intel.com/go/idfsessionsBJ
URL is on top of Session Agenda Pages in Pocket Guide
3
4. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
4
5. We are at an Inflection Point in
Healthcare - TRENDS
% of population over age 60
30+ %
25-29%
20-24%
2050
10-19%
0-9%
WW Average Age 60+: 21%
Source: United Nations “Population Aging 2002”
Healthcare costs are Global AGING U.S. Healthcare BIG DATA
RISING Average age 60+: Value
Significant % of GDP growing from 10% to $300 Billion in value/year
21% by 2050 ~ 0.7% annual productivity
growth
Source: McKinsey Global Institute Analysis
ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast
5
6. We are at an Inflection Point in
Healthcare - TRENDS
Storage Growth
Total Data Healthcare Providers (PB)
15000
Admin
Imaging Medical Imaging Archive Projection
10000 Case from just 1 healthcare system
EMR
Email
5000
File
Non Clin Img
0
Research
2010 2011 2012 2013 2014 2015
Data Explosion projected to reach 35 Zetabytes by 2020, with
a 44-fold increase from 2009
Source: McKinsey Global Institute Analysis
ESG Research Report 2011 – North American Health Care Provider Market Size and Forecast
6
7. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
7
8. Big Data in Healthcare
Where is the data coming from?
2. Clinical Decision Support &
1. Pharma/Life Sciences Trends (includes Diagnostic
Imaging)
4. Patient Behavior/Social
3. Claims, Utilization and Fraud
Networking
How do we create value? (examples)
1. Personalized Medicine 2. Clinical Decision Support
4. Analytics for Lifestyle and
3. Enhanced Fraud Detection
Behavior-induced Diseases
McKinsey Global Institute Analysis
8
9. Big Data Solution for Healthcare
Health Info Personal Health
Primary Care Aging Society
Services Management
New Healthcare Clinical Decision Personalized
Applications Cancer Genomics
Support Medicine
Analytics and Medical Imaging
SQL-like Query Machine Learning
Visualization Analytics
Data Processing/
Medical Records Genome Data Medical Images
Management
Distributed Storage Security and Imaging
Platform Optimization Privacy Acceleration
9
10. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
10
11. Big Data Challenges are More than Data Size...
And Require New Technologies
Lab results, billing data, images, sensors data from
Volume devices, genomics
• Structured data in standard formats like HL7
Variety • Unstructured data from dictations, transcription,
photos, images
Analyzing data from existing databases for claims,
Value patient history, archived images, real-time data
analytics for clinical decision support
• Realtime rather than batch-style analysis
Velocity • Data streamed in, tortured, and discarded
• Making impact on the spot rather than after-the-fact
Traditional business solutions connecting to new data and
analytics models for real-time value opportunities
11
12. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
12
13. All Eyes on Data for Value
Big Data Storage Considerations
Traditional Solution
Traditional Storage Approaches
Environments
Large Analytics – Hadoop*
ERP, CRM, Batch,
Large DB – Hive*
OLTP-DB
Large Backup – Lustre*
Edge Servers
Analytical
Synchronization
End-to-End
Machine-to-Machine
Source-to-Source
Data Source
Text-Voice-Video-Sensor
Requesting Or M2M
Communication
Batch – Business Applications
Rich Visualization – Secure Data Analysis and Caching
13
14. All Eyes on Data for Value Data Center Provisioning
Discrete
Virtual
Cloud – As A Service
Big Data Storage Considerations HPC
Traditional Solution
Traditional Storage Approaches
Environments
Large Analytics – Hadoop*
ERP, CRM, Batch,
Large DB – Hive*
OLTP-DB
Large Backup – Lustre*
Edge Servers
Analytical
Synchronization
End-to-End
Machine-to-Machine
Source-to-Source
Operational Solution Stack Example
Applications & Services
Data Source
Text-Voice-Video-Sensor Visualization – File Structure & Analytical
Requesting Or M2M Tools
Communication Data Delivery, Operational & Graphical
Batch – Business Applications Analytics
Data Management & Computational
Analytics
Rich Visualization – Secure Data Analysis and Caching Compute – Storage & Infrastructure
Platforms
14
15. Enterprise Big Data Architecture
STRUCTURED ENTERPRISE
DATA PLATFORMS
DATA PLATFORMS TOOLS
Legacy Node Node Node ODS & Data
Marts
Data Mining
Hadoop* Dev IDE
Logs
Create Map
CONSUME REDUCE IMPORT
Enterprise Data
Warehouse
UNSTRUCTURED
Visualize
Social & Web No-SQL
Spreadsheets
INSIGHTS
Legacy APPS
Document In Memory DB
Types
SQL
Web
Transcriptions &
Apps
Notes
RDBMS MashUps
15
16. Big Data Architectural Framework
Data
Provisioning Models-Storage & Connectivity Considerations Velocity
NAS - SAS and
Databases 10GBe MPP Databases Data
Distributed
DBMS / NoSQL Fast Fabric DW Appliances Vulnerability
Storage
Human
Data Sources Security
Genome Surveillance and Text, Video GIS Services
Diagnostic Medical Social Medical
& Drug Images
Medical Device Log and Audio Privacy
Devices
Streaming Data Media Records
Discovery Files Compliance
Provisioning Models Can Vary by Data Characteristics
16
17. Big Data Architectural Framework
Data as a Services
Distributed Virtual Persistence
HPC / TCP Vertically
Event, Message Real-Time, Cached, Federated EDW, Marts
MIC Integrated Data
Software Characteristics
Intel
Data ingestion, Integration and Processing Services AIM Data Volume
Suite and
Distributed High Performance Integration
Data Processing Quality
Tools
Hadoop* MapReduce
Cloud
Data
Provisioning Models-Storage & Connectivity Considerations Velocity
NAS - SAS and
Databases 10GBe MPP Databases Data
Distributed
DBMS / NoSQL Fast Fabric DW Appliances Vulnerability
Storage
Human
Data Sources Security
Genome Surveillance and Text, Video GIS Services
Diagnostic Medical Social Medical
& Drug Images
Medical Device Log and Audio Privacy
Devices
Streaming Data Media Records
Discovery Files Compliance
Provisioning Models Can Vary by Data Characteristics
17
18. Big Data Architectural Framework
Data Access Data
User Visibility
Authentication
NLP/Semantic
Custom Analytic Solutions BI & Predictive Analytics Search/
Integrated Machine
MapReduce Textual Analytics Analytics with Existing BI/Analytics Learning
Hadoop with in-database Knowledge
Streaming Analytics Support data processing support Management
Data as a Services
Distributed Virtual Persistence
HPC / TCP Vertically
Event, Message Real-Time, Cached, Federated EDW, Marts
MIC Integrated Data
Software Characteristics
Intel
Data ingestion, Integration and Processing Services AIM Data
Suite Volume
Distributed High Performance Integration
Data Processing and
Tools Quality
Hadoop* MapReduce
Cloud
Data
Provisioning Models-Storage & Connectivity Considerations Velocity
NAS - SAS and
Databases 10GBe MPP Databases Data
Distributed
DBMS / NoSQL Fast Fabric DW Appliances Vulnerability
Storage
Human
Data Sources Security
Genome Surveillance and Text, Video GIS Services
Diagnostic Medical Social Medical
& Drug Images
Medical Device Log and Audio Privacy
Devices
Streaming Data Media Records
Discovery Files Compliance
Provisioning Models Can Vary by Data Characteristics
18
19. Accessing Big Data (Clients)
“Know Me” “Free Me” “Express Me” “Link Me”
Mobile Laptops,
Smart Clinical Tablet Ultrabook™ Fixed Digital
Phone Assistant PCs Devices PCs Signage Kiosk
Mobility
Vital sign,
I & O entry
Medication
administration
Template
data entry
Free-format text
data entry
Large diagnostic
images
Data inquiry
Manageability
19
20. Building on the Ecosystem
Database and Analytics Environments Optimized on Intel
Life Sciences
Database and compute infrastructure Analytics engines Workloads & Solutions
Relational
VOLTDB
EXALYTICS
Nonrelational
Open Source:
BLAST, FASTA,
ClustalW, HMMER,
Darwin, etc.
No Matter the Choice: All optimized on Intel® Xeon® processor
based hardware
20
21. Intel® Products and Software For Big Data
Scaling
Compute
Flexible Workloads &
Intel® Xeon® processor E5-
and E7 based servers up to Analysis Technical Compute
80% performance boost with Optimized Intel Xeon processor E5-
hardware-enhanced security
Data Delivery & based servers for TCP
Intel® Xeon Phi™ co-
Storage Management processor
Intelligent scale-out storage Software Ecosystem Integrated Systems
built with Intel Xeon
Interconnect Embedded Analysis Solutions
processor E5-based storage From Intel ISG
Efficiency
Robust & Secure
Interconnect
Visibly Mobile Intel Software EcoSystem
Fast Fabric & Caching Performance Client Hadoop*
Investing in new fabric Lustre*
approaches Rich Visualization In-memory
non-volatile memory that Seamless Access In stream data analysis
provide capacity caching for End to end security
data velocity
Performance Client
Network Rich modeling support
Intelligent scale-out Client – server application
networking management
from 10GBe – 40GBe
21
22. Examples of Intel-powered Servers in
Big Data and Analytics
Cisco* UCS Server1 Dell* PowerEdge* C Series2 Oracle* Sun Fire* server3
Intel® Xeon® Intel Xeon processor Intel Xeon processor E7-
processor 5600 5500/5600 4800
Cisco UCS server with EMC The Dell | Cloudera* solution Oracle Exalytics* In-Memory
Greenplum MR software - for Apache* Hadoop combines Machine, features the Oracle
“enterprise-class” BI Foundation Suite and
Hadoop* distribution that Oracle TimesTen In-Memory
features technology from Database for Exalytics
MapR
Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of
271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New
score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon®
processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or
Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data
are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase.
1 http://gigaom.com/cloud/ciscos-servers-now-tuned-for-hadoop/
2 http://www.businesswire.com/news/home/20110804005376/en/Dell-Cloudera-Collaborate-Enable-Large-Scale-Data
3 http://www.itp.net/mobile/588145-oracle-unveils-exalytics-in-memory-machine
22
24. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
24
25. Use Case: Regional Health Info Network – China
Real-time Clinical Decision Support
Data Analytic Care Coordination
• Real-time and recursive information R&D
…
Clinical decision support
…
processing of health data (EHR,
medical images) to support care RHIN
coordination, clinical decision Ancillary Health EHR Registries
support, and public health Data &
Services
Information
DW
Data &
Services
Data &
Services
management
• Enabling health data analytic with
Longitudinal Record Services
Hadoop* (HBase*/Hive*)
• Potential to scale cross geos and Health Information Access Layer
across sectors/segments
Primary care Public
• Involving local ISVs, local OEMs Hospital
(Grassroots) Health
• Technical Challenges
– Transforming the relational DB to
Hadoop (HBase/Hive)
– Addressing the usages of big data
analytics in RHIN
25
26. RHIN/Grassroots Solution with Big Data (Hadoop*)
Integrated User Interface(Citizen, Physician, Health Authority)
Cloud -based Regional Healthcare Service Distributed Data Service
System System
Multi-Tenancy Application Presentation
(Report, Viewer)
Pubic Medical
Health Service New Rural
Data Mining
(Mahout*)
Language & Compiler
(Hive*)
Medical
Distributed Batch Processing Real-time
Insurance Framework Database
Operation Drug Mgt. (MapReduce) (HBase*)
Mgt. Service
Distributed File System Coordination Service
(HDFS) (ZooKeeper*)
Infrastructure Virtualization Structured Data Collector Log Date Collector
(Sqoop*) (Flume*)
Network Storage Server
Virtualization Virtualization Virtualization
EHR data Repository
Health Information Access Layer (HIAL)
Grassroots
Hospital Hospital Care
26
Institution
27. Use Case: NEXTBIO
Analytics for Genomics Data
• Cost to sequence a genome has fallen by
800x in the last 4 years
• Each genome has ~4 million variants
• Growth in the genomics data in the public
and private domain
• Data available in variety of sources
– Structured, semi-structured, unstructured
• New aggregated data growing
exponentially
Data Interpretation & Commercializing
Sequencing Processing Analytics Targeted
Cloud Storage Therapeutics
Visualization Companion
3 Billion
Base Pairs Millions of Millions of Variants Diagnostics
Variants Millions of Patients Actionable Biomarkers
27
28. Use Case: NEXTBIO
Patient Correlation Data
Novel Discoveries
Biomarkers
Disease Mechanism
Drug Indications
Clinical Trial Parameters
Patient Care Options
Large content repository of public and private genomic data
combined with proprietary and patented correlation engine
28
29. Use Case: NEXTBIO
Nextbio & Intel Collaboration
Technical Challenge:
• Immutable Data – write once,
never change, read many times
• Traditional Bloom Filters works
• Hadoop* & HBase* well suited
1 genome 10 million rows
100 genomes 1billion rows
1M genomes 10 trillion rows
100M genomes 1 quadrillion
1,000,000,000,000,000 rows
• App can dynamically partitions HBase
as data size grows
Intel Optimizations for Hadoop:
• Optimized Hadoop stack in open
source
• Stabilize HBase to provide reliable
scalable deployment
• Optimize and support scale-out as
data size dramatically grows
• Exploring cluster auto tuning,
Security & Compliance, etc.
29
31. Data Trends
World’s Data Kaiser’s
Data 90%
80%
STRUCTURED UNSTRUCTURED STRUCTURED UNSTRUCTURED
UNSTRUCTURED
DATA DATA DATA DATA
DATA
• 80% of world’s data is unstructured • 90% of Kaiser’s data is
(Rise of Mobility devices, and machine unstructured (80% of EHR and Image
generated data) data)
• 44x as much data over the coming • 25x as much data over the coming
decade (35 zettabytes by 2020) decade (One exabyte by 2020)
• Majority of data growth is driven by • Majority of data growth is driven by
unstructured data (Active archives, unstructured data (Medical Images,
Medical images, Online movies and Videos, Text, Voice)
storage, Pictures)
• Information is centric to providing
• Information is centric to new wave Real-time Personalized Healthcare
of opportunities (Retail, Financing, (Requires Contextual – device,
Insurance, Manufacturing, Healthcare,…) environment, spatial, Demographics,
Social and Behavioral profiles in
addition to medical information)
• Industry is employing Big Data
Technologies for Information • Kaiser is evaluating Big Data
extraction Technologies…
31 Source: Kaiser
32. Data Platform Compute Trends –
Distributed Compute
Kaiser is looking to exploit
this capability…
• Structured, Relational • Unstructured, Non-tabular
Tabular Data Data
• Interactive Query Support • Rich Ad Hoc Integration
Slave(s)
• Real-time Analytics • Real-time Analytics
• SQL Transaction Data • UQL ALL Data
Master DAS
Share-Nothing
Distributed Storage and
SAN/NAS Compute ($)
• Fault-tolerant MasterSlave Architecture
In-Memory capable of withstanding partial system failures
(50$)
SAN/NAS • Data is distributed across processing slave
nodes
MPP (10$)
• Resources containing data are not shared
• Master manages the data distribution, job
scheduling across slave nodes and aggregating
SAN/NAS result sets
SMP (Disk Caching, • Integrate built/bought Real-time Predictive
SAN/NAS
High Speed Network) Analytical Solutions or Processing logic
(10$)
SMP (5$)
Discontinuous Change
32
33. Big Data Platform – Requirements
Data Process
Characteristics Characteristics
Information drives process optimizations with
strategic impact. Modeling business intuition Intuition
from data deluge. (Simulation,
Volume Optimization,
Stochastic Optimization)
(Sensors, EMR, Ability to model information and transition from
Claims, Pharmacy,
Images)
multiple access methods to generating, sharing, Information
collaborating and acting on insights anytime, (Standard & Ad Hoc
anywhere on any device. reporting, Query, Alerts,
Forecasting, Access)
Velocity
Support current BI tools focused on structured
(SLAs, Real-time information. Build/buy packaged unstructured Interrogation
Decision Support & data processing and analytics tools. (Clustering, Statistical,
Contextual Quality, Semantics)
Intelligence)
A portfolio of tools to manage (profile,
cleanse, classify, synchronize, aggregate,
Integration
(Alignment, Semantics,
integrate, share) ALL types of data. Completeness, Quality)
Variety
(Structured, Text,
Unstructured, A unified information storage methodology Ingestion
Documents, Images)
enabling users to manage data from ALL sources. (Data Model, Metadata
Reference Data, Store)
33
34. Big Data – Selection Criteria
DATA SIZE Gigabytes, Terabytes, Petabytes
DATA TYPE Structured, Semi-Structured, Unstructured
DATA CLASS Human Generated, Machine Generated
DATA CATALOG Text, Image, Audio, Video
DATA VELOCITY Batch, Streaming
DATA ACCESS Analytics, Search, Transaction (ACID, BASE)
DATABASE TYPE Relational , File Based, Columnar, NoSQL, Document, Graph, RDF
SERVER
ARCHITECTURE SMP, MMP, Distributed Processing
DISTRIBUTED
PROCESSING Appliance, Commodity Cluster (CC) < 1K nodes, CC >1K nodes
STORAGE
ARCHITECTURE NAS, SAN, Direct Access Storage, Spinning Disks, Flash, SSD
Financial, Computer Vision Engine, Geospatial, Machine Learning,
FRAMEWORKS
Mathematical, Natural Language Processing, Neural Networks,
Statistical Modeling, Time-Series Analysis, Voice Engine
ANALYTICS Standard Reporting, Ad hoc Reporting, Query/Drill downs, Alerts
Forecasting, Simulations, Optimization, Stochastic Optimizations
34
35. Agenda
• Healthcare and Big Data Trends
• What is Big Data in Healthcare?
• Big Data Challenges
• Methods to Manage Big Data
• Use Cases
• Summary and Next Steps
35
36. Summary
• We are at an inflection point
in Big Data and analytics in
healthcare
• We need to make Big Data
efficient and accessible
• Focus on innovation, rely on
the ecosystem for services
outside your core competency
• Adopt standards and best
practices leveraging
worldwide models
36
37. Next Steps
Help build the Big Data Health Continuum:
• Create technology-differentiated offerings,
advocating open standards and best practices
• Identify potential customers and ecosystem
partners in core healthcare usage models
• Deliver industry proof points to accelerate
adoption
• Develop joint marketing programs to raise
awareness, amplify our thought leadership and
generate customer value
Together, We Create the Network Effect
37
37
38. Additional Sources of Information
• Big Data and Analytics at Intel - Intel® Big Data and Analytics
• Healthcare Blogs – Intel Healthcare IT Professionals
• Whitepapers
– The Growing Importance of Big Data, Real Time Analytics
– SAP In-Memory Appliance Software: Real-Time Business
Intelligence
– Oracle: Big Data for Enterprise
– Big Data: The next frontier for innovation, competition, and
productivity
• Videos
– SAP-HANA – A Collaboration Between SAP & Intel
38
39. Intel Technologies
• Intel® Virtualization Technology (Intel® VT) – Provides flexibility and maximum system utilization
by consolidating multiple environments into a single server, workstation, or PC
• Intel® vPro™ Technology – Designed specifically for the needs of business, notebooks and desktops
with Intel® vPro™ technology have security and manageability built right into the chip
• Intel® Trusted Execution Technology (Intel® TXT) – Protect confidentiality and integrity of
business data against software-based attacks.
• Intel® Anti-Theft Technology (Intel® AT) – Providing the option to activate hardware-based client-
side intelligence to secure the PC and its data in the event the notebook is lost or stolen
• Intel® AES New Instructions (Intel® AES-NI) – The Advanced Encryption Standard (AES)
algorithm is now widely used across the software ecosystem to protect network traffic, personal data,
and corporate IT infrastructures
• Intel® Identity Protection Technology (Intel® IPT) – Two-factor authentication directly into the
processors of select 2nd generation Intel® Core™ processor-based PCs
• Intel® Cloud Access 360 – Protection Enterprise Access to Cloud and Protecting Enterprise
Applications in the Cloud
• Intel® Expressway Service Gateway – High performance security, xml acceleration and routing.
Cross-domain service mediation, threat prevention, policy enforcement. Interoperable ESB gateway
• McAfee Cloud Security Platform* – Consistent security policies, reporting, and threat intelligence
across all cloud traffic—now available from a single platform
• Intel® Scale-out Storage – Tackle your data center’s challenges with enterprise storage solutions
powered by the world’s most advanced multi-core architecture
• Intel® Solid State Drives – High performance, Self-Encrypting Solid State Drives for protecting
sensitive data at rest
• Intel Unified Networking – Unified Networking enables cost-effective connectivity to the LAN and
the SAN on the same Ethernet fabric
39
41. Legal Disclaimer
• Intel® vPro™ Technology is sophisticated and requires setup and activation. Availability of features and results will
depend upon the setup and configuration of your hardware, software and IT environment. To learn more
visit: http://www.intel.com/technology/vpro.
• Ultrabook Touch/Convertibility: Ultrabook™ products are offered in multiple models. Some models may not be available in
your market. Consult your Ultrabook™ manufacturer. For more information and details, visit
http://www.intel.com/ultrabook .
• Intel® Virtualization Technology (Intel® VT) requires a computer system with an enabled Intel® processor, BIOS, and
virtual machine monitor (VMM). Functionality, performance or other benefits will vary depending on hardware and
software configurations. Software applications may not be compatible with all operating systems. Consult your PC
manufacturer. For more information, visit http://www.intel.com/go/virtualization.
• Intel® AES-NI requires a computer system with an AES-NI enabled processor, as well as non-Intel software to execute the
instructions in the correct sequence. AES-NI is available on select Intel® processors. For availability, consult your reseller
or system manufacturer. For more information, see Intel® Advanced Encryption Standard Instructions (AES-NI)
• Intel® Active Management Technology (Intel® AMT) requires activation and a system with a corporate network
connection, an Intel® AMT-enabled chipset, network hardware and software. For notebooks, Intel AMT may be unavailable
or limited over a host OS-based VPN, when connecting wirelessly, on battery power, sleeping, hibernating or powered off.
Results dependent upon hardware, setup and configuration. For more information, visit Intel® Active Management
Technology.
• Intel® Anti-Theft Technology (Intel® AT): No system can provide absolute security under all conditions. Requires an
enabled chipset, BIOS, firmware and software, and a subscription with a capable Service Provider. Consult your system
manufacturer and Service Provider for availability and functionality. Intel assumes no liability for lost or stolen data and/or
systems or any other damages resulting thereof. For more information, visit http://www.intel.com/go/anti-theft.
• Intel® Trusted Execution Technology (Intel® TXT): No computer system can provide absolute security under all
conditions. Intel® TXT requires a computer with Intel® Virtualization Technology, an Intel TXT enabled processor,
chipset, BIOS, Authenticated Code Modules and an Intel TXT compatible measured launched environment (MLE). Intel TXT
also requires the system to contain a TPM v1.s. For more information, visit http://www.intel.com/technology/security.
• Intel® Identity Protection Technology (Intel® IPT): No system can provide absolute security under all
conditions. Requires an Intel® Identity Protection Technology-enabled system, including a 2nd Generation Intel® Core™
processor enabled chipset, firmware and software, and participating website. Consult your system manufacturer. Intel
assumes no liability for lost or stolen data and/or systems or any resulting damages. For more information, visit
http://ipt.intel.com.
41
42. Risk Factors
The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the
future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,”
“intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking
statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking
statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors
could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the
following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand
could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance
of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns
including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial
conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could
negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by
a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult
to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and
market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing
programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological
developments and to incorporate new features into its products. The gross margin percentage could vary significantly from
expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying
products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and
associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials
or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and
intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in
countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters,
infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and
compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's
products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures.
Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The
succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In
connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are
being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intel's
results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and
by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as
the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an
injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting
Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed
discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most
recent Form 10-Q, report on Form 10-K and earnings release.
Rev. 1/17/13
42