Big Data Solutions forHealthcareWayne Wu, Global Health Solution Architect, IntelHubert Ding, Healthcare Solution Architec...
2
Agenda    • Healthcare and Big Data Trends    • What is Big Data in Healthcare?    • Big Data Challenges    • Methods to M...
Agenda    • Healthcare and Big Data Trends    • What is Big Data in Healthcare?    • Big Data Challenges    • Methods to M...
We are at an Inflection Point in    Healthcare - TRENDS                                           % of population over age...
We are at an Inflection Point in     Healthcare - TRENDS                 Storage Growth              Total Data Healthcare...
Agenda    • Healthcare and Big Data Trends    • What is Big Data in Healthcare?    • Big Data Challenges    • Methods to M...
Big Data in Healthcare    Where is the data coming from?                                            2. Clinical Decision S...
Big Data Solution for Healthcare       Health Info                             Personal Health                           P...
Agenda     • Healthcare and Big Data Trends     • What is Big Data in Healthcare?     • Big Data Challenges     • Methods ...
Big Data Challenges are More than Data Size...     And Require New Technologies                         Lab results, billi...
Agenda     • Healthcare and Big Data Trends     • What is Big Data in Healthcare?     • Big Data Challenges     • Methods ...
All Eyes on Data for Value       Big Data Storage Considerations                                                Traditiona...
All Eyes on Data for Value                                                             Data Center Provisioning           ...
Enterprise Big Data Architecture     STRUCTURED                                                                           ...
Big Data Architectural Framework                                                                                          ...
Big Data Architectural Framework                                                       Data as a Services                 ...
Big Data Architectural Framework                                                                                          ...
Accessing Big Data (Clients)      “Know Me”                   “Free Me”             “Express Me”          “Link Me”       ...
Building on the Ecosystem     Database and Analytics Environments Optimized on Intel                                      ...
Intel® Products and Software For Big Data                                            Scaling               Compute        ...
Examples of Intel-powered Servers in     Big Data and Analytics     Cisco* UCS Server1                                   D...
Big Data Applications in Healthcare (PRC)                                        2. Clinical     •药品研发                    ...
Agenda     • Healthcare and Big Data Trends     • What is Big Data in Healthcare?     • Big Data Challenges     • Methods ...
Use Case: Regional Health Info Network – China     Real-time Clinical Decision Support                                    ...
RHIN/Grassroots Solution with Big Data (Hadoop*)       Integrated User Interface(Citizen, Physician, Health Authority)    ...
Use Case: NEXTBIO     Analytics for Genomics Data      •    Cost to sequence a genome has fallen by           800x in the ...
Use Case: NEXTBIO     Patient Correlation Data                                                Novel Discoveries           ...
Use Case: NEXTBIO     Nextbio & Intel CollaborationTechnical Challenge:•    Immutable Data – write once,     never change,...
Use Case: Big Data at Kaiser Permanente30
Data Trends                 World’s Data                                       Kaiser’s                                   ...
Data Platform Compute Trends –     Distributed Compute                                                  Kaiser is looking ...
Big Data Platform – Requirements     Data                                                                    Process Chara...
Big Data – Selection Criteria       DATA SIZE     Gigabytes, Terabytes,   Petabytes       DATA TYPE     Structured, Semi-S...
Agenda     • Healthcare and Big Data Trends     • What is Big Data in Healthcare?     • Big Data Challenges     • Methods ...
Summary     • We are at an inflection point       in Big Data and analytics in       healthcare     • We need to make Big ...
Next Steps     Help build the Big Data Health Continuum:     •   Create technology-differentiated offerings,         advoc...
Additional Sources of Information     •   Big Data and Analytics at Intel - Intel® Big Data and Analytics     •   Healthca...
Intel Technologies     • Intel® Virtualization Technology (Intel® VT) – Provides flexibility and maximum system utilizatio...
Legal DisclaimerINFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED...
Legal Disclaimer     •   Intel® vPro™ Technology is sophisticated and requires setup and activation. Availability of featu...
Risk Factors The above statements and any others in this document that refer to plans and expectations for the first quart...
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Big Data Solutions for Healthcare

  1. 1. Big Data Solutions forHealthcareWayne Wu, Global Health Solution Architect, IntelHubert Ding, Healthcare Solution Architect, IntelBIGS001
  2. 2. 2
  3. 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 Guide3
  4. 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 Steps4
  5. 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 growthSource: McKinsey Global Institute AnalysisESG Research Report 2011 – North American Health Care Provider Market Size and Forecast5
  6. 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 2009Source: McKinsey Global Institute AnalysisESG Research Report 2011 – North American Health Care Provider Market Size and Forecast6
  7. 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 Steps7
  8. 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 Analysis8
  9. 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 Acceleration9
  10. 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 Steps10
  11. 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 opportunities11
  12. 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 Steps12
  13. 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 Caching13
  14. 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 Platforms14
  15. 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 WarehouseUNSTRUCTURED Visualize Social & Web No-SQL Spreadsheets INSIGHTS Legacy APPS Document In Memory DB Types SQL WebTranscriptions & Apps Notes RDBMS MashUps15
  16. 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 Characteristics16
  17. 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 Characteristics17
  18. 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 Characteristics18
  19. 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 Manageability19
  20. 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 hardware20
  21. 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 – 40GBe21
  22. 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-Data3 http://www.itp.net/mobile/588145-oracle-unveils-exalytics-in-memory-machine22
  23. 23. Big Data Applications in Healthcare (PRC) 2. Clinical •药品研发 Decision •临床数据比对 对药品实际 作用进行分析;实 1. Pharma/Life Support & 匹配同类型的病人,用药 施药品市场预测 Sciences Trends •临床决策支持 •基因测序 利用规则和数据实时分析给 •分布式计算加快基因测序计算 (includes 出智能提示 效率 Diagnostic Imaging) •远程监控 •公共卫生实时统计分析 采集并分析病人随身携带仪 发现公共卫生疫情及公民健康 4. Patient 器数据,给出智能建议 状况 3. Claims, Behavior/ •人口统计学分析 •新农合基金数据分析 Utilization & Social 对不同群体人群的就医,健 及时了解基金状况,预测风险 Fraud 康数据实施人口统计分析 辅助制定农合基金的起付线, Networking 赔付病种等 •了解病人就诊行为 发现病人的特定就诊行为, •基本药物临床应用分析 分配医疗资源 分析基本药物在处方中的比例23
  24. 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 Steps24
  25. 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 RHIN25
  26. 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 Care26 Institution
  27. 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 Biomarkers27
  28. 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 engine28
  29. 29. Use Case: NEXTBIO Nextbio & Intel CollaborationTechnical 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 growsIntel 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
  30. 30. Use Case: Big Data at Kaiser Permanente30
  31. 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. 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 Change32
  33. 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 fromClaims, 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  InterrogationDecision 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  IngestionDocuments, Images) enabling users to manage data from ALL sources. (Data Model, Metadata Reference Data, Store)33
  34. 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 Optimizations34
  35. 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 Steps35
  36. 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 models36
  37. 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 Effect3737
  38. 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 & Intel38
  39. 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 fabric39
  40. 40. Legal DisclaimerINFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED,BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT ASPROVIDED IN INTELS TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVERAND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDINGLIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANYPATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT.• A "Mission Critical Application" is any application in which failure of the Intel Product could result, directly or indirectly, in personal injury or death. SHOULD YOU PURCHASE OR USE INTELS PRODUCTS FOR ANY SUCH MISSION CRITICAL APPLICATION, YOU SHALL INDEMNIFY AND HOLD INTEL AND ITS SUBSIDIARIES, SUBCONTRACTORS AND AFFILIATES, AND THE DIRECTORS, OFFICERS, AND EMPLOYEES OF EACH, HARMLESS AGAINST ALL CLAIMS COSTS, DAMAGES, AND EXPENSES AND REASONABLE ATTORNEYS FEES ARISING OUT OF, DIRECTLY OR INDIRECTLY, ANY CLAIM OF PRODUCT LIABILITY, PERSONAL INJURY, OR DEATH ARISING IN ANY WAY OUT OF SUCH MISSION CRITICAL APPLICATION, WHETHER OR NOT INTEL OR ITS SUBCONTRACTOR WAS NEGLIGENT IN THE DESIGN, MANUFACTURE, OR WARNING OF THE INTEL PRODUCT OR ANY OF ITS PARTS.• Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined". Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information.• The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.• Intel product plans in this presentation do not constitute Intel plan of record product roadmaps. Please contact your Intel representative to obtain Intels current plan of record product roadmaps.• Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor family, not across different processor families. Go to: http://www.intel.com/products/processor_number.• Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order.• Copies of documents which have an order number and are referenced in this document, or other Intel literature, may be obtained by calling 1-800-548-4725, or go to: http://www.intel.com/design/literature.htm• Intel, Xeon, Core, Phi, vPro, Sponsors of Tomorrow and the Intel logo are trademarks of Intel Corporation in the United States and other countries.• *Other names and brands may be claimed as the property of others.• Copyright ©2013 Intel Corporation.40
  41. 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. 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 Intels 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 Intels products; actions taken by Intels 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. Intels 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 Intels 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. Intels 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 Intels 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. 1/17/1342
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