Big Data at the Speed of Business - IBM Innovationen für eine neue Ära

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1 Big Data at the Speed of Business - IBM Innovationen für eine neue Ära Udo Hertz, Director of Information Management Development IBM Deutschland 13. Juni IBM Corporation

2 Agenda 1 IBM s viewpoint Sicht auf on Big big Data data und and Analytics analytics 2 Fünf überzeugende Anwendungsfälle 3 IBM s einzigartiger Beitrag zum Kundenerfolg 4 Empfehlungen für die ersten Schritte 2013 IBM Corporation

3 Auf einem Smarten Planeten verändern umwälzende technologische Faktoren die Geschäfts- und IT-Welt Cloud Computing Mobile Social Media Internet of Things

4 Data Big Data is the ist New der neue Oil Rohstoff der Wirtschaft We have for the first time an economy based on a key resource [Information] that is not only renewable, but self-generating. Running out of it is not a problem, but drowning in it is. John Naisbitt Um einen Rohstoff zu nutzen, braucht es Mining, Refining und Delivering 4 IBM Confidential

5 Agenda 1 IBM s Sicht auf Big Data und Analytics 2 Fünf überzeugende Anwendungsfälle Five compelling big data use cases 3 IBM s einzigartiger Beitrag zum Kundenerfolg 4 Empfehlungen für die ersten Schritte 2013 IBM Corporation

6 Fünf Anwendungsfälle und ihre Erfolgszahlen Enrich Your Information Base with Big Data Exploration 99% Reduction In Time Required For Analysis Improve Customer Interaction with Enhanced 360º View of the Customer 1,100 Association Publishing Partnerships Prevent Threat and Fraud with Security and Intelligence Extension 42TB Real-time Acoustic Data Analyzed Optimize Infrastructure and Monetize Data with Operations Analysis Gain IT efficiency and scale with Data Warehouse Augmentation 60K Metered Customers in Five States 40X Gain in Analysis Performance

7 IBM Big Data Innovationen im 1. Halbjahr 2013 InfoSphere Big Insights & Streams Enhanced Big Data Platform advances in consumability and performance DB2 with BLU Acceleration Speed of Thought Analytics System for Analytics New Model The fastest performance of Netezza Technology to date System for Hadoop Explore and analyze Hadoop data with appliance simplicity

8 Jede Branche kann Big Data sinnvoll einsetzen Optimizing Offers and Cross-sell Customer Service and Call Center Efficiency Fraud Detection & Investigation Credit & Counterparty Risk 360 View of Domain or Subject Catastrophe Modeling Fraud & Abuse Producer Performance Analytics Analytics Sandbox Pro-active Call Center Network Analytics Location Based Services Smart Meter Analytics Distribution Load Forecasting/Scheduling Condition Based Maintenance Create & Target Customer Offerings Business process transformation Audience & Marketing Optimization Multi-Channel Enablement Digital commerce optimization Actionable Customer Insight Merchandise Optimization Dynamic Pricing Customer Analytics & Loyalty Marketing Predictive Maintenance Analytics Capacity & Pricing Optimization Shelf Availability Promotional Spend Optimization Merchandising Compliance Promotion Exceptions & Alerts Civilian Services Defense & Intelligence Tax & Treasury Services Measure & Act on Population Health Outcomes Engage Consumers in their Healthcare!! %# "#$ Advanced Condition Monitoring Data Warehouse Optimization Actionable Customer Intelligence Operational Surveillance, Analysis & Optimization Data Warehouse Consolidation, Integration & Augmentation Big Data Exploration for Interdisciplinary Collaboration Uniform Information Access Platform Data Warehouse Optimization Airliner Certification Platform Advanced Condition Monitoring (ACM) Customer/ Channel Analytics Advanced Condition Monitoring Increase visibility into drug safety and effectiveness

9 Agenda 1 IBM s Sicht auf Big Data und Analytics 2 Fünf überzeugende Anwendungsfälle 3 IBM s einzigartiger Beitrag zum Kundenerfolg IBM s unique value for client success 4 Empfehlungen für die ersten Schritte

10 Die Vorteile der IBM Big Data Platform Systems Management Hadoop System BIG BIG DATA DATA PLATFORM PLATFORM Application Development Accelerators Stream Computing Discovery Data Warehouse Information Integration & Governance Warum braucht es eine Plattform? Die meisten Big Data-Anwendungsfälle brauchen eine Kombination von Technologien IBMs Big Data Platform verknüpft etablierte Technologien mit Big Data Innovationen und bildet damit die einzige Big Data Plattform Verschiedene Deployment-Modelle Data Media Content Machine Social

11 IBM bietet einen ganzheitlichen und integrierten Ansatz für Big Data und Analytics Performance Management Model Development ANALYTICS Risk Analytics Decision Management Content Analytics Business Intelligence and Predictive Analytics Visualization and Exploration BIG DATA PLATFORM Integration and Governance Only IBM has expanded and evolved Analytics for Big Data to Fuel all decision-making with powerful analytics Broaden analytic adoption without silos or programming Analyze all data wherever it lives Accelerate business value with solutions that have built-in analytics expertise so organizations can find what is business relevant in big data and make it instantly actionable

12 IBM Big Data Innovationen im 1. Halbjahr 2013 InfoSphere Big Insights & Streams Enhanced Big Data Platform advances in consumability and performance DB2 with BLU Acceleration Speed of Thought Analytics System for Analytics New Model The fastest performance of Netezza Technology to date System for Hadoop Explore and analyze Hadoop data with appliance simplicity

13 Innovationen im 1. Halbjahr 2013 Verbesserungen der Big Data Platform bei Handhabung und Performance InfoSphere BigInsights 2.1 For exploration, analysis & archiving large volumes and variety of data Data at Rest InfoSphere Streams 3.1 For real-time analysis of data in motion Data in Motion Hadoop System Big SQL GPFS-FPO High Availability Stream Computing Increased performance Developer enhancements Simplified large scale deployments Enhanced integration

14 Innovationen im 1. Halbjahr 2013 Introducing BLU Acceleration IBM Research & Development Lab Innovations Dynamic In-Memory In-memory columnar processing with dynamic movement of unused data to storage Actionable Compression Industry s first data compression that preserves order so that the data can be used without decompressing Parallel Vector Processing Multi-core and SIMD parallelism (Single Instruction Multiple Data) Data Skipping Skips unnecessary processing of irrelevant data BLU Acceleration Super Super Fast, Fast, Super Super Easy Easy Create, Load Load and and Go! Go! No No Indexes, No No Aggregates, No No Tuning, Tuning, No No SQL SQL changes, No No schema changes

15 Innovationen im 1. Halbjahr 2013 Die bisher beste Leistung der Netezza-Technologie Accelerate Performance of Analytic Queries e.g. 3X faster performance 1 Increase Efficiency of your Data Center $#! New Model e.g. 50% greater data capacity per rack 2 Simplicity and Ease of Administration e.g. improved resilience 1 Based on a comparison of the IBM PureData System for Analytics N2001 to the IBM PureData System for Analytics N1001. The performance speed refers to the query times on both macro-analytic and mixed workload tests as conducted in IBM engineering lab benchmarks. The N2001 query times were an average of 3x faster than those of the N1001. Individual results may vary. 2 Capacity of IBM PureData System for Analytics N2001 compared to previous generation IBM PureData System for Analytics N1001.

16 Innovationen im 1. Halbjahr 2013 Qualitativ beste Data Services für Big Data zenterprise #$ for exploration & online archiving % &!! for reporting & analytics and operational analytics % & for SQL & NoSQL transactions with enhanced Hadoop integration in DB2 11 (beta)g $ for highest performance transactions with enhanced Hadoop integration in IMS 13 (beta)

17 Innovationen im 1. Halbjahr 2013 Erfasst und analysiert Hadoop-Daten mit der Einfachheit einer Appliance Accelerate Big Data Time to Value e.g. Deploy 8X faster than custom-built 1 $# Simplify Big Data Adoption & Consumption e.g. Single system console Simplicity and Ease of Administration e.g. Only integrated Hadoop system with built-in archiving tools 2 1 Based on IBM internal testing and customer feedback. "Custom built clusters" refer to clusters that are not professionally pre-built, pre-tested and optimized. Individual results may vary. 2 Based on current commercially available Big Data appliance product data sheets from large vendors. US ONLY CLAIM.

18 Agenda 1 IBM s Sicht auf Big Data und Analytics 2 Fünf überzeugende Anwendungsfälle 3 IBM s einzigartiger Beitrag zum Kundenerfolg 4 Empfehlungen für die ersten Schritte Recommendations on how to get started

19 Drei kritische Erfolgsfaktoren 20 STRATEGY & VALUE What are the key business issues or opportunities that Big Data can help me to address? TECHNOLOGY What are the essential capabilities we need to ensure we have in place? PEOPLE & PROCESS What skills and processes do I need to add or modify to be successful?

20 Weitere Informationen For additional information including whitepapers and demos, please visit: Big Data Hub Smarter Analytics Reference: Big Data for Smarter Decision Making by Colin White Big Data Analytics - TDWI ebook IBV Study - Education: Social Media Analytics, YouTube video Understanding Big Data ebook Free online education at bigdatauniversity.com Services: Develop your Big Data strategy with help from IBM Global Business Services

21 ibm.com/bigdata

22 Legal Disclaimer IBM Corporation All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. If the text contains performance statistics or references to benchmarks, insert the following language; otherwise delete: Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. 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Subsequent references can drop IBM but should include the proper branding (e.g., Lotus Sametime Gateway, or WebSphere Application Server). Please refer to for guidance on which trademarks require the or symbol. Do not use abbreviations for IBM product names in your presentation. All product names must be used as adjectives rather than nouns. Please list all of the trademarks that you use in your presentation as follows; delete any not included in your presentation. IBM, the IBM logo, Lotus, Lotus Notes, Notes, Domino, Quickr, Sametime, WebSphere, UC2, PartnerWorld and Lotusphere are trademarks of International Business Machines Corporation in the United States, other countries, or both. Unyte is a trademark of WebDialogs, Inc., in the United States, other countries, or both. 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