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Big Data & Analytics Architecture

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This presentation covers a solution "Advanced Analytics Platform" for Telecommunication organizations.

This presentation covers a solution "Advanced Analytics Platform" for Telecommunication organizations.

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  • 1. Advanced Analytics Platform Deep Dive Components, Patterns, Architecture Decisions ISA-3637 (Tue Nov 5 11:15 AM – 12:15 AM) Dr. Arvind Sathi asathi@us.ibm.com Richard Harken rharken@us.ibm.com Tommy Eunice teunice@us.ibm.com Mathews Thomas Mathews@us.ibm.com © 2013 IBM Corporation
  • 2. Please note IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.
  • 3. Acknowledgements and Disclaimers Availability. 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. The workshops, sessions and materials have been prepared by IBM or the session speakers and reflect their own views. They are provided for informational purposes only, and are neither intended to, nor shall have the effect of being, legal or other guidance or advice to any participant. While efforts were made to verify the completeness and accuracy of the information contained in this presentation, it is provided AS-IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this presentation or any other materials. Nothing contained in this presentation 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. All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. 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. © Copyright IBM Corporation 2013. All rights reserved. •U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. •Please update paragraph below for the particular product or family brand trademarks you mention such as WebSphere, DB2, Maximo, Clearcase, Lotus, etc IBM, the IBM logo, ibm.com, [IBM Brand, if trademarked], and [IBM Product, if trademarked] are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol (® or ™), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at www.ibm.com/legal/copytrade.shtml If you have mentioned trademarks that are not from IBM, please update and add the following lines: [Insert any special 3rd party trademark names/attributions here] Other company, product, or service names may be trademarks or service marks of others.
  • 4. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 5. AAP – Telecommunications Use Cases Industry Imperatives MAJOR use cases Create & Deliver Smarter Services Transform Operations Location Based Services IT Infrastructure Transformation (Traditional to Big Data) Cross Industry Solutions Voice & Data Fraud Build Smarter Networks Personalize Customer Engagements Network Analytics Pro Active Call Center Network Infrastructure Planning (Performance, Capacity, Usage) Customer Data/Location Monetization Product Knowledge Hub Smarter Campaigns Customer Knowledge Hub Social Media Insight Emerging Use Cases  Smarter Advertising  Customized Customer Marketing  3rd Party API’s  Cloud services for SMEs, enterprises  Contactless services (payments and banking)  M2M (smart cars, eHealth)  Tiered Services  Big Data Scale  Investment Decisions  Lower storage requirements  Smarter Returns  Analyze data before it lands – then store only what you need  New analytic models  Share critical information across the enterprise vs. deliver multiple copies of the data  Traditional Infrastructure Optimization  Product Knowledge Hub  Content Network Distribution  Proactive Device Management  Network Fault Prevention  ICTO (Energy Savings)  Real Time Traffic Optimization  Network Abuse from excessive data users  Discrete on-line charging for quality of experience  Real time automated capacity management for dropped calls  SON Capacity Management for special events (traffic offload)  Service Migration  Social Advocacy  Cross Offering Transparency  Smarter Customer Interaction & Engagement  Real-time Customer Experience Insight  Smarter Campaigns  Customer Retention  Micro Segmentation Marketing  Next Best Offer  Retail cross Channel optimization
  • 6. How to turn streaming noisy Telco Location data into meaningful location, then discover customer insights Location Pattern Analytics Stream data Call Detail Records SMS Voice GPS Tracking Wifi off load Reference Data Cell Tower Wifi AP Maps GIS, POI Special Service Numbers e.g bank, 1-800 Big Data Integration Mobile Location Data Processing: Map mapping, Business rules et. Spatio-Temporal Event Association Analysis Analyzable Location Event Meaningful Location Data Who, when, where and what subscriberId: home: subscriberId: Work: Timestamp: POIs & period … Position: latitude + Sequence of longitude meaningful Precision: 0~2 km Locations… Direction: nullable Commute means: Speed: nullable car/subway/bus Activity : nullable Micro segmentaton Business traveler Regular commuter Heavy driver Social Butterfly Mom ….. Location Patterns on Individual and Group level Every Sunday noon, Bob goes to xxx mall to shopping and has lunch Every Thursday afternoon, Bob goes to customer site at XXX …..
  • 7. Mobile Couponing Use Case 1) Contacts Offertel Communications to run campaign for a new store next to a movie theater 2) Opts-in to receive mobile coupons from the Telco 7) Posts on twitter, Facebook public fan page for Cuppa Heaven Telco Customer Profile Campaign Delivery System 6A) Receives mobile 6A) Receives coupon for new 6B) Deliver mobile coupon Cuppa Heaven store Coupons to for new Cuppa mobile opt-out Heaven store clients via email & web site 7) Monitor Campaign Performance 5) Priority list transferred to conduct campaign Advanced Analytics Platform Customer Action Telco clients who have opted out of Mobile Cuppa Heaven/Offertel Action coupons 3) Use Social media to establish “Opinion Leaders”, potential coffee drinkers, movie goers 4) Driving habits, coffee preference, & opinion leaders used to prioritize customer target list
  • 8. AAP – Media and Entertainment Use Cases Organizational FOCUS areas Create differentiated customer experiences “Connected Consumer” Build an agile digital supply chain “Smarter Media” Audience & Marketing Optimization Industry Team use cases Operations Analysis & Optimization Multi-Channel Enablement Business Process Transformation Infrastructure Mgmt & Security Digital Commerce Optimization (sales play) 360o View of the Customer Customer & Market Insight MAJOR use cases Advertising Optimization Media, Metadata & Optimization. •Social Profiling/ Sentiment Analysis •Churn Optimization •Customer Care Optimization •Audience/ Viewing Duplication •Audience Composition Index •Multi-Platform Ad Performance •Advertiser Revenue Analysis •Real Time Audience Targeting •CRM Optimization •Real-time ad targeting •Ad inventory Optimization •Real-time ad reporting •Search engine optimization •Campaign optimization (in-flight) •Marketing campaign effectiveness •Network & infrastructure optimization •Network Demand Forecasting •Content optimization •Content demand forecasting •IP Rights Optimization
  • 9. AAP for Real-time Bidding of Advertisements Telco Website Content Provider Turn Telco Flex Tag TURN DMP Location Events / xDR Telco Data Usage Data Integration Campaign Feedback Customer Predictive Models TURN DSP Campaign Mgmt Advanced Analytics Platform Real-time Scoring Bid Req Customer Data Campaign Details Analytics Visualization Additional data (e.g. Offer acceptance, location) Offer & Response Bid Req Offer & Response
  • 10. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 11. New Architecture to Leverage All Data and Analytics Streams Data in Motion Information Ingestion and Operational Information  Stream Processing  Data Integration  Master Data Data at Rest Intelligence Analysis Real-time Analytics     Video/Audio Network/Sensor Entity Analytics Predictive Landing Area, Analytics Zone and Archive Exploration , Integrated Warehouse , and Mart Zones Decision Management BI and Predictive Analytics Navigation and Discovery Data in Many Forms Information Governance, Security and Business Continuity
  • 12. AAP Capabilities IBM Big Data Advanced Analytics Platform (AAP) Architecture Continuous Feed Sources Data Repositories External Data 3rd party Visualize, explore, investigate, search and report High Volume Data for Historical Analysis Model Creation Capture Changes Event Execution Open API Discovery Analytics Take action on analytics Campaign Mgmt. Pro-active Customer Experience Management Pro-active Network Mgmt Deploy Model Policy Mgmt Real time Scoring & Decision Mgmt. Policy Management ... B D In Database Mining Database Server Batch Data A Semi Structured Data Analytics Engine UnStructured E Data Structured Data Hadoop Enterprise Data Warehouse Search, Pattern Matching, Quantitative, Qualitative F Insight Advanced Analytics Platform Create & Deliver Smarter Services G High Performance Unstructured Data analysis Actions Deduplicate Data Integration ETL F C Prediction / Policy Engine Standardize Identity Resolution Outcome Optimization E Customer Activities Historical Data Models Deploy Model High Velocity Social Real-time scoring, classification, detection and action Streaming Engine Network Policies Customer Data Model Based Predictive Analytics Sense, Categorize, Score, Identify, Count, Decide Align Focus Streaming Data XDR Application & Usage Data B D Network Topology Data High Performance Historical analysis C Network Events A Transform Operations Build Smarter Networks Customer Care Reports & Dashboards Ad-hoc Queries Simulation Marketing Reports Network Planning Dashboards ... NOC/SOC Geo/Sem antic Mapping Information Interaction Users Personalize Customer Engagements G
  • 13. AAP Capabilities IBM Big Data Advanced Analytics Platform (AAP) Architecture Continuous Feed Sources Data Repositories External Data 3rd party F G High Performance Unstructured Data analysis Discovery Analytics Take action on analytics Customer Activities Event Execution Streaming Engine Historical Data Models Deploy Model High Velocity Social Visualize, explore, investigate, search and report Sense, Categorize, Score, Identify, Count, Decide Align Focus Streaming Data Network Policies Customer Data Model Based Predictive Analytics Real-time scoring, classification, detection and action E InfoSphere Streams XDR Application & Usage Data B D Network Topology Data High Performance Historical analysis C Network Events A High Volume DataSPSS for Historical Analysis Model Creation Capture Changes BPM WODM, Optim Pro-active Network Mgmt Deploy Model Policy Mgmt Real time Scoring & Decision Mgmt. WODM B In Database Mining Database Server Batch Data PDA Semi A Structured Data Analytics Engine UnStructured Structured Data Data E InfoSphere PDOA BigInsights Hadoop Enterprise Data Warehouse Search, Pattern InfoSphere Social Media Matching, Quantitative, Qualitative F Insight Data Explorer Analytics Advanced Analytics Platform Create & Deliver Smarter Services Pro-active Customer Experience Management Policy Management ... Actions Deduplicate Data Integration ETL Open API C Prediction / Policy Engine Data Stage Quality Stage Standardize MDM Identity Resolution Outcome Optimization IBM (Unica) Campaign Mgmt. Campaign Transform Operations Build Smarter Networks D Cognos Customer Care Reports & Dashboards Ad-hoc Queries SPSS Simulation Reports Dashboards Marketing Network Planning ... NOC/SOC Geo/Sem antic Mapping Information Interaction Users Personalize Customer Engagements G
  • 14. AAP Capabilities Capabilities Overview Capability Streaming Engine Prediction / Policy Engine Database Server Insight Information Interaction Capability Description  Align diverse streams of data, identify customers, align to IDs, sense data importance  Categorize incoming data, use window counts to aggregate atomic data or threshold vioilations, focus attention on monitored situations abstracted from raw events  Use scoring models developed by prediction engine to score observations, activities, customers, etc. in real time  Make data ready for execution of events – e.g., designing campaign messages based on information available.  Includes TEDA and geo-spatial accelerators      Create models using historical data sources Optimize outcomes by promoting best model for a particular treatment (Champion / Challenger) Manage policies associated with decisions – e.g., WODM decision rules, Optim data policies, etc. Includes SPSS Deployment Server Includes SPSS location analytics     Provide capabilities for storage of structured, unstructured and semi-structured data Provide capabilities for analytics using DB functions (e.g., SPSS model development) Provide capabilities for data archival using archival policies Includes Optim / DS for archival policy execution  Deep analysis of consumer behavior is performed to mine data for model creation  Includes unstructured search, pattern matching using arbitrarily defined patterns, qualitative analytics, quantification of data (e.g., sentiment analysis)  Includes Big Insights accelerators  Perform Ad hoc queries, standard reports, dash board  Run simulation models, what-if analysis  Geo-spatial and semantic viewing of data
  • 15. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 16. Mature Organizations are Looking for Instantaneous Insight from Data Speed to insight Respondents were asked how quickly business users require data to be available for analysis or within processes. Box placement reflects the prevalence of that requirements within each a stage. Total respondents n = 973 16
  • 17. Stream Computing Represents a Paradigm Shift Traditional Computing Stream Computing Historical fact finding Current fact finding Find and analyze information stored on disk Analyze data in motion – before it is stored Batch paradigm, pull model Low latency paradigm, push model Query-driven: submits queries to static data Data driven – bring data to the analytics Real-time Analytics 17
  • 18. Massively scalable stream analytics Deployments Linear Scalability • Clustered deployments – unlimited scalability Source Adapters Automated Deployment • Automatically optimize operator deployment across nodes Performance Optimization • Parallel & pipeline operations • Efficient multi-threading Analytics on Streaming Data • Analytic accelerators for a variety of data types • Optimized for real-time performance 18 Analytic Operators Sink Adapters Streams Studio IDE Automated and Optimized Deployment Streaming Data Sources Streams Runtime Visualization
  • 19. Big Data in Real Time with InfoSphere Streams Filter / Sample Modify Analyze Fuse Classify Score 19 Windowed Aggregates Annotate
  • 20. AAP Capabilities IBM Big Data Advanced Analytics Platform (AAP) Architecture Continuous Feed Sources Data Repositories External Data 3rd party F G High Performance Unstructured Data analysis Discovery Analytics Take action on analytics Customer Activities Event Execution Streaming Engine Historical Data Models Deploy Model High Velocity Social Visualize, explore, investigate, search and report Sense, Categorize, Score, Identify, Count, Decide Align Focus Streaming Data Network Policies Customer Data Model Based Predictive Analytics Real-time scoring, classification, detection and action E InfoSphere Streams XDR Application & Usage Data B D Network Topology Data High Performance Historical analysis C Network Events A High Volume DataSPSS for Historical Analysis Model Creation Capture Changes BPM WODM, Optim Pro-active Network Mgmt Deploy Model Policy Mgmt Real time Scoring & Decision Mgmt. WODM B In Database Mining Database Server Batch Data PDA Semi A Structured Data Analytics Engine UnStructured Structured Data Data E InfoSphere PDOA BigInsights Hadoop Enterprise Data Warehouse Search, Pattern InfoSphere Social Media Matching, Quantitative, Qualitative F Insight Data Explorer Analytics Advanced Analytics Platform Create & Deliver Smarter Services Pro-active Customer Experience Management Policy Management ... Actions Deduplicate Data Integration ETL Open API C Prediction / Policy Engine Standardize Identity Resolution Outcome Optimization IBM (Unica) Campaign Mgmt. Campaign Transform Operations Build Smarter Networks D Cognos Customer Care Reports & Dashboards Ad-hoc Queries SPSS Simulation Reports Dashboards Marketing Network Planning ... NOC/SOC Geo/Sem antic Mapping Information Interaction Users Personalize Customer Engagements G
  • 21. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 22. What is Sensitive Data Personally Sensitive • Information that can be misused to harm a person in financial, employment or social way. (Names, Social Security Number, Credit Card, etc.) Network Sensitive • Information that can be misused to breech or disable critical network communication (Circuit Identifiers, IP Addresses, etc.) Corporate Sensitive • Information that can misused to compromise the competitive position of a company (Operational Metrics, etc.)
  • 23. 6 steps that work together to achieve an acceptable and manageable level of data security Assess Risk Audit Define process Processes & Information assets Manage Implement Controls
  • 24. Data masking requires a combination of process, templates and tools Our approach brings together data masking infrastructure using DataStage and ProfileStage, combining with Masking on Demand plug-in using Optim technology. Reusable Processes Identify Select Verify Implement Validate Templates Masking Utilities Data Definitions - Incremental Autogen - Swap - Relational Group Swap - String Replacement - Universal Random - Customer ID - Name - Address - Credit Card No - Social Sec No - Etc. Tools InfoSphere Analyzer Optim, DataStage
  • 25. AAP Capabilities IBM Big Data Advanced Analytics Platform (AAP) Architecture Continuous Feed Sources Data Repositories External Data 3rd party F G High Performance Unstructured Data analysis Discovery Analytics Take action on analytics Customer Activities Event Execution Streaming Engine Historical Data Models Deploy Model High Velocity Social Visualize, explore, investigate, search and report Sense, Categorize, Score, Identify, Count, Decide Align Focus Streaming Data Network Policies Customer Data Model Based Predictive Analytics Real-time scoring, classification, detection and action E InfoSphere Streams XDR Application & Usage Data B D Network Topology Data High Performance Historical analysis C Network Events A High Volume DataSPSS for Historical Analysis Model Creation Capture Changes BPM WODM, Optim Pro-active Network Mgmt Deploy Model Policy Mgmt Real time Scoring & Decision Mgmt. WODM B In Database Mining Database Server Batch Data PDA Semi A Structured Data Analytics Engine UnStructured Structured Data Data E InfoSphere PDOA BigInsights Hadoop Enterprise Data Warehouse Search, Pattern InfoSphere Social Media Matching, Quantitative, Qualitative F Insight Data Explorer Analytics Advanced Analytics Platform Create & Deliver Smarter Services Pro-active Customer Experience Management Policy Management ... Actions Deduplicate Data Integration ETL Open API C Prediction / Policy Engine Standardize Identity Resolution Outcome Optimization IBM (Unica) Campaign Mgmt. Campaign Transform Operations Build Smarter Networks D Cognos Customer Care Reports & Dashboards Ad-hoc Queries SPSS Simulation Reports Dashboards Marketing Network Planning ... NOC/SOC Geo/Sem antic Mapping Information Interaction Users Personalize Customer Engagements G
  • 26. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 27. Buddies, Hangouts, Globtrotters 10 Top Hangouts Areas of mobility analytics  Individual Lifestyle and Usage profiles  Popular Locations with specific profiles  Who are the Buddies  Predicting where people go Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Sofa Surfer Mobile ID Buddy Rank 2702 1 1256 2 8786 3 4792 4 8950 5 © 2012 IBM Corporation
  • 28. What are Profiles • Lifestyle Profiles are defined by marketing analysts for specific use cases or marketing programs • Usage Profiles are created using data mining algorithms and define how a person uses services during the day • Location Affinity is created with algorithms and determines preferred locations for individuals throughout the day and week • Together these uniquely define a person with relation to how the retailer or marketer might want to market to them
  • 29. Creating Groups of Mobility Profiles Enables Better Prediction for Certain Groups  profiles breakdown like this  Homebody, doesn't visit too many unique locations  Daily Grinder, back and forth to work, quiet weekends, makes stops along the way  Norm Peterson, inside the lines, no deviations  Delivering the goods, no predictable patterns, many different locales during the day  Globe Trotter, either not in town, or keeps their phone turned off  Rover Wanderer, spends evenings at various location (sofa surfers www.couchsurfing.org)  “Other”, is a group hard to categorize
  • 30. By Profile, when is it easy or difficult to predict where they will be? Profile Day Time Predictability Daily Grinder Thursday Dinner Highest Daily Grinder Friday Afternoon Lowest Homebody Saturday Night Highest Homebody Wednesday Morning Lowest These are the 2 most predictable profiles, yet there is diversity in their predictability. To best communicate with Daily Grinders, contact them on Thursday Afternoons just before dinner
  • 31. Preferred Locations of by profile type at Lunchtime Weekdays (Central Stockholm) Daily Grinders Night Shifters Delivering the Goods
  • 32. What analysis is available (Anonymous Data) From the mobility profiles, summarized, anonymous analysis is available  Summarized to ensure anonymity, analysis of popular locations by time of day and profile of subscribers is possible    For retailers this information can help understand what types of people are nearby at lunch time What types of people prefer which areas. Some obvious results are Globe Trotters go to airports, Daily Grinders go to office buildings. Other non-obvious results show up also. Are there predictable patterns that we can use to target certain groups in the future?
  • 33. What Makes this Possible?   Using the power of Netezza and modeling capabilities of SPSS we can literally throw all the data at data mining algorithms and create discrete clusters of subscribers by activity, mobility Apply the data mining outputs to the entire subscriber base by creating detailed specific analyses for each subscriber refined by the mobility profiles
  • 34. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 35. Real-time Adaptive Analytics High Velocity Sensor Analytics Engine High Volume Scorer Predictive Modeler
  • 36. Adaptive Analytics • Collaboration across tools • SPSS and iLOG to manage models and rules • PDA to do query processing for the models • Streams to run the model • PMML to flow models from SPSS / iLOG to Streams
  • 37. Content • Use cases to support Business Architecture • Components to support Application Architecture • Data Integration • Privacy Management & Archiving • Location & Lifestyle Analytics • Adaptive Analytics • Momentum and Conclusions
  • 38. Marketing Assets Resource Link IBM Big Data Hub – Telco Home Page http://www-01.ibm.com/software/data/bigdata/industrytelco.html IBM Big Data Hub Cross-industry http://www.ibmbigdatahub.com/ Light Reading Webinar – “Big Data dramatically changes the Telco Game Plan” http://www.lightreading.com/webinar.asp?webinar_id=300 92&webinar_promo=1000000332 Big Data Analytics (e-book) http://ibm.co/Zw0jRW Big Data Analytics for Communications Service Providers (whitepaper) http://bitly.com/RJHbhj Telco Industry Blog on IBM Big Data Hub (Author - Gaurav Deshpande) http://www.ibmbigdatahub.com/blog/author/gauravdeshpande Videos http://www.youtube.com/watch?v=FIUFYyz03u8 http://www.youtube.com/watch?v=eg8KSLAZ2HM http://pro.gigaom.com/webinars/netezza-making-bigdata-analytics-pay/ http://youtu.be/bdJu1Pt374g
  • 39. IBM Big Data / Advanced Analytics Value Proposition All Telco Data Combine Network Data (usage, performance, capacity), Billing Call Detail Records, Subscriber, Channel, Policy, Device, Social etc. At Scale Ability to manage the stored Petabytes of data and incoming billions of records per day At Speed of Business Ability to process data and analytics in real time and close to point of origination to support emerging use cases such as Location Based Services (LBS) and Machine to Machine (M2M) Only IBM Only IBM can deliver the complete end to end technology and skills to capture quickly the new ERA value of Telco Big Data
  • 40. Communities • On-line communities, User Groups, Technical Forums, Blogs, Social networks, and more o Find the community that interests you … • Information Management bit.ly/InfoMgmtCommunity • Business Analytics bit.ly/AnalyticsCommunity • Enterprise Content Management bit.ly/ECMCommunity • IBM Champions o Recognizing individuals who have made the most outstanding contributions to Information Management, Business Analytics, and Enterprise Content Management communities • ibm.com/champion
  • 41. Thank You Your feedback is important! • Access the Conference Agenda Builder to complete your session surveys o Any web or mobile browser at http://iod13surveys.com/surveys.html o Any Agenda Builder kiosk onsite

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