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Konceptuelt overblik over Big Data, Flemming Bagger, IBM

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Præsentation fra IBM Smarter Business 2012

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Konceptuelt overblik over Big Data, Flemming Bagger, IBM

  1. 1. Insight to Action – Big Data– Challenge and Opportunity
  2. 2. Smarter Business 2012 Mobility Smarter Social Smarter Smarter – bring your own Analytics Collaboration Security Cities deviceInsight to Action – Smarter Smarter Smarter SmarterBig Data - Challenge Commerce Product Process Infrastructure and Opportunity & Marketing Innovation Optimization Management Automation
  3. 3. Agenda10:30 IBM Big Data Platform Flemming Bagger, Big Data Analytics Leader, Nordic11:15 Pause11:30 Opnå konkrete resultater med Big Data Analytics Lauren Walker, Big Data Analytics Leader, Europe12:15 Frokost13:30 Succes eller fiasko? Sådan håndteres Big Data i den finansielle sektor Keith Prince, EMEA Industry Solutions Executive, Financial Services, IBM14:15 Pause14:30 Dataindsamling og overvågning på tværs af sociale medier Ulrik Bo Larsen, Founder & CEO, FALCON Social15:10 Afrunding
  4. 4. Agenda10:30 IBM Big Data Platform Flemming Bagger, Big Data Analytics Leader, Nordic11:15 Pause11:30 Opnå konkrete resultater med Big Data Analytics Lauren Walker, Big Data Analytics Leader, Europe12:15 Frokost13:30 Succes eller fiasko? Sådan håndteres Big Data i den finansielle sektor Keith Prince, EMEA Industry Solutions Executive, Financial Services, IBM14:15 Pause14:30 Dataindsamling og overvågning på tværs af sociale medier Ulrik Bo Larsen, Founder & CEO, FALCON Social15:10 Afrunding
  5. 5. Information ManagementHighlight from the IBM CEO Study 2012 © 2012 IBM Corporation
  6. 6. Information Management 83x 6,000,000 users on Twitter 500,000,000 users on Twitter pushing out 300,000 pushing out 400,000,000 tweets per day tweets per day 1333x © 2012 IBM Corporation
  7. 7. Information Management In 2005 there were 1.3 billion RFID tags in circulation…© 2012 IBM Corporation
  8. 8. Information ManagementWhere is big data coming from? 4.6 30 billion RFID tags today billion camera 12+ TBs (1.3B in 2005) phones of tweet data world wide every day 100s of millions of GPS data every? TBs of enabled day devices sold annually 25+ TBs of 2+ log data billion every day people on 76 million smart the Web by end meters in 2009… 2011 200M by 2014 © 2012 IBM Corporation
  9. 9. Information ManagementIn Order to Realize New Opportunities, You Need to Think Beyond TraditionalSources of Data Transactional and Machine Data Social Data Enterprise Application Data Content  Volume  Velocity  Variety  Variety  Structured  Semi-structured  Highly unstructured  Highly unstructured  Throughput  Ingestion  Veracity  Volume © 2012 IBM Corporation
  10. 10. Information ManagementThe Characteristics of Big Data Cost efficiently Responding to the Collectively analyzing processing the increasing Velocity the broadening Variety growing Volume 50x 35 ZB 30 Billion RFID sensors 80% of the and counting worlds data is unstructured 2010 2020 Establishing the 1 in 3 business leaders don’t trust Veracity of big the information they use to make data sources decisions © 2012 IBM Corporation
  11. 11. Information Management The Big Data Conundrum The percentage of available data an enterprise can analyze is decreasing proportionately to the available to that enterprise – Quite simply, this means as enterprises, we are getting “more naive” about our business over time Just collecting and storing “Big Data” doesn’t drive a cent of value to an organization’s bottom line Data AVAILABLE to an organization Data an organization can PROCESS © 2012 IBM Corporation
  12. 12. Information ManagementBig Data is a Hot topic- Because Technology Makes it Possible to Analyze ALL Available Data Cost effectively manage and analyze all available data in its native form unstructured, structured, streaming…….Internal and external Website Social Media Billing ERP Network Switches CRM RFID © 2012 IBM Corporation
  13. 13. Information ManagementMost Client Use Cases Combine Multiple Technologies Pre-processing Ingest and analyze unstructured data types and convert to structured data Combine structured and unstructured analysis Augment data warehouse with additional external sources, such as social media Combine high velocity and historical analysis Analyze and react to data in motion; adjust models with deep historical analysis Reuse structured data for exploratory analysis Experimentation and ad-hoc analysis with structured data © 2012 IBM Corporation
  14. 14. Information ManagementBusiness-centric Big Data enables you to start with a critical business pain andexpand the foundation for future requirements  “Big data” isn’t just a technology—it’s a business strategy for capitalizing on information resources  Getting started is crucial  Success at each entry point is accelerated by products within the Big Data platform  Build the foundation for future requirements by expanding further into the big data platform14 © 2012 IBM Corporation
  15. 15. Information Management1 – Unlock Big Data Customer Need – Understand existing data sources – Expose the data within existing content management and file systems for new uses, without copying the data to a central location – Search and navigate big data from federated sources Value Statement – Get up and running quickly and discover and retrieve relevant big data – Use big data sources in new information-centric applications Get started with: IBM Vivisimo Velocity © 2012 IBM Corporation
  16. 16. Information ManagementMost Common Big Data Use Case = 360-ViewsSingle view of the information Customer- Facing Professional/Kn owledge Worker © 2012 IBM Corporation
  17. 17. Information Management2 – Analyze Raw Data Customer Need – Ingest data as-is into Hadoop and derive insight from it – Process large volumes of diverse data within Hadoop – Combine insights with the data warehouse – Low-cost ad-hoc analysis with Hadoop to test new hypothesis Value Statement – Gain new insights from a variety and combination of data sources – Overcome the prohibitively high cost of converting unstructured data sources to a structured format – Extend the value of the data warehouse by bringing in new types of data and driving new types of analysis – Experiment with analysis of different data combinations to modify the analytic models in the data warehouse Get started with: InfoSphere BigInsights © 2012 IBM Corporation
  18. 18. Information Management3 – Simplify your Warehouse  Customer Need – Business users are hampered by the poor performance of analytics of a general-purpose enterprise warehouse – queries take hours to run – Enterprise data warehouse is encumbered by too much data for too many purposes – Need to ingest huge volumes of structured data and run multiple concurrent deep analytic queries against it – IT needs to reduce the cost of maintaining the data warehouse  Value Statement – Speed and Simplicity for deep analytics (Netezza) – 100s to 1000s users/second for operation analytics (IBM Smart Analytics System)  Get started with: IBM Netezza18 © 2012 IBM Corporation
  19. 19. Information Management4 – Reduce costs with Hadoop Customer Need – Reduce the overall cost to maintain data in the warehouse – often its seldom used and kept ‘just in case’ – Lower costs as data grows within the data warehouse – Reduce expensive infrastructure used for processing and transformations Value Statement – Support existing and new workloads on the most cost effective alternative, while preserving existing access and queries – Lower storage costs – Reduce processing costs by pushing processing onto commodity hardware and the parallel processing of Hadoop Get started with: IBM InfoSphere BigInsights © 2012 IBM Corporation
  20. 20. Information ManagementIBM Significantly Enhances Hadoop IBM Innovation• Scalable • Performance & reliability – New nodes can be added on the fly. – Adaptive MapReduce, Compression, Indexing, Flexible Scheduler• Affordable – Massively parallel computing on • Analytic Accelerators commodity servers • Productivity Accelerators• Flexible – Web-based UIs – Hadoop is schema-less, and can absorb – Tools to leverage existing skills any type of data. – End-user visualization• Fault Tolerant • Enterprise Integration – Through MapReduce software framework – To extend & enrich your information supply chain.20 © 2012 IBM Corporation
  21. 21. Information Management5 – Analyze Streaming Data Streaming Data Sources Streams Computing Customer Need – Harness and process streaming data sources – Select valuable data and insights to be stored for ACTION further processing – Quickly process and analyze perishable data, and take timely action Value Statement – Significantly reduced processing time and cost – process and then store what’s valuable – React in real-time to capture opportunities before they expire Customer examples – Ufone – Telco Call Detail Record (CDR) analytics for customer churn prevention Get started with: InfoSphere Streams © 2012 IBM Corporation
  22. 22. Information ManagementEntry points are accelerated by products within the big data platform1 – Unlock Big Data Analytic Applications BI / Exploration / Functional Industry Predictive ContentIBM Vivisimo Reporting Visualization App App BI / Analytics Analytics Reporting IBM Big Data Platform 3 – Simplify your Visualization Application Systems warehouse2 – Analyze Raw Rata & Discovery Development Management NetezzaInfoSphereBigInsights Accelerators Hadoop Stream Data System Computing Warehouse 5 – Analyze Streaming4 – Reduce costs with DataHadoop InfoSphere StreamsInfoSphereBigInsights Information Integration & Governance22 © 2012 IBM Corporation
  23. 23. Information ManagementIs Big Data imperative? © 2012 IBM Corporation
  24. 24. Information Management THINK24 © 2012 IBM Corporation
  25. 25. Agenda10:30 IBM Big Data Platform Flemming Bagger, Big Data Analytics Leader, Nordic11:15 Pause11:30 Opnå konkrete resultater med Big Data Analytics Lauren Walker, Big Data Analytics Leader, Europe12:15 Frokost13:30 Succes eller fiasko? Sådan håndteres Big Data i den finansielle sektor Keith Prince, EMEA Industry Solutions Executive, Financial Services, IBM14:15 Pause14:30 Dataindsamling og overvågning på tværs af sociale medier Ulrik Bo Larsen, Founder & CEO, FALCON Social15:10 Afrunding
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