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  • 1. CME Industry Directions Aligning solutions to MegatrendsJeff EdlundCTO Communications & Media Solutions©2011 Hewlett-Packard Development Company, L.P.©2011Copyright Hewlett-Packard Development Company, L.P.The information contained herein is subject to change without notice
  • 2. Agenda• Overview of CMS Portfolio• Megatrends in the CSP Market• Big Data / Analytics Intersection• HP CMS Response• New Innovations – Live Customer Intelligence – Personal Profile2 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 3. CMS Portfolio3 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 4. Megatrends in the CSP Market
  • 5. Megatrends• Analytics / Big Data – Movement from Systems of Record to System of Engagement – Transforming data into insight enabling enhanced service experiences• Globalization – Everything is becoming connected, always on and mobile – Systems & service must move and interplay the way same as their users• Social Networking / Communications – Rapidly replacing email and SMS as a preferred form of Communications – Entirely new business models emerging as the Communications channel is now public• Ecosystem Players – The pre-Smartphone ecosystem used to be the device + CSP – New Ecosystems include: Device, Communications, Content, Applications, Mobility & Managed Experience – Apple & Android currently dominate. Amazon will emerge in 2012• Machine to Machine (M2M) – Not a new phenomenon but mobility opens up new opportunities – Represents one of the clearest paths for new CSP revenue5 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 6. HP observed investment directions Investment PoR New Capabilities6 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 7. Big Data: It’s more than just storing bits7
  • 8. Everything is becoming INSTRUMENTED INTERCONNECTED INTELLIGENT We now have the ability People, systems and We can respond to changes to measure, sense and objects can communicate quickly and accurately, and get see the exact condition of and interact with each better results by predicting and practically everything. other in entirely new ways. optimizing for future events. MANUFACTURING IT CUSTOMERSWORKFORCE SUPPLY CHAIN TRANSPORTATION FACILITIES ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 9. Data growth is massive Importance of Volume of Digital Data Decision Making Every day, 15 petabytes of new information are 70% of executives believe that being generated. This is 8x more than the poor decision making has had a information in all U.S. libraries. degrading impact on their companies’ performance In 2010, the codified information base of the world was doubling 1 hours. 1 Only 9% of CFOs believe they excel at interpreting data for senior managementAnalytics, modeling, and visualization of this data can help to run our systems more effectively ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 10. HP View of Big Data• Velocity – Moves at very high rates (think sensor-driven systems) – Valuable in its temporal, high velocity state• Volume – Fast-moving data creates massive historical archives – Valuable for mining patterns, trends and relationships• Variety – Structured (logs, business transactions) – Semi-structured and unstructured10 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 11. Big Data and the CSP• Incoming CSP data streams are different than traditional business apps – Need to write data quickly & reliably, but …• It’s not just about high speed writes – Need to validate in real-time – Need to count and aggregate – Opportunity to analyze in real-time – Need to scale on demand – May need to transact11 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 12. High velocity DBMS requirements• Ingest at very high speeds and rates • Support millions of write operations per second at scale• Scale easily to meet growth and demand peaks • Read and write latencies below 50 milliseconds• Support integrated fault tolerance • Provide ACID-level consistency• Support a wide range of real-time (or guarantees (maybe) “near-time”) analytics • Support one or more well-known• Integrate easily with high volume application interfaces analytic data stores – SQL – Key/Value – Document12 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 13. Traditional Big Data use cases13 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 14. Big Data Management Infrastructure Online gaming  Structured data Ad  ACID guarantees serving  Relational/SQL Sensor  Real-time analytics data Financial NewSQL Analytic trade  Unstructured data Data stores Internetcommerce  Eventual consistency  No Schema SaaS,  KV, document Web 2.0 Mobile NoSQL platforms ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 15. Systems of Record vs. Systems of Engagement- moving from storing data to putting information into action• Systems of Record create efficiency • Impossible to transact commerce without SoR • Focus’ on cost, quality and contractual obligations• Systems of Engagement create effectiveness • Address the complexities of business relationships • Create compelling customer interactions on-line in real-time• What’s the correct architecture • SoE’s operate on top of and in touch with SoR’s • This requires an evolutionary infrastructure not a wholesale revolution ©2011Copyright Hewlett-Packard Development Company, L.P. “Systems of Engagement” is a phrase coined by Geoff Moore
  • 16. Implications of SoE’s for the CSP- what’s the big change Systems of Record Systems of Engagement• Command & control – Collaborative model• Transaction oriented – Interaction oriented• Data centric – User experience centric• Users learn the System – Systems learn users: • Navigation, value, etc… • Wants, needs, desires• Very safe & secure – Very baller & hipster ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 17. Transforming Big Data through Analytics: Customer Experience Assurance17
  • 18. Real-time Analytics requirements• Database should support a wide variety of high performance reads – High-frequency single-partition – Lower-frequency multi-partition• Common analytic queries should be optimized in the database – Multi-partition aggregations, limits, etc.• Database should accommodate a flexible range of relational data operations – Particularly relevant to structured data18 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 19. Integrating with Analytical data stores• Database should offer high performance, transactional export• Export should allow a wide variety of common data enrichment operations – Normalize and de-normalize – De-duplicate – Aggregate• Architecture should support loosely-coupled integrations – Impedance mismatches – Durability19 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 20. Types of Analytics How can we achieve the best outcome Stochastic Optimization including the effects of variability? Prescriptive Optimization How can we achieve the best outcome?Degree of Complexity Predictive modeling What will happen next if ? Simulation What could happen … ? Predictive Forecasting What if these trends continue? Alerts What actions are needed? Query/drill down What exactly is the problem? Ad hoc reporting How many, how often, where? Descriptive Standard Reporting What happened? Based on: Competing on Analytics, Davenport and Harris, 2007 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 21. The new face of Information Mgmt - making use of the data sources available today Social Usage EaaS Mobility Networking Profile patterns Time shifted Timely Historical ContextualYesterday Reporting Today Relevant Elitist IM Democratic
  • 22. Where’s the value - Data or Information?Perhaps it is the Insights… ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 23. Where’s the value - Data or Information?… Perhaps it is the Insights I feel Obligated to counter the bad reviews. The printer is just fine. I don’t know what people are complaining about regarding the software but it installed seamlessly and is intuitive in its operation. Even though the paper tray jams sometimes I am happy I bought this wonderful printer. cartridge paper tray price printer scanner software 0 -1 0 +1 0 +1 All HP Printers copy, fax, feature, 1.00 photo, price, 0.00 print, quality 1 3 5 7 9 11 13 15 17 19 21 23 25 cartridge, driver, ink, % pos % neg installation, paper, software, usb ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 24. Information Management Value Chain - transforming data into meaningful results Insight • Capture • Predict • Integrate • Analyze • PublishContent • Classify • Process • Personalize Results • Govern Information Action Business Value Point of view • Avoid boiling the ocean, progress steadily • Most CTO’s / CIO’s tell us they are stuck in the first stage • Unstructured data & rich content are huge problems to solve • Predictive analytics transforming Information -> Insight -> Action = $$$ • Feedback and measurement on Results is critical
  • 25. CSP Data Sources of today- the data flood is here and begs the question: What do you analyze?– XDR – HLR – Performance– OM – HSS – Fault– DPI – Location – Probes– SBC – LERG – Timers– PCMD – Billing – Topology– MDM – Marketing – PCRF What’s most important: Network data, Subscriber data, Application data, Market Information New analytical models & technology can provide usable advantage
  • 26. Customer Centric Sources Include: • HSSSources Include: • Provisioning• CDR • PM • Billing• PCMD • FM• xDRSources Include: Sources Include:• LERG • DPI• Demographics • SBC• Segmentation • Social Network 26 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 27. Assuring the Customer ExperienceCustomer Insights: Delighted Customers:– that drive great – services that work & experiences play the way that– help you customize they do your offers – billing plans that fit– allow you to massively a variable lifestyle personalize service – self customization as delivery personal needs dictate as the CSP you have far more information at your disposal than OTT providers 27 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 28. HP CMS Big Data / Analytics Platform SolutionsService Personalization Network Intelligence Service Intelligence Mobile Experience Personalization Subscriber UDR Broker Policy Charging Marketing Data Mgr Personalized CEP – Data Exposure Advertising Real-time Profile IDOL 10 / Vertica Analysis & Exchange IUM Actionable Customer ExperienceProfile Data Device Data Network Data Usage Data Subscriber Data Management Tethering / Usage Analysis28 ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 29. Real-time analytics = effective decisions Dynamic Business Conditions Business Automation Enables Fast Response Point of Transaction Response to Dynamic Conditions Effective Real-time Business Event Decisions Result Measured Potential Business Root Cause Determined Corrective Decision Made Value Action Taken Action time ©2011Copyright Hewlett-Packard Development Company, L.P.
  • 30. Innovations in Analytics
  • 31. Social Analytics Offerings Service Platform (API) LCI Explore Vertica IDOL HP Cloud Platform
  • 32. Chameleon Personal Profile• Comprehensive and ubiquitous cloud Personality Profile• Network element that collects, stores and analyzes personal data• Gather data across multitude of user devices• Builds individual opt-in Personality Profiles based upon Consumer behavior• Algorithmically translate real-world data into effective actionable models Open Eco-system Secure Collect Charge Store Report Analyze Profile
  • 33. THANK YOU