Big Data Analytics Drive Data to Decision

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http://timoelliott.com/blog - Keynote presentation from the SAP Analytics Innovation Tour, ANZ -- turning big data into decisions, using signal data to make business change

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Big Data Analytics Drive Data to Decision

  1. 1. Big Data AnalyticsDrive Data to DecisionTimo Elliott, SAP, February 2013 Public
  2. 2. TODAY’S PRINCIPAL BUSINESS METRICS ARE OVER A 2 CENTURY OLD • Financial statements • Balance sheets • Net income© 2013 SAP AG. All rights reserved. 2
  3. 3. SOME PEOPLE WOULD EASILY RECOGNIZE THEM© 2013 SAP AG. All rights reserved. 3
  4. 4. TRADITIONAL FINANCIAL METRICS ARE BACKWARD-LOOKING Most Established KPIs too© 2013 SAP AG. All rights reserved. 4
  5. 5. WHEN WE REALLY NEED TO BE LOOKING FORWARD© 2013 SAP AG. All rights reserved. 5
  6. 6. BUT TYPES AND VOLUMES OF DATAHAVE GROWNRADICALLY Demand CRM Data Sales Order Mobile Instant Messages Transactions Big Data Things Customer Sales Order Planning CRM Data Things Opportunities Inventory Demand Mobile Big Data Customer Planning Transactions© 2013 SAP AG. All rights reserved. 6
  7. 7. TODAY WE MEASURE AVAILABLE DATA INZETTABYTES IN 2011, THE AMOUNT OF DATA SURPASSED 90% OF THE DATA IN THE WORLD TODAY has been created in the last two years alone 1.8 ZETTABYTES COMBINED GDP OF: 1.8 57.5 = TRILLION = $34.4 • US • France ZETTABYTES = BILLION 32 GB iPads • • • Japan China Germany • UK • Italy **IDC Digital Universe Study Extracting Value from Chaos© 2013 SAP AG. All rights reserved. 7
  8. 8. BIG DATA DE-HYPED “Big data” is high-volume, -velocity, and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.© 2013 SAP AG. All rights reserved. 8
  9. 9. © 2013 SAP AG. All rights reserved. 9
  10. 10. WHAT IF YOU COULD TURN NEW SIGNALSINTO BUSINESS VALUE? :-) :-) Brand Sentiment Predictive Maintenance Network Optimization Insider Threats Higher NPS Less Downtime Lower Cost Greater Security Product Recommendation Asset Tracking Asset Tracking More Sales MaintenanceMitigation, Real-time Personalized Care Predictive Product Risk Brand Sentiment Increase Productivity Less Downtime Retain Market ValueIncrease Productivity Loyal Customers Recommendation Higher NPS More Sales Personalized Care 360O Customer View Propensity to Churn Loyal Customers Loyal Customers Greater Retention Insider Threats Fraud Detection Greater Security Lower Risk Network Optimization Lower Cost Propensity to Churn Real-time Demand/ 360O Customer View Fraud Detection Risk Mitigation, Real-time Greater Retention Supply Forecast Loyal Customers Real-time Demand/Supply Forecast Value Lower Risk Retain Market More Efficient More Efficient
  11. 11. © 2013 SAP AG. All rights reserved. 11
  12. 12. © timoelliott.com “When you two have finished arguing your opinions, I actually have data!”© 2013 SAP AG. All rights reserved. 14
  13. 13. © 2013 SAP AG. All rights reserved. 15
  14. 14. © 2013 SAP AG. All rights reserved. 16
  15. 15. © 2013 SAP AG. All rights reserved. 17
  16. 16. IN-MEMORY© 2013 SAP AG. All rights reserved. 19
  17. 17. FASTER© 2013 SAP AG. All rights reserved. 20
  18. 18. SMARTER© 2013 SAP AG. All rights reserved. 21
  19. 19. Expected IT Benefits of In-Memory Technology SIMPLERSource: IDC SAP HANA Market Assessment Survey, 2011© 2013 SAP AG. All rights reserved. 22
  20. 20. THE NEW INFORMATION CONSUMER… ResponsiveAccessible Engaging 10% 75% today by 2020© 2013 SAP AG. All rights reserved. Public 23
  21. 21. BREAKING DOWN BARRIERS “New Reality: In-memory computing is ripping up the rules” - Gartner Plan Past Act Future Structured Unstructured© 2013 SAP AG. All rights reserved. Public 24
  22. 22. POWER AGILITY ENTERPRISE Big Data Analytics Cloud Mobile Social© 2013 SAP AG. All rights reserved. Public 25
  23. 23. INNOVATIONSAP VISUAL INTELLIGENCE© 2013 SAP AG. All rights reserved. 26
  24. 24. © 2013 SAP AG. All rights reserved. Public 27
  25. 25. Extend your analytics capabilities where you want to be… Sense & Respond Predict & ActCompetitive Advantage Optimization Predictive Modeling What is the best that could happen? Generic Predictive Analytics Ad Hoc Reports & What will happen? OLAP Standard Cleaned Reports Raw Why did it happen? Data Data What happened? Analytics Maturity The key is unlocking data to move decision making from sense & respond to predict & act © 2013 SAP AG. All rights reserved. Public 28
  26. 26. “SAP is a newcomer to big data predictive analytics but is a Leader due to a strong architecture and strategy.” Forrester© 2013 SAP AG. All rights reserved. 29
  27. 27. MOBILE© 2013 SAP AG. All rights reserved. 30
  28. 28. © 2013 SAP AG. All rights reserved. 34
  29. 29. © 2013 SAP AG. All rights reserved. 35
  30. 30. Faster  Smarter  Simpler ANALYTICS OPERATIONS© 2013 SAP AG. All rights reserved. Public 36
  31. 31. Real-Time Data Platform Custom Big Cloud Mobile SCM ERP Planning Analytics EDW EDW/ EDW Apps Data DM SAP Real-Time Data Platform Extreme Data Management Capabilities to transact | move | store | process | analyze | deliver Information Governance© 2013 SAP AG. All rights reserved. Public 37
  32. 32. DATA QUALTY© 2013 SAP AG. All rights reserved. 38
  33. 33. Applications for Data Stewards© 2013 SAP AG. All rights reserved. 39
  34. 34. Business Solutions Retail High Tech Oil & Gas Consumer Products Telco Analysis Etc. Action © 2013 SAP AG. All rights reserved. Public 40
  35. 35. Intelligent Retail Store operations Omni-channel experience Customized promotions Optimized inventory© 2013 SAP AG. All rights reserved. 42
  36. 36. CUSTOMER SPOTLIGHT HSE24 • Cross-sell based on the consumer, not just the product© 2013 SAP AG. All rights reserved. 43
  37. 37. CUSTOMER SPOTLIGHT BURBERRY • Improve customer experience and loyalty • Become #1 IT-driven fashion retailer • Personalized Burberry client iPad app© 2013 SAP AG. All rights reserved. 44
  38. 38. CUSTOMER SPOTLIGHT L’Oréal • Be a trusted advisor for customers© 2013 SAP AG. All rights reserved. 45
  39. 39. Coinstar• Inventory Optimization• Real time Offers• Servicing © 2013 SAP AG. All rights reserved. 46
  40. 40. INTELLIGENT BANKING Customer-centric banking Aggregated risk Liquidity risk management Prevent Fraud, Money Laundering© 2013 SAP AG. All rights reserved. 48
  41. 41. © 2013 SAP AG. All rights reserved. 49
  42. 42. Improve patient outcome Improve doctor/patient relationships More preventative healthcare Higher quality of life for patients INTELLIGENT HEALTHCARE© 2013 SAP AG. All rights reserved. 50
  43. 43. Mitsui Knowledge IndustryHealthcare – Speed Research & Improve Patient Support Business Challenges 408,000x  Reduce delays and minimize the costs associated with new drug faster than discovery by optimizing the process for genome analysis traditional disk-  Improve and speed decision making for hospitals which conduct based systems in a cancer detection based on DNA sequence matching technical PoC Technical Implementation  Leveraged the combination of SAP HANA, R, and Hadoop to store, pre-process, compute, and analyze huge amounts of data 216x faster by  Provide access to breadth of predictive analytics libraries reducing genome analysis from Benefits several days to  For pharmaceutical companies, provide required new drugs on only 20 minutes time and aid identification of “driver mutation” for new drug targets making real-time  Able to provide a one stop service including genomic data cancer/drug analysis of cancer patients to support personalized patient screening possible therapeutics“ ”Our solution is to incorporate SAP HANA along with Hadoop and R to create a single real-time big data platform. With this wehave found a way to shorten the genome analysis time from several days down to only 20 minutes.Yukihisa Kato, CTO and Director of MITSUI KNOWLEDGE INDUSTRY © 2013 SAP AG. All rights reserved. Public 51
  44. 44. Real-time performance management Fast financial close Efficient payables and receivables Treasury exposure management Real-time compliance INTELLIGENT FINANCE© 2013 SAP AG. All rights reserved. 52
  45. 45. INNOVATIONSAP SUITE ON HANASOLUTIONS FOR FINANCE REAL-TIME PERFORMANCEPerformance Financial Treasury Finance ComplianceManagement Close Management Operations POWERED BY SAP HANA© 2013 SAP AG. All rights reserved. 53
  46. 46. © 2013 SAP AG. All rights reserved. 54
  47. 47. INNOVATIONSAP FRAUD MANAGEMENT© 2013 SAP AG. All rights reserved. 55
  48. 48. INTELLIGENT CITIES Citizen safety and security Smarter city governance Sustainable cities© 2013 SAP AG. All rights reserved. 56
  49. 49. © 2013 SAP AG. All rights reserved. 57
  50. 50. NFL© 2013 SAP AG. All rights reserved. 58
  51. 51. NBA.com/stats© 2013 SAP AG. All rights reserved. 59
  52. 52. © 2013 SAP AG. All rights reserved. 60
  53. 53. BigpointGaming Industry - Predictive Game Player Behavior Analysis Business Challenges 5,000 events per  Increase conversion rates from free  paying player second loaded onto SAP HANA (not  Increase the average revenue per paying player possible before)  Decrease churn – keep paying players playing longer Technical Challenges  Leverage real-time data processing in SAP HANA and 10-30% classification algorithms with R integration for SAP HANA to increase in revenue deliver personalized context-relevant offers to players per year  Analyze vast amounts of historical and transactional data to forecast player behavior patterns Interactive Benefits data analysis  Real-time insights leading to improved  Per player profitability analysis and increased understanding of design thinking and player behavior game planning  Increase data volume and processing capabilities to communicate personalized messages to players“”At Bigpoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have toload in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In thisco-innovation project with SAP HANA, using Real Time Offer Management Bigpoint, we hope to increase revenue by 10-30%.Claus Wagner, Senior Vice President SAP Technology, Bigpoint © 2013 SAP AG. All rights reserved. Public 61
  54. 54. A Platform for Everyone Free developer licenses, easy access to HANA in the Cloud© 2013 SAP AG. All rights reserved. Public 62
  55. 55. © 2013 SAP AG. All rights reserved. 63
  56. 56. © 2013 SAP AG. All rights reserved. 64
  57. 57. THE POSSIBILITIES ARE ENDLESS… Instantly predict market trends Provide exactly the right offers and customer needs and service levels to every customer Predict how market price Have a continuously-updated volatility will impact your window onto future sales, production plans showing changes in real time See changes in demand or Understand what your supply across your entire customers and potential Supply Chain immediately customers are saying about you, right now Monitor and analyze all Predict cash flows to manage deviations and quality issues collections, risk and short-term in your production process borrowing in real time Design Thinking© 2013 SAP AG. All rights reserved. Public 65
  58. 58. DATA TO DECISION DATA TO DECISION Capture Analyze EngageAnalytic applications Industry Line of Businessfrom SAP EnterpriseAnalytic solutions Business Performance Governance, Risk,from SAP Intelligence Compliance ManagementReal-time Databases In-memory Informationdata platform management
  59. 59. Introducing SAP HANA Insights Traditional Stack DBA and IT Costs TCO Reduction by Approx 60% Predictive BI tools SAP HANA Insights Analytic DB DBA and IT Costs ETL SAP HANA Insights Predictive Relational DB BI Suite HANA EnterpriseBuilt by Sanjay Poonen, Copyright SAP, Representative figures based off CIO interviews, showing approximate proportional quantities of IT spending in Analytics © 2013 SAP AG. All rights reserved. Public 68
  60. 60. Teamwork© 2013 SAP AG. All rights reserved. 69
  61. 61. IT’S TIME FOR ANALYTICS LIKENEVER BEFORE© 2013 SAP AG. All rights reserved. Public 70
  62. 62. Thank youTimo Elliott, SAPEmail: timo.elliott@sap.comTwitter: @timoelliottBlog: timoelliott.com

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