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UKISUG2014 Big Data Presentation

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Big Data presentation at UK & Ireland SAP User Group, in Birmingham UK, November 2014

Published in: Data & Analytics, Technology
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UKISUG2014 Big Data Presentation

  1. The Big Trends in Big Data Timo Elliott, Global Innovation Evangelist, SAP @timoelliott
  2. Agenda Big Data Directions Using Big Data to Improve The Customer Experience Using Big Data to Empower Employees Using Big Data to Optimize Resource Use Using Big Data for Business Networks Wrap-up © 2014 SAP SE or an SAP affiliate company. All rights reserved. 1
  3. Big Data Directions © 2014 SAP SE or an SAP affiliate company. All rights reserved. 2
  4. The World Has Turned Upside-Down Transient, flexible Permanent, fixed ANALYTICS OPERATIONS © 2014 SAP SE or an SAP affiliate company. All rights reserved. 4
  5. What Is Big Data? The Google Summary … © 2014 SAP SE or an SAP affiliate company. All rights reserved. 7
  6. Big Data Is Not Only About “Big” Data “My analytics are becoming more difficult because of the variety and types of data sources (not just the volume)” Source: Paradigm4 data scientist survey 2014 www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf © 2014 SAP SE or an SAP affiliate company. All rights reserved. 8
  7. Process data Human data Machine data Big Data Adds New Data Opportunities © 2014 SAP SE or an SAP affiliate company. All rights reserved. 9
  8. Big Data is “Signal” Data © 2014 SAP SE or an SAP affiliate company. All rights reserved. 10
  9. Predictive Reaches Maturity Descriptive: What happened? Predictive: What will happen? Diagnostic: Why did it happen? Prescriptive: How can we make it happen? Hindsight Insight Foresight © 2014 SAP SE or an SAP affiliate company. All rights reserved. 11
  10. Companies Don’t Use Most of Their Data Today SMBs: LEs: Unstructured 9 TB 75 TB 50TB Semi-structured 0.6 TB 5 TB 2 TB Structured 4 TB 50 TB 12 TB Only 12% used today Average data volume per company Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012. Base: 634 business intelligence users and planners © 2014 SAP SE or an SAP affiliate company. All rights reserved. 12
  11. Transactions Are Still a Big Part of Big Data “Which types of data do you anticipate using in the next year?” Source: Paradigm4 data scientist survey 2014 www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf © 2014 SAP SE or an SAP affiliate company. All rights reserved. 13
  12. Big Data Is Heading for the “Trough of Disillusionment” Source: Gartner, August 2014, www.gartner.com/newsroom/id/2819918 © 2014 SAP SE or an SAP affiliate company. All rights reserved. 14
  13. Benefits from Big Data Initiatives # 5 Identified new product opportunities (6%) #4 More reliable decision making (9%) #3 Improved operational efficiency (11%) #2 Identified new business opportunities (31%) #1 “DON’T KNOW” (51%) Source: Information Difference Research Study Dec 2013: “Big Data Revealed” http://helpit.com/us/industry_articles/big_data_revealed.pdf © 2014 SAP SE or an SAP affiliate company. All rights reserved. 15
  14. Hadoop and Other “NoSQL” Technology Enterprise “Data Lakes” and “Data Hubs” © 2014 SAP SE or an SAP affiliate company. All rights reserved. 16
  15. Hadoop is Complementary, Not a Replacement Source: Gartner © 2014 SAP SE or an SAP affiliate company. All rights reserved. 17
  16. A Typical Example of DW and Hadoop Integration © 2014 SAP SE or an SAP affiliate company. All rights reserved. 18
  17. OLTP + OLAP = HTAP HTAP = Hybrid transaction/analytical processing A single system for both OLTP (operational) and OLAP (analytical) processing. Data is stored once, in-memory, and so instantly available for analytics. “Hybrid transaction/analytical processing will empower application leaders to innovate via greater situation awareness and improved business agility. This will entail an upheaval in the established architectures, technologies and skills driven by use of in-memory computing technologies as enablers.” Gartner, 2014 Source: Gartner 2014, “Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation” © 2014 SAP SE or an SAP affiliate company. All rights reserved. 19
  18. With HTAP, the Operational Schema Looks Like a DW SAP HANA SAP HANA Live (Virtual Data Model) Customer Service Risk Management Team Finance and Operations Account Administration Executive Management Customers Inventory Channel Products Suppliers Pricing Accounting Planning Forecasting © 2014 SAP SE or an SAP affiliate company. All rights reserved. 20
  19. Big Data Architecture Directions: Short Term Data Warehouse BI Tools Hadoop HTAP Where does data arrive? When does it need to move? Where does modeling happen? What can users do themselves? What governance is required? © 2014 SAP SE or an SAP affiliate company. All rights reserved. 21
  20. Big Data Architecture Directions: Long Term Metadata abstraction Increasingly automated Learning algorithms Content & PrDoacteas s Included Metadata abstraction Increasingly automated Learning algorithms Content and Process Included Warehouse BI Tools Where does data arrive? When does it need to move? Where does modeling happen? What can users do themselves? What governance is required? Integrated Data “SysteHma”d (ocolopud and on-premise) Hadoop HTAP Where does data arrive? When does it need to move? Where does modeling happen? What can users do themselves? What governance is required? Integrated Data “System” (cloud & on-premise) BI Tools © 2014 SAP SE or an SAP affiliate company. All rights reserved. 22
  21. Opportunity Areas for Innovation Big Data initiatives are typically in one of the following areas: Hyper-personalize Customer Experience Plan & optimize Resources in Real Time Engage & empower Workforce of the Future Harness the intelligence of Networked Economy © 2014 SAP SE or an SAP affiliate company. All rights reserved. 23
  22. Using Big Data to Improve the Customer Experience © 2014 SAP SE or an SAP affiliate company. All rights reserved. 24
  23. 80% of CEOs think they deliver a superior customer experience – but only 8% of customers agree. Source: The New Yorker © 2014 SAP SE or an SAP affiliate company. All rights reserved. 25
  24. Personalized Service © 2014 SAP SE or an SAP affiliate company. All rights reserved. 26
  25. 27 Simplifying Systems The benefits of the SAP HANA platform are significant with a hugely simplified footprint. We’re putting the whole business on the SAP HANA Enterprise cloud ” “
  26. Real-Time Retail Insights © 2014 SAP SE or an SAP affiliate company. All rights reserved. 28
  27. Social Data © 2014 SAP SE or an SAP affiliate company. All rights reserved. 29
  28. Unstructured Data “The improved information flow allows Medtronic to address product performance issues efficiently, accurately, and effectively and to detect trends at an earlier stage.” © 2014 SAP SE or an SAP affiliate company. All rights reserved. 30
  29. New Products and Services © 2014 SAP SE or an SAP affiliate company. All rights reserved. 31
  30. Network Analysis Churn model accuracy improved by 47% with social © 2014 SAP SE or an SAP affiliate company. All rights reserved. 32
  31. Sharing Data with Customers © 2014 SAP SE or an SAP affiliate company. All rights reserved. 33
  32. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 34
  33. Using Big Data to Empower Employees © 2014 SAP SE or an SAP affiliate company. All rights reserved. 35
  34. Worldwide, Only 13% of Employees Are Engaged at Work 18% 52% 30% 26% 57% 14% 70% 17% 16% 26% 65% 9% 100% 75% 50% 25% 0% USA UK Canada France Actively Disengaged Not Engaged Engaged Source: Gallup State of the Global Workplace Report 2013 © 2014 SAP SE or an SAP affiliate company. All rights reserved. 36
  35. Empowering Individual Performance Adapting to the analytics needs of your employees © 2014 SAP SE or an SAP affiliate company. All rights reserved. 37
  36. “Self-Service” Analytics © 2014 SAP SE or an SAP affiliate company. All rights reserved. 38
  37. Analytics Collaboration © 2014 SAP SE or an SAP affiliate company. All rights reserved. 39
  38. Collaborative Analytics © 2014 SAP SE or an SAP affiliate company. All rights reserved. 40
  39. Using Big Data to Optimize Resource Use 0101101100010101010 1010010101001111010 1010100101110101010 1010101001001010010 0100101110110101010 © 2014 SAP SE or an SAP affiliate company. All rights reserved. 41
  40. Unilever “if we knew then what we know now, we would have started deploying SAP HANA much earlier, because it’s so important for business... We think it’s even more disruptive than we initially thought — we’ve only just started” Marc Béchet, VP Global IT ERP, Unilever © 2014 SAP SE or an SAP affiliate company. All rights reserved. 42
  41. Nope © 2014 SAP SE or an SAP affiliate company. All rights reserved. 43
  42. Textile Rubber & Chemical Company 500 Employees, 4 internal IT staff Business Suite on HANA Why in-memory? Because it simplified our IT Landscape In 5 minutes we could see more information than we could in the last 7 months ” “ © 2014 SAP SE or an SAP affiliate company. All rights reserved. 44
  43. Big Data Process Mining © 2014 SAP SE or an SAP affiliate company. All rights reserved. 46
  44. Wearable devices have grown by 2x month over month since October 2012. Source: Mary Meeker’s Internet Trends, 2013 Photo: Intel Free Press
  45. The “Datafication” of Daily Life © 2014 SAP SE or an SAP affiliate company. All rights reserved. 48
  46. Unexpected Uses of Existing Data Source: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/ © 2014 SAP SE or an SAP affiliate company. All rights reserved. 49
  47. Data, Data, Everywhere © 2014 SAP SE or an SAP affiliate company. All rights reserved. 50
  48. Sensors Allow Tracking of the Previously Untrackable © 2014 SAP SE or an SAP affiliate company. All rights reserved. 51
  49. Sensors + Cloud + Mobile + Analytics 1. Install flow sensors on your beer lines 2. The sensors beam data to box plugged into the internet 3. Data sent to HANA in the cloud 4. Mobile interfaces to analyze consumption http://weissbeerger.com/ © 2014 SAP SE or an SAP affiliate company. All rights reserved. 52
  50. Sensors + Cloud + Mobile + Analytics (cont.) © 2014 SAP SE or an SAP affiliate company. All rights reserved. 53
  51. Networked Crane Safety © 2014 SAP SE or an SAP affiliate company. All rights reserved. 54
  52. Sensors + Analytics + Predictive Maintenance © 2014 SAP SE or an SAP affiliate company. All rights reserved. 56
  53. Making It Easier to Add Sensors © 2014 SAP SE or an SAP affiliate company. All rights reserved. 57
  54. Using Big Data for Business Networks © 2014 SAP SE or an SAP affiliate company. All rights reserved. 58
  55. Networked economy: the next economic revolution $0.36T 1850 Industrial economy $12.10T 1970 $27.50T $90.0T All figures are in Trillions; 1990 international dollars; Source: Department of Economics, UC Berkeley, BAIN 8 MacroTrends Brief. © 2014 SAP AG or an SAP affiliate company. All rights reserved. IT economy 1990 Internet economy 2020 Networked economy Gross world product
  56. Information Ecosystems 60 © 2014 SAP SE or an SAP affiliate company. All rights reserved. 60
  57. Business Networks Are Becoming Information Networks Procurement Sales Finance Logistics Supply Chain Sustainability Compliance Buyers Suppliers Partners Ariba Network More than 1M suppliers in more than 190 countries around the world Transact with suppliers – The Network handles over $460 billion per year in commerce Reduce supply costs – Customers save a combined total of $82M daily © 2014 SAP SE or an SAP affiliate company. All rights reserved. 61
  58. The SAP Big Data Strategy © 2014 SAP SE or an SAP affiliate company. All rights reserved. 62
  59. SAP Big Data Architecture Big Data Development Tools Industry Apps Line of Business Apps Data Connectors In-memory & petabyte-scale ETL Visualization & Exploration Advanced Analytics Reporting Streaming Analytics BI & © 2014 SAP SE or an SAP affiliate company. All rights reserved. 63
  60. Three Core Areas of Big Data Strategy Data Science Big Data Analytics & Apps Apply Achieve Big Data Platform Accelerate © 2014 SAP SE or an SAP affiliate company. All rights reserved. 64
  61. The SAP HANA Platform and Hadoop Custom Apps Mobile Apps Big Data Data Ingestion Acquisition Apps ERP Apps SAP Analytics SAP HANA PLATFORM In-memory processing platform for real-time transactions + end-to-end analytics that offers massive simplification. Extended Application Services Processing Engine Database Services (OLTP + OLAP) Unified Administration Application Development Application Function Libraries & Data Models Integration Services Smart Data Access Transfer Datasets SAP ERP BW SAP IQ Web / Sensor Call Center Other Data Sources SAP SLT / Rep Server SAP SQL Anywhere SAP ESP SAP Data Services Hadoop Adapter Hadoop Hive Hortonworks Data Platform Intel Distribution for Hadoop Partner Hadoop Distributions © 2014 SAP SE or an SAP affiliate company. All rights reserved. 65
  62. Front-End Tools Adapted to Different Needs DECISION MAKER DESIGNER Explore Monitor Design Plan People Govern DATA Enrich Explain DATA ANALYST/SCI ENTIST ENGAGE Enterprise BI VISUALIZE Agile Visualizations PREDICT Advanced Analytics © 2014 SAP SE or an SAP affiliate company. All rights reserved. 66
  63. Big Data Applications — E.g., Risk, Sensing, … © 2014 SAP SE or an SAP affiliate company. All rights reserved. 67
  64. Design Thinking © 2014 SAP SE or an SAP affiliate company. All rights reserved. 68
  65. Wrap-Up © 2014 SAP SE or an SAP affiliate company. All rights reserved. 69
  66. 7 Key Points to Take Home 1. Big Data is a huge opportunity 2. Get closer to your customers through better insight and hyper-personalization 3. Use “datafication” to make better use of resources 4. Empower your employees to make better decisions 5. Leverage your business networks 6. Big data is the heart of your next IT platform — simplicity and flexibility are essential 7. The biggest barriers are ideas and culture — use design thinking to help © 2014 SAP SE or an SAP affiliate company. All rights reserved. 70
  67. Thank you Timo Elliott, SAP timo.elliott@sap.com Twitter: @timoelliott Blog: timoelliott.com © 2014 SAP SE or an SAP affiliate company. All rights reserved.
  68. © 2014 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 72

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