Big Data: Next Frontier for Innovation

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Clifford Dickerson
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12 The implementation of a state-of-the-art Data Analytics program is the foundational platform required for the three Discovery Themes to have a transformative impact on the advancement of science and technology research, education, and outreach across the university. David Cole, Professor and Ohio Research Scholar, School of Earth Sciences, College of Arts and Sciences 12 Copyright 2014, Oracle and/or its affiliates. All rights reserved.

13 The notion of Big Data has created a stir and promises to revolutionize all walks of human endeavor ranging from the financial industry to the health-care industry, from the social sciences (e.g., study of personalized and social interactions) to engineering (e.g., analyzing sensory data from the manufacturing process), and from scientific discovery to cyber-security. David Tomasko, Professor of Chemical and Biomolecular Engineering, Associate Dean for Undergraduate Education and Student Services, College of Engineering 13 Copyright 2014, Oracle and/or its affiliates. All rights reserved.

14 Data Analytics includes the set of skills necessary to identify how to manage, represent and manipulate large and complex data stores, how to abstract, model, and effectively analyze such data to facilitate the identification and evaluation of appropriate hypothesis and actionable patterns, and how to create quantitative visual cues to help explain and take informed decisions from those data. David Tomasko, Professor of Chemical and Biomolecular Engineering, Associate Dean for Undergraduate Education and Student Services, College of Engineering 14 Copyright 2014, Oracle and/or its affiliates. All rights reserved.

23 #Genlike Generation 23

24 Affordability and Transparency 24

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26 The Rise of the MOOCs 26

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28 Google announces Android software for wearable tech Launching a new software development kit, Google is preparing for a new market of smartwatches and other wearable technologies - starting with a Motorola moto 360 watch in summer

29 2014 Survey of College and University Presidents 50% of campus leaders agree that President Obama's plan to collect and report data on college outcomes is appropriate - but just 13 percent are confident that the data will be accurately collected and reported. 29

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31 Machine to Machine Era Infinite Possibilities By 2020 People in the WORLD CONNECTED Devices 31

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34 History of Big Data 34

35 1943 Records Department of FBI 35

37 Implementing analytics and applying it to make data driven decisions is a major differentiator between high performing and low performing organizations. Big Data: The Next Frontier for Innovation, Competition and Productivity McKinsey Global Institute

38 What Makes it Big Data? SOCIAL BLOG SMART METER VOLUME VELOCITY VARIETY VALUE 38

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41 Thoughts Things Processes 41

42 Thoughts Things Processes Thoughts Things Processes 42

43 People to Machine (P2M) Ohio State Doctor Shows Promise of Google Glass in Live Surgery 43

44 Stale Data Islands of Data Drowning in Spreadsheets Gather and Organize All Data Lack of Data Skill 44

45 The Big Data Problem and Opportunity Produce Data 12 % Executives who feel they understand the impact data will have on their organizations Use Data 45

46 Oracle s Strategy for Big Data Produce Data How do you shrink this gap? Capture massive data volumes Analyze all data Secure and unify the data platform Use Data 46

47 Big Data Connectors and Data Integrator Big Data Appliance + Hadoop / NoSQL Exadata + Oracle Database 47

48 Technically, Hadoop consists of two key services: Reliable data storage using the Hadoop Distributed File System (HDFS) and high-performance parallel data processing using a technique called MapReduce 48

scale to hundreds or thousands of computers, each with several processor cores.

49 Hadoop is a large-scale distributed batch processing infrastructure. While it can be used on a single machine, its true power lies in its ability to scale to hundreds or thousands of computers, each with several processor cores. Hadoop is also designed to efficiently distribute large amounts of work across a set of machines. 49

50 Oracle s Strategy for Big Data Produce Data Use Data How do you shrink this gap? Capture massive data volumes Analyze all data Secure and unify the data platform 50

51 Analyze All Your Data In-Place Advanced Analytics Big Data Appliance + Hadoop / NoSQL Exadata + Oracle Database 51

52 Expose All Data to End Users OBI EE Endeca Big Data Appliance + Hadoop / NoSQL Exadata + Oracle Database 52

53 Oracle s Strategy for Big Data Produce Data Use Data How do you shrink this gap? Capture massive data volumes Analyze all data Secure and unify the data platform 53

54 Large, Research Intensive University Data Reservoir Big Data Appliance + Hadoop Exadata + Oracle Database 54

55 Securing Big Data Increasingly, Big Data solutions are capturing sensitive information must be protected and audited This is no different than critical data stored in an RDBMS 55

56 Enhanced Big Data Security Authenticate users with secure Kerberos protocol Authorize access to data with fine grained controls Audit activity and access with Oracle Audit Vault and Database Firewall Encrypt data as it flows thru the system 56

57 Oracle Strategy for Big Data Produce Data Use Data How do you shrink this gap? Capture massive data volumes Analyze all data Secure and unify the data platform 57

58 Advanced Query & Analysis Full Power of SQL and Advanced Analytics Transparent to Applications No Changes to Application Code Single View of All Data Unified Metadata Across RDBMS & Hadoop Fastest Performance Utilize SQL Processing Across the Platform Unified Data Platform Leverage Existing Skills Lower Cost & Complexity of Big Data Adoption 58

59 Skills Gap 59

60 190,000 Projected Shortage In Data Scientists By 2018 Data Scientist.the sexiest job in the 21 st century 60

61 Big Data 61

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65 Every day I wake up and ask, How can I flow data better, manage data better, analyze data better? Rollin Ford, CIO, WalMart Source: The Economist, Data, Data Everywhere Retail and Consumer Insights with Big Data 65

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68 Analytics

69 Analytics 3.0 The Era of Impact Tom Davenport IIA 69

70 In the coming years, 75 to 80 percent of new cloud applications will be data-intensive, and data is becoming much more strategic than applications. Frank Gens International Data Corporation (IDC)) Analyst 70

71 Analytics 3.0 Reference Architecture Event Processing Expert System Real-Time Decisions Social Social Sensors Information Discovery 71

72 Oracle Integrated Solution for Big Data HDFS Oracle NoSQL Database Enterprise Applications Hadoop Oracle Big Data Connectors Data Warehouse In-Da atabase Ana alytics Analytic Applications Interactive Discovery ACQUIRE ORGANIZE ANALYZE DECIDE 72

73 Oracle Big Data Architecture Capabilities Data Acquire Organize Analyze Decide Structured Unstructured Semi- str ructured Master & Reference Transactions Machine Generated Social Media Text, Image Video, Audio DBMS (OLTP) Files NoSQL HDFS ETL/ELT ChangeDC Real-Time Streaming Event Processing Hadoop (MapReduce) ODS Warehouse Graph Model In-Database Analytics Scorecards Dashboards Query Reporting Real-Time Decisions EPM, BI, Social Applications Text Analytics and Search Advanced Analytics Interactive Discovery Manag gement Security, Governance Specialized Hardware Big Data Cluster High Speed Network RDBMS Cluster In Memory Analytics 73

74 Why Now? Big Fast Smart Hyper Data Demand for Accountability and Transparency Fueled by Funding Shortages Increased Consumer Like Services To be Competitive in Global Knowledge Economy 7 Billion People in the World only 317 Millions are Americans To Lead Everyone needs to be educated and equipped with the skills of the future Innovates use of disruptive technologies such as Cloud, Social, Analytics, and Mobile...can make Education More Accessible, Affordable, and Relevant 75

75 21 ST Century Learning Essential Ingredients Proficiency Personalized Data-Driven Science of Learning Exploit Technology to Enhance Learning Experience 76

76 Big Idea About Big Data for Education & Research Why? Increase the quality of decisions made by: Students Faculty Advisors/Counselors Chief Business Officers Colleges Principal Investigators (PIs) 77

77 Big Data in Education The Future is Now! Help predict student success Improve graduation rates and student retention Determine what learner does know and does not know Monitor student s behavior and level of engagement Trigger alerts when the learner is getting of track, bored, or frustrated Increase engagement with game mechanics And more 78

IDEN TIFY GROW Profess ional & Person ADV ENH al ANC ANC E E ANA LYZ E

78 Closed-Loop Student Success & Experience INTERVENE PREDICT EMPOWER INT ERV ENE ACQUIRE Knowled ge & Skills ENG AGE ACQUIRE Knowledge & Skills IDEN TIFY GROW Profess ional & Person ADV ENH al ANC ANC E E ANA LYZ E Consolidate GROW Professional & Personal ANALYZE ENGAGE IDENTIFY SUCCEED ENHANCE 79

79 When we understand the differences in our student needs, and drive our services based on that fact based decision making, we will give them the soil they need to flourish. Anonymous 80

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83 Oracle Big Data Appliance Cost Effective High Performance Hadoop Appliance Fastest, Pre-Configured Hadoop Appliance Best-in-Class Pre-Integrated Software Superfast connectivity to Exadata: 15TB/Hour Partitioned & Non-partitioned; Online & Offline Loads Pre-integrated Software Linux, Java VM, Cloudera Distribution of Hadoop Oracle R Distribution & NoSQL Database Start Small, Add Servers, Add Storage 84

84 Oracle Exadata Cost Effective Fastest Database Machine Speed Up Query Performance X Smart-Scan eliminates IO Bottlenecks Flash Cache enable In-Memory Data Access Parallel Query provides Scale out Processing Fast, Cheap Storage Columnar Compression provides 10X Savings Capacity of Disk; IOs of Flash; Speed of D-RAM Start Small, Add Servers, Add Storage 85

85 Oracle In-Memory DBMS Fastest Query Performance In-Memory Column Store Sales CPU State column Load multiple State values CA Vector Register >100X Faster SIMD Compare Vector all values in Compare 1 cycle all values in 1 cycle Scales Up or Scales out for very large data sets Scans use super fast SIMD vector instructions Billions of rows/sec scan rate per CPU core Joins up to 10X Faster 86

86 Oracle DBMS SQL & R Analyze Data across all your Systems Hadoop R SQL Oracle Database Expand the data and users who can analyze on Hadoop Use Full Power of Oracle SQL and R on all data IB Stream Hadoop Resident data through Fast Big Data Connectors 87

87 Customer Examples 88

88 National Cancer Institute Identifying Relationship between Gene to Cancer Interaction 17,000 Genes 5 Major Cancer Types 60M Patients 20M Medical Publications 89

89 Turkcell Location-based Mobile Billboard on Oracle Event Processing 1.5B Events Per Day 800K Events per Second 50 Simultaneous Campaigns 90

<Insert Picture Here> Introduction to Big Data Technology

<Insert Picture Here> Introduction to Big Data Technology Introduction to Big Data Technology The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into