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2 Big, Fast and Smart Kevin Walsh CTO Oracle Asia R&D Centers

3 The World has Changed Proactive Computing Amplified Collaboration Tools & Processes Sense-making and Visualization Device Webs and Sensor Webs Ubiquitous Displays Abundant Computation and Connectivity Common 3D graphical Interfaces 3

4 Market Overview Exponential Growth in Data Volumes 45 PB+ Maintained by Large Orgs 45%+ Annual Growth 15 out of 17 Industry Sectors in the U.S. Have More Data Than the U.S. Library of Congress 4

5 CIO must lead the transformation of IT to leverage: Cloud Mobile Big Data 5

6 To keep up, Enterprises must master BIG Data as raw material Fast Insights to Algorithms Smart 6 Analytics & Data Science

7 [We re in] a new regime: one where large amounts of relevant digital information exist on virtually any topic of interest to a business. McAfee and Brynjolfsson Harvard Business Review October

8 8 200m 100m 2.5 Customers Served per Week Transactions per Hour Petabytes of Data

9 The old way What Caused this to happen? We Don t really know Static Information about historical events 9

10 What Does a Big Data World Look Like? Retailer / White Goods What they collect Food monitoring by sensors refrigeration monitors food usage and sell-by dates How they use it Automated food ordering Better targeted marketing due to better data about how/when customer uses products Big Data means More detailed information about customers Better store based demand planning We can predict customer behavior using Real-World Data and Real-time Experimentation 10

11 Welcome to the World of Realtime Analytics shows us what the customer is thinking before buying 11

12 Market Overview Exponential Growth in Data Volumes leads to More Data Challenges 12

13 Need Fast Data Brittle What We Hear Need Integration Inflexible Devices N i c h e Security Platforms No SQL Rigid 13 Testing Agility Sensors Slow Proprietary Scale CLOUD Analytics Lifecycle Mgt B I G D ATA

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15 Challenge: How to Turn This into Information 15

16 Data Exhaust A Valuable Resource For Predictions Example : Location Data is a byproduct of mobile phone usage. This data has tremendous value for understanding social interaction, buying patterns, public transit, advertising, etc. Asia is the largest generator of such data More than 800 Million mobile phones in China, and 100 million in Japan 16

17 Analytics from 330 million Smartphones 17

18 Social Engagement is Being Driven by Customer Adoption of Social Media as a Life Facilitator New social platforms are emerging to meet expanded customer needs 51% of consumers follow a retailer to get information on deals and coupons 18 42% of TV viewers are actively co-engaged with a technology device often in a social context 17% of consumers have used social media to obtain a service response Sources: Facebook 10Q, June 2012 (Total Monthly Active Users); Comscore Datamine Study, February 2012; Nielsen/NMIncite Social TV Study, November 2011; American Express Global Customer Response Barometer Study, May 2012

19 SoftBank Finding Communication Customers Hidden Voice Objectives Discover customers hidden voice Explain and predict customer behavior Solution We will continue to put all our strength into our endeavors to further accelerate the Information Revolution and bring happiness for everyone Masayoshi Son Chairman & CEO SoftBank Mobile Corp. Capture customer social media activity Analyze sentiment and identify hot words Correlate social sentiment with customer service data Understand what happened, why it happened and what will happen next 19 Social Data Stream Text &Sentiment Analysis EDW & Sandbox BDA Exadata Information Discovery Exalytics

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21 Oracle Engineered Systems Big Exalogic Data Elastic Appliance Cloud 21 Exadata Database Machine SPARC SuperCluster Extreme performance Faster time to value Lower total cost of ownership Reduced risk Easier to scale, easier to manage One-stop support Exalytics In-Memory Analytics

22 Engineered Systems Simplify Big Data Platform for the Intelligent Enterprise Oracle Big Data Appliance Oracle R Engine Oracle RTD Learning Models Oracle Endeca Information Discovery Oracle Exadata Oracle Exalytics InfiniBand InfiniBand CEP Oracle RTD Decisions Acquire 22 Organize & Discover Analyze Decide

23 5 MB Enterprise Data In 1957 IBM 350 Disk 23 >130 Million Times More Data 648 TB in 2012 Oracle Big Data Appliance

24 Engineered for Extreme Performance Oracle Engineered Systems Optimized Out of the Box 24 In-Memory Database Machine 10X FASTER parallel jobs Exadata 10X to 50X Storage FASTER Servers data warehouse and OLTP Exabus 4X Throughput, 6X Lower Application Application Latency

25 Exadata Best of Disk, Flash, Memory in One Solution Smart Flash Smart Scan InfiniBand HOT WARM Smart Flash Cache Smart Flash Log Flash Disk? Gb /sec COLD Smart Flash Caching boosts Flash capacity 25 CPU offload processes at full Flash bandwidth Lowest latency, highest data transfers

26 What is Fast Data? Turning High Velocity Data into Value It s about getting more from in-flight data It s about faster action, faster insights It s about visibility and predictability It s about running your business in real-time 26

27 Fast Data Adds Value to Big Data Streams Event Event--Driven Enriched Stream Binary / Sensors HTML Expert System Decision Engine Event Processing XML Financial Trans + GeoXY EDI Financial Transaction Time Ordered and does not END External/Internal Event Sources from a Variety of Event Technology Generators: Social Media Streaming Tellers Credit Cards 27 Extreme Through-put Microsecond latency Cache, Process, Route & Push Main Patterns to Implement: Filter Enrichment Aggregation Routing, Distinct Event Patterns Pushing Only Valued Events Dynamically Push the Right Event on time. Event Well Formed and routed. Capability to aggregate data on-fly

28 Key Value Drivers of Timely, Accurate Action Delivering Tangible Results with Fast Data New Services 28 Better Customer Experience Improved Efficiency Higher Quality In Operations

29 Fast Data use case Next generation traffic monitoring with agility Telecom in Japan Oracle Event Processing, Oracle Coherence, Oracle BI with Exalogic & Exadata Detect the failure sign or the trend by capturing the multi-layer network traffic Enrich analysis with historical data Easy to adjust the monitoring KPI 29 Visualize Action

30 Oracle Fast Data Filter, Move, Transform, Analyze, and Act at High Velocity 30 FILTER & CORRELATE MOVE & TRANSFORM Oracle Event Processing Oracle Coherence Oracle GoldenGate Oracle Data Integrator ANALYZE Oracle Business Analytics ACT Oracle Real-Time Decisions Oracle BPM

31 Customer Example: Turkcell Location-based Temporal Mobile Billboard Over 1.5B Events Processed Daily Exceeded expectations by processing over 800,000 subscriber related events per second (with 1.5 Billion Events Daily) Provided and executed over 50 simultaneous campaigns Ensured customer responsiveness with less than 1 second times with a scalable architecture, ready to expand on demand Ran Oracle Event Processing, Exadata, Oracle Data Integrator, Oracle GoldenGate 31

32 Velocity Demand Fueled by M2M Growth The New Reality of the Internet of Things Data from things is growing 22X over 5 years from Devices are becoming more intelligent Devices are becoming more connected Source: CISCO VNI Mobile

33 Smart Machines and M2M JR East Smart Vending Machine Vending Machine uses sensors to determine age, sex, buying intention of customer. Collects different information like temperature, average crowd size, past history to offer targeted beverage to specific customers. Shuts itself down to save power when nobody is around. Linked to supply chain, nearby machines wirelessly 3X more sales than standard Machine 33

34 Companies Moving Closer to Demand-Driven Vision Store and Daily Consumption-Driven Planning Aggregate Planning Account Team Reporting Evolution in Use of Consumption Data 34 Vision The DemandDriven Value Chain

35 In-Memory Applications Smart Applications run times faster Product In-Memory Applications Cost Management Policy Analytics Next Best Action Project Discovery Financial Position Analyzer Financial Allocations Analyzer Labor Rules and Monitoring Sales Advisor Project Portfolio Management Virtual Close Consumption-Driven Planning Performance-Driven Planning Logistics Command Center 35 Business critical applications and workflows often take hours or days to execute Financial Close, Cost Management, Projections, Planning Oracle In-Memory Applications leverage DRAM and Flash memories to run times faster Transforms batch processes to real time Changes business dynamics Quickly discover growth opportunities Make smarter decisions Reduce corporate costs Accelerate time-consuming workflows

36 Oracle In-Memory Consumption-Driven Planning Using Fast Data to make smarter decisions Solution Overview Utilize daily store-level consumption data to Safety stock and store replenishment plan create time-phased sell-in forecast Create a time-phased store replenishment plan Scalability for store, daily, intra-day planning Consumption-driven and traditional DC- level demand planning in single system, rolling up to enterprise plan Integrated execution with VCP and EBS 36 Sell-in calculations based on consumption data

37 SMART Business Driven by Analytics and Data First we guess it. Don t laugh, that s really true. It doesn t make a difference how beautiful your guess is, it doesn t make a difference how smart you are if you made the guess, or what his name is... If it disagrees with experiment, it s wrong. That s all there is to it. Richard Feynman 37

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39 The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. 39

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