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Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 CON2161 Big Data in Financial Services: Technologies, Use Cases and Implications.

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Presentation on theme: "Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 CON2161 Big Data in Financial Services: Technologies, Use Cases and Implications."— Presentation transcript:

1 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 CON2161 Big Data in Financial Services: Technologies, Use Cases and Implications Jim Acker Global Solution Manager for Big Data Industry Business Unit, Financial Services

2 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2 Understanding the Drivers Executives frustrated with their data gathering and distribution systems Executives Biggest Data Management Gripes:* #1 #2 #3 #4 #5 Dont have the right systems in place to gather the information we need (38%) Cant give our business managers access to the information they need; need to rely on IT (36%) Systems are not designed to meet the specific needs of our industry (29%) Cant make sense of the information we have and translate it into actionable insight (25%) Information is no longer timely by the time it makes it to our business managers (24%) * Source: Oracle Overload to Impact Study 2012

3 Copyright © 2013, Oracle and/or its affiliates. All rights reserved ,000 Status updates 510,040 Comments 2,000,000 Search Queries 204,166,667 s 571 New Websites The data problem just got a lot bigger Leveraging untapped data for commercial gain

4 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 4 The Big Data Opportunity Big Data: Techniques and Technologies that Enable Enterprises to Effectively and Economically Analyze All of their Data

5 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 5 Big Data is ALL Data Unstructured, Semi-Structure and Structured There is always structure. But its not formally defined or anticipated. Social Media, RSS feeds, Videos, DOCs, PDFs, Graphics Semi-Structured. Does not conform to DB tables, but still contains tags or semantic elements. s, log files, machine generated content What is the main difference in this data? Volume, Velocity, Variety, Value What is the main difference in this data? Volume, Velocity, Variety, Value These Characteristics Challenge your Existing Architecture and your Thought Processes These Characteristics Challenge your Existing Architecture and your Thought Processes

6 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 6 Contrast in Big Data Models Demands a new holistic look into data architecture SQL RDBMS Schema on Write Relational DB HDFS Schema on Read Map-Reduce Distributed File System No / Minimal Data Model Explicit Extreme Scale Scale Large Scale Batch / slow – getting faster Processing Real time and batch Minimal Security Robust Flexibility and time to value Advantages Optimized and familiar

7 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 7 Pulling it ALL Together for Business Value Create value from the full range of data sources – Its about using ALL your data – No more sampling Value First – Let the data drive the questions, or … – Test a hypothesis against all your data Still Need Information Management – Once you find value, incorporate IM – Big Data is NOT a Silo

8 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 8 A Word of Caution Gartner Hype Cycle for Big Data You are Here

9 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 9 Big Data in Financial Services

10 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 10 ALL DATA Discover Analyze Plan Predict BETTER DECISIONS FASTER ACTION Big Data is About Analytics 10 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.

11 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 11 Big Data Use Cases Today Finding and Monetizing Unknown Relationships Correlating Diverse Data Sets Drive Opportunity Reduce Cost

12 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 12 Big Data Solutions for Financial Services Two main patterns for how customers are using Big Data IT Optimization Big Data Analytics ETL and batch processingExtended Data Warehouse Mainframe offloadingArchiving Customer 360Omni-channel CX Cross-selling / Geo-fencingPayment Analytics AML / Anti-FraudRisk Management Pricing ManagementCompute Offload (VAR)

13 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 13 IT Optimization

14 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 14 Big Data Usage Pattern ETL and Batch Processing Workloads on Hadoop Integrate SQL NoSQL Scalable Flexible Cost Effective DW & BI AnalyticsWeb Mainframe

15 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 15 Regions Bank Objectives Meet ever evolving regulatory requirements Consolidate existing deposit, loan and customer databases Solution Big Data Appliance and Exadata ODS for single, reliable, cleansed data source ODS is single landing zone and archival repository for internal, external, structured, semi-structured, and unstructured data Results & Benefits Faster access to all their data Reduced IT costs by eliminating duplicate data stores Results & Benefits Faster access to all their data Reduced IT costs by eliminating duplicate data stores

16 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 16 Thomson Reuters Objectives Maximize cross-sell opportunities Lower cost and complexity Solution Economically capture all customer activity Testing 50M events/sec ingest rates into the Oracle Big Data Appliance Feeds Exadata EDW for customer profitability & segmentation analysis Rick King Chief Operating Officer for Technology Thomson Reuters Oracle's engineered systems… are geared toward high performance big data delivery - and that is exactly the type of work we do BDAExadataExalytics EDW Sandbox & DR Event Capture & Store Interactive Analytics Research Applications Upsell/Cross Sell

17 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 17 Big Data Usage Pattern Expand Data Warehouse with Granular Data Store Marts Data Warehouse Σ Σ Business Intelligence Archiving Online Scalable Flexible Cost Effective Data Factory

18 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 18 End-to-end business information environment that provides accurate, transparent and timely information to shareholders, regulators and management Objectives Tier 1 Global Bank New Information Management Architecture Results & Benefits Reduce complexity and risk of changes Reduce cost of operation Increased stability & performance Results & Benefits Reduce complexity and risk of changes Reduce cost of operation Increased stability & performance Solution 7 Exadata Racks 16 Node Hadoop Cluster – 33TB Oracle Loader for Hadoop (pending)

19 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 19 Big Data Analytics

20 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 20 Ad-hoc Big Data Usage Pattern Scale-out Information Discovery Online Scalable Flexible Cost Effective Data Factory Continuous On-Demand

21 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 21 Enable customers to learn about stocks and increase buying confidence Cultivate the advisor-client relationship online and acquire smaller clients Objectives Credit Suisse Increased sales through instant access to information Results & Benefits Incremental sales for Bank based on this application for 5 years. Improved customer relationships Results & Benefits Incremental sales for Bank based on this application for 5 years. Improved customer relationships Solution Information Discovery on pooled research data sets in multiple unstructured formats Oracle powers their internal application that advisors utilize to quickly find information on financial metrics

22 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 22 Big Data Usage Pattern Instant Responses based on Historical Analysis Business Intelligence Online Scalable Flexible Cost Effective Integrate EventDecisions

23 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 23 NoSQL for Fraud Scoring Financial Services coordinated theft prevention Objectives Solution Combine data sources for complex scoring Detect, alert analyst with low latency Handle burst seasonal transaction volumes Oracle Coherence cluster for real time transaction object management Oracle NoSQL Database for fraud model and customer profile management Oracle Database for statistics and fraud modeling-related data Application Data Ingestion Transaction Authorization Processor Transaction Authorization Processor NoSQL DB Driver Results & Benefits Simple data model, flexible transactions Scalable, Low Latency data management Easy configuration and administration Enterprise Support Results & Benefits Simple data model, flexible transactions Scalable, Low Latency data management Easy configuration and administration Enterprise Support

24 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 24 Real-time Location-Based Offers Tier 1 Global Bank Objectives Customer profile enrichment with Big Data Capture credit card POS and merchant data with event processor Determine geo location of POS and nearby bank wholesale customers Leverage real-time decision engine to generate offer to mobile device Solution Increase revenue through real-time, location based offers

25 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 25 Tier 1 Global Bank Offer Workflow Capture credit card transactions & identify customer location Derive next best offer using customer information and propensity Evaluate offers based on customer location Make offer through mobile text message Locate and identify customerSelect next best offer Identify next best offersMake offer Analyze customer acceptance/ rejection Enrich propensity based on acceptance/rejection

26 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 26 ATM MACHINE POS DEVICE SMART PHONE APP Event Capture and Co-relation Temporal cache based customer identification REAL TIME EVENT CAPTURE TCPIP IFX XML Routing Integration adapters Rules Mapping Real-time/Near Time, Batch DATA TRANSPORT LAYER XML IFX TCPIP Real time decision Real time intervention – click to chat, click to call Adaptive self- learning Near real-time analysis and dashboarding INTELLIGENT INTERVENTION PLATFORM WEBSERVICES MQ NEXT BEST ACTION EXECUTION Near time/Batch for acceptance/rejection data Near time/Batch to perform model update FACTORY BANK REPOSITORIES Client profile, historical transactions, Good life data, segment info, profit info, risk info, Opt-in information etc. KEY VALUE PAIRS Map information, social networks, device logs, smart app interfaces etc. STAGING Structured, Non- structured, Semi- structured MapReduce + NLP Derived outputs- intent, segment, enhanced customer mastering ETL/Real-Time Statistical modeling – Propensity, segments etc. Natural language processing Intent and semantic inference Advanced model free visualization DATA VISUALIZATION LAYER DATA PROCESSING LAYER DATA STORAGE LAYER System Architecture Oracle Big Data at Work LEGEND

27 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 27 Product Roadmap

28 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 28 Engineering the Oracle Big Data Solution StreamAcquire – Organize – Analyze In-Database Analytics Data Warehouse Oracle Advanced Analytics Oracle Database Oracle BI Enterprise Edition Oracle Real-Time Decisions Endeca Information Discovery Decide Oracle Event Processing Apache Flume Applications Oracle NoSQL Database Cloudera Hadoop Oracle R Distribution Oracle Big Data Connectors Oracle Data Integrator Unified Analytics APIs

29 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 29 Why Make Big Data a Divided World? VS

30 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 30 Goal: Unified Data Analytics Environment VS Real-Time Analytics Thousands of Users Secure and Available All Data On- line and Ready to Use Large Scale Systems Cost Effective

31 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 31 Unified Data Analytics Environment Unified Analytics API SQLR MR Unified Analytics Processing Platform HadoopRDBMS IB Management Framework and Tools

32 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 32 Analyze Data across your Oracle Systems SQL Analytics on ALL data Expand the data pool for analytics leveraging Hadoop Stream Hadoop resident data through Big Data Connectors for SQL processing Use the full power of Oracle SQL on all data Or use Oracle Loader for Hadoop to integrate data in Oracle Database SQL HadoopOracle Database IB

33 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 33 Analyze Data across your Oracle Systems R Analytics on ALL data Expand the data pool for analytics leveraging Hadoop Improve scalability and performance for R without changes to your programs Dynamically leverage Hadoop through Big Data Connectors to execute R analytics R HadoopOracle Database IB

34 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 34 Unified Data Analytics Environment Real-Time Analytics Thousands of Users Secure and Available All Data On- line and Ready to Use Large Scale Systems Cost Effective

35 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 35 Unified Big Data Environment VS &

36 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 36 Oracle Big Data Solution StreamAcquire – Organize – Analyze In-Database Analytics Data Warehouse Oracle Advanced Analytics Oracle Database Oracle BI Enterprise Edition Oracle Real-Time Decisions Endeca Information Discovery Decide Oracle Event Processing Apache Flume Applications Oracle NoSQL Database Cloudera Hadoop Oracle R Distribution Oracle Big Data Connectors Oracle Data Integrator Complete Integrated Scalable

37 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 37

38 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 38


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