“Innovation through Prediction” - Hybrid Cloud Big Data Platform John Andrew Oracle Enterprise Architect Learn. Predict. Influence.

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Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Agenda Innovation Business Drivers Use Case Context Reference Architecture Context Hybrid Cloud Solution Context Q&A

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. What if … 4 1.You knew what products your customers would be the most likely to buy in advance? 2.You could maximize your profits by determining the highest price a customer will pay for a product? 3.You could optimize customer service to resolve concerns proactively before they become issues? and finally…. 4.Political parties had a way of determining and influencing the voters to vote for them?

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.5 What does it mean for Business… Increased Customer Satisfaction and Revenue Drastically Reduced Business Risk Increased Efficiency and Productivity

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.6 Core business themes and building blocks … Predicting and Influencing Model Accuracy Anytime & Anywhere availability Time to Value and Economy of Scale

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Google Trend Analysis as of Oct Predictive Analytics Prediction Big Data Foundation Model Accuracy Cloud Deployment Economy Of scale

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Hindsight -> Insight -> Foresight Descriptive Analytics Predictive Analytics Prescriptive Analytics Forecasting Trends Relationships Forecasting Trends Relationships Analytics Models Semantics Analytics Models Semantics Reports Alerts Discovery Reports Alerts Discovery RDBMS / SQL (x)OLAP Warehouse RDBMS / SQL (x)OLAP Warehouse Business Expectation Architecture Pattern Prediction Next Best Action Influencing Prediction Next Best Action Influencing Machine learning Decision Hub Optimization Machine learning Decision Hub Optimization 8

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Increasing sources of relevant data can boost model accuracy 9 Naïve Guess or Random 100% 0% Population Size Failure Prediction Accuracy Model with 20 variables Model with 75 variables Model with 250 variables 100% More Data + Variety Data -> Better Predictive Models Model with “Big Data” and hundreds -- thousands of input variables including: Customer sentient data Competitors data Environmental data Spatial location data Long term vs. recent historical behavior Sensor data etc.

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Agenda Innovation Business Drivers Use Case Context Reference Architecture Context Hybrid Cloud Solution Context Q&A

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Predictive Analytics Use Cases 11 Predictive Pricing (Competitive, Dynamic, and Demand) Predicting Influence (Product and Customer) Fraud Prevention (Box tops)

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Collecting All Pricing related Data… “Data Wrangling” 12 Enterprise DataNon Enterprise Data

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. “Training” Pricing model … Machine Learning 13 Customer Preference Consumer Price Index Historical Pricing Avg Inventory Turnover Segment ID Customer Segment Producer Price Index Yes80%.12.60FMHV78% No60%.34.15ANPMV65% No65%.12.30FRLV60% No50%.18.35PURMV55% Yes78%.16.70NUTRHV80% No95%.53.40RBLV90% No74%.45.25CGFHV75% No70%.66.38SFIMV65% Have an algorithm determine what is different and the importance of the differences in the green and gray metrics to figuring out preference

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Advanced Analytics Algorithms More than just linear regression to help predict the future and discovery relationships Logistic Regression Decision Trees Naïve Bayes Support Vector Machines Regression Linear Regression Support Vector Machines Classification Multi-Layer Neural Networks Anomaly Detection One-Class SVM Attribute Importance Minimum Description Length Principal Components Analysis Clustering Hierarchical k-Means Hierarchical O-Cluster Expectation-Maximization Feature Extraction Nonnegative Matrix Fact(NMF) Singular Value Decomposition(SVD) Collaborative Filtering (LMF) Text Mining Tokenization Theme Extraction Algorithms works across data sets (Relational and Non-Relational) 14

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Current State Constraints and Gaps 15 1.Analytical Challenges Misspecification and using a sample to estimate the model Resource (memory) constraints of analytical scripts (R scripts) Analyze data without help from IT 2.Data Management Challenges Complexity and Cost issues resulted using smaller data sets for analytics Smaller the data sets, less accurate analytical outcomes Data latency issues increased as the result of data exists in multiple places 3.Deployment Challenges Up front large CapEx to build and deploy the Platform

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Agenda Innovation Business Drivers Use Case Context Reference Architecture Context Hybrid Cloud Solution Context Q&A

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.17 f(predictive analytics) = ( unified data predictive models +

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Architecture Vision 18 Creating an Unified Data + Advanced Analytic Platform for the Era of Big Data and the Cloud Simple Any data size Any data variety Any platform Unify Enterprise (All) Data Analytical Models User Interaction Secure Control Access Protect Integrity Resilient Reliable Timely Elastic

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.19 Architecture Themes Architecture Fit As-is Data Discovery At-source Data Analytics At scale and Performance Financial Fit Reduced $ per Model CapEx to OpEx Transition Lower TCO Operational Fit Automated Infrastructure Unified Management Simplified Support Model

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.20 On-Premises Architecture Pattern – Ingestion to Analytics Data Ingestion Service 3 rd Party Data Cloud Service csv xls ❶ Big Data Discovery Service Client R Engine ❸ ❺ Big Data Service ❷ Enterprise Data Warehouse ❹ Big Data SQL

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.21 Cloud Architecture Pattern – Ingestion to Analytics Big Data Cloud Service Data Preparation Cloud Service 3 rd Party Data Cloud Service csv xls Client R Engine ❶ ❷ Big Data Discovery Cloud Service ❸ ❺ ❹ Database Cloud Service Big data SQL

Copyright © 2015 Oracle and/or its affiliates. All rights reserved.22 Hybrid Architecture Pattern – Ingestion to Analytics Big Data Cloud Service Big Data Preparation Cloud Service Big Data Discovery Cloud Service 3 rd Party Data Cloud Service csv xls Enterprise Data Warehouse Client R Engine ❶ ❷ ❸ ❹ ❺

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Analytic Reference Architecture Built it once and use it multiple times 23 Foundational Analytical Services Functional Analytical Services

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Reference Architecture Data Flow … Import and Ingest Cleanse and Normalize Repair and Standardize Classify and Extract Augment and Enrich Visualization Orchestration, Integration, Lineage, and Preparation Data Sources Data Storage and Management Data Ingestion Sales & Inventory Continuous Data Warehouse Daily Updates Competitors Data On-demand Archive Mainframe Public Sources (Free) 3 rd Party Sources (Pay) Outside Data Structured Unstructured Structured Oracle Teradata IBM Data Consumption Unified models Sense and respond Mobile interaction Discovery Lab Business Users Analytical Lab Development Enterprise Analytics Analytical Models Data Discovery BI and Analytics Unified Secure Access Search Visualize Transform Share/Subset Regression & classification Anomaly detection Segment analysis Data Movement Data Access Data Reservoir (Semi and Unstructured) Enterprise Data Data Warehouse (Structured) Hadoop (HDFS) NoSQL Meta data Analytic Engine Data Mining Analytic Engine In-memory Processing Spark 24

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Agenda Innovation Business Drivers Use Case Context Reference Architecture Context Hybrid Cloud Solution Context Q&A

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Oracle Analytics as a Service Platform Private, Public and Hybrid Cloud deployment options 26 Graph Analytics

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Predictive Pricing Hybrid Cloud Solution realization 27

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Unified Analytical Model Execution 28 Client R Engine Oracle Database User tables In-db stats/dm Database Server Machine ORE packagesR Functions Oracle R Advanced Analytics for Hadoop Oracle DB Advanced Analytics Big SQL Services Enterprise R Data Mining

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Kick Start Your Hybrid Cloud Big Data Strategy 29 1.Guiding Success Factors Integrate business and technology vision (Identify stockholders that will carry over to implementation). Focus on the next 12 months Identify your target architecture ₋Avoid tunneling on one use case Keep in mind Big Data is not a cure-all Hadoop is a complementary to your existing EDW. It could very well be your “System of truth” but most likely not your “System of record” 2.Jump start your Innovation with Oracle proven reference architecture Oracle proven Analytical platform Oracle proven hybrid cloud deployment

Copyright © 2015 Oracle and/or its affiliates. All rights reserved. Why Oracle Hybrid Cloud Analytical Platform? Enterprise-Grade Cloud Capabilities Discover and Predict – Fast Govern and Secure All Data Simplify Access to All Data PerformanceIntegrationAvailabilityElasticityManageability 30

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