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Driving Digital Transformation with Machine Learning in Oracle Analytics

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The adoption of machine learning (ML) is increasing at near-breakneck speed. As organizations seek innovative ideas on how to improve the business, Oracle Analytics Cloud with ML capabilities is leading the charge. With built-in drag-and-drop functions into visualizations and autonomous prediction execution, Oracle Analytics puts the power of machine learning in your hands.

We covered how Oracle Analytics can connect various data sources, allow you to apply ML without being statistically savvy, and easily build your story in presentation format.

Discussion included:

-In-depth look at Oracle Analytics Cloud
-How to connect different data sources like SaaS applications, data lakes, external data sources and more
-Custom-trained ML models demonstration
-Real-world business use case from end to end

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Driving Digital Transformation with Machine Learning in Oracle Analytics

  1. 1. Driving Digital Transformation with Machine Learning in Oracle Analytics
  2. 2. Shiv Bharti Practice Director, Oracle Business Analytics shiv.bharti@perficient.com Mazen Manasseh Director, Oracle Business Analytics mazen.manasseh@perficient.com
  3. 3. 3 Agenda • About Perficient • Disruptions in Analytics Market • Steps to Perform Data Discovery and Analysis • OAC ML Models • Typical Workflow to Perform ML • Demo • Q&A
  4. 4. 4 About Perficient Perficient is the leading digital transformation consulting firm serving Global 2000 and enterprise customers throughout North America. With unparalleled information technology, management consulting, and creative capabilities, Perficient and its Perficient Digital agency deliver vision, execution, and value with outstanding digital experience, business optimization, and industry solutions.
  5. 5. 5 Perficient Profile • Founded in 1997 • Public, NASDAQ: PRFT • 2018 revenue est. $495 million • Major market locations: Allentown, Atlanta, Ann Arbor, Boston, Bozeman, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Houston, Lafayette, Milwaukee, Minneapolis, New York City, Northern California, Oxford (UK), Phoenix, Seattle, Southern California, St. Louis, Toronto, Washington, D.C. (metro) • Global delivery centers in China, India and Mexico • 3,000+ colleagues • Dedicated solution practices • ~95% repeat business rate • Alliance partnerships with major technology vendors • Multiple vendor/industry technology and growth awards
  6. 6. 6 PERFICIENT’S ORACLE BI PRACTICE Fast Facts • Practice Started: 2004 • Projects Completed: 400+ • Management Team: 15 years • 60% of consultants former Oracle Eng. • Oracle authorized education center • Oracle Analytics Cloud (OAC), Oracle BI Apps, OBIEE, ODI • Perficient runs it’s business on Oracle Analytics Cloud (OAC) Solutions Expertise • BI/DW strategy and assessments • Oracle Analytics Cloud (OAC) • Machine Learning/Big Data • OBIEE and Oracle BI Apps • Cloud & on-premises solutions • Custom data warehouse services • Master Data Management • Data integration, discovery, big data • Exadata & Exalytics • Oracle Golden Gate Oracle Specializations
  7. 7. 7 Disruptions in Analytics and BI Market Augmented 3  Automation  Machine Learning  Predictive Analytics  Natural Language Query Self-Service 2 Centralized 1  Data Visualization  Adhoc Capabilities  Business Centric Analytics  Common Semantic Layer  Single Source of Truth  Semantic Layer  Pixel-Perfect Reports  IT Centric  Static Reports
  8. 8. 8 Autonomous Analytics Equals Augmented Analytics Business Value Agility Centralized Stronger Governance Self-Service Improved User Productivity Data-Driven Business Dashboards Role-Based Access Control Semantic Layer Pixel-Perfect Reports Smart View ANALYTICS CLOUD ANALYTICS CLOUD Data Visualization Data Flows Storytelling What-If Analysis ANALYTICS CLOUD 1-Click Explain Data Enrichment Adaptive & Personalized Analytics Natural Language Query & Narrate © Oracle Corporation
  9. 9. Steps to Perform Data Discovery & Analysis
  10. 10. 10 OAC for Data Discovery Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover • Leverage OAC adapters to connect to a variety of data sources • Ingest data into the data lake • Use OAC to replicate data into Oracle Big Data or Oracle DB
  11. 11. 11 OAC for Data Preparation Prepare Data Sets • Ease of use: Spreadsheet-like transformations • Remove duplicates, replace nulls, and standardize inconsistent values • Create custom groups and expressions Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  12. 12. 12 OAC for Data Preparation Create Data Flows to Map to New Data Sets • Intuitive data transformation flows • Immediate feedback in data preview • Function shipping: Pushdown of execution into sources • Native execution in Spark for data lake • Load data into data sets, databases or Essbase cubes Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  13. 13. 13 OAC for Data Analysis Visualize & Present • Automatic chart creation based on intelligent data services • Rich palette of built-in visualizations • Single click trending and forecasting, clustering and outliers detection Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  14. 14. 14 OAC for Data Analysis Automatic Explanation of Data Sets • Explore and understand unfamiliar data • Automatic pattern detection • Guide users towards strongest correlated factors and variances from norm Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  15. 15. 15 OAC for Prediction Advanced Transforms and Scripts in Data Flows • Time Series Forecast • Predict possible future trends based on past value patterns • Based on ARIMA model • Sentiment Analysis • Analysis of natural language based on term usage • Custom scripts in Python and R Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  16. 16. 16 OAC for Predictions Machine Learning Data Flows • Use data flows to build machine learning pipelines • Train and score models through data flows Variety of ML Algorithms • Numeric prediction • Multi-classifier • Binary classifier • Clustering • Customer algorithms Step 4 Predict Step 3 Analyze Step 2 Prepare Step 1 Discover
  17. 17. 17 Built-in ML Algorithms Type of ML Model Models Application Examples Supervised Learning Purpose: Numeric prediction against new date Numeric Prediction Scripts: CART, Elastic Net Linear Regression, Linear Regression, Random Forest • How many units do we need to stock in warehouse inventory? • How much revenue is anticipated over the next year?
  18. 18. 18 Built-in ML Algorithms Type of ML Model Models Application Examples Supervised Learning Purpose: Numeric prediction against new date Numeric Prediction Scripts: CART, Elastic Net Linear Regression, Linear Regression, Random Forest • How many units do we need to stock in warehouse inventory? • How much revenue is anticipated over the next year? Supervised Learning Purpose: Prediction against new data; classifications/labels are known Multi-Classifier Scripts: CART, Naive Bayes, Neural Network, Random Forest, SVM • What is the next-best product to recommend to a customer? • Cross-selling likelihood to what other product/service offering? Binary Classification Scripts: CART, Logistic Regression, Naive Baise, Neural Network, Random Forest, SVM • Will a sales opportunity win or lose? (Win/Loss) • Which customers are more likely to renew our service? (Yes/No)
  19. 19. 19 Built-in ML Algorithms Type of ML Model Models Application Examples Supervised Learning Purpose: Numeric prediction against new date Numeric Prediction Scripts: CART, Elastic Net Linear Regression, Linear Regression, Random Forest • How many units do we need to stock in warehouse inventory? • How much revenue is anticipated over the next year? Supervised Learning Purpose: Prediction against new data; Classifications/labels are known Multi-Classifier Scripts: CART, Naive Bayes, Neural Network, Random Forest, SVM • What is the next-best product to recommend to a customer? • Cross-selling likelihood to what other product/service offering? Binary Classification Scripts: CART, Logistic Regression, Naive Baise, Neural Network, Random Forest, SVM • Will a sales opportunity win or lose? (Win/Loss) • Which customers are more likely to renew our service? (Yes/No) Unsupervised Learning Purpose: Understand structure of data without a known classification Clustering Scripts: Hierarchical Clustering, K-Means • Which customer cohorts are more responsive to specific types of trade promotions? • What type of product packaging is more popular in which location, age group, household income, etc.?
  20. 20. 20 Typical Workflow to Use ML with Data ~75% ~25%Historical Data Training Data Validation Data Traditional Approach to ML
  21. 21. 21 Typical Workflow to Use ML with Data ~75% ~25%Historical Data Training Data Validation Data Traditional Approach to ML Train Model • Using Training Data Score Model • Using Validation Data Apply Model • Using New Data 1 2 3
  22. 22. 22 Typical Workflow to Use ML with Data ~75% ~25%Historical Data Training Data Validation Data Traditional Approach to ML Train Model • Using Training Data Score Model • Using Validation Data Apply Model • Using New Data Oracle Analytics ML with Data Flow Data Flow Train Model Function Historical Data ML Model Score 1 1 2 3
  23. 23. 23 Typical Workflow to Use ML with Data ~75% ~25%Historical Data Training Data Validation Data Traditional Approach to ML Train Model • Using Training Data Score Model • Using Validation Data Apply Model • Using New Data Oracle Analytics ML with Data Flow Data Flow Train Model Function Historical Data ML Model Score New Data ML Model 1 2 1 2 3 • Option 1: Apply model directly in visualization by creating scenarios • Option 2: Apply model in data flow to store prediction in data set
  24. 24. Demo
  25. 25. 25 Demo 1. Drag and Drop of Advanced Analytical Functions Forecasting (HR voluntary/involuntary terminations) 2. Explain Feature for Automated Insights Exit survey responses (recommending company to others) 3. ML Model Generation A. Train and score a Binary Classifier ML Model for voluntary terminations B. Apply created model
  26. 26. Q & A
  27. 27. 27 Join us for any one of our eight sessions at Collaborate! We’re pleased to have been selected to present on key topics from Oracle Business Intelligence and Oracle EPM to ERP. Meet our experts and enter to win drawings held at the close of each session! Meet Our Experts at Collaborate
  28. 28. 28 Next up: [Event] Modern Business Experience March 19-21, 2019 Las Vegas [Event] Collaborate 19 April 7-11, 2019 San Antonio Follow Us Online • Perficient.com/SocialMedia • Facebook.com/Perficient • Twitter.com/PRFT_Oracle • Blogs.perficient.com/Oracle
  29. 29. Thank You

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