This document discusses driving digital transformation with machine learning in Oracle Analytics. It provides an overview of Perficient, an Oracle partner, and their Oracle Analytics practice. It then discusses disruptions in analytics towards augmented analytics. The document outlines typical steps to perform data discovery, preparation, analysis, and prediction using Oracle Analytics Cloud (OAC). It demonstrates the built-in machine learning models and typical workflow to perform machine learning using OAC data flows. Finally, it advertises an upcoming presentation and conference where attendees can learn more.

1. Driving Digital Transformation
with Machine Learning in
Oracle Analytics

2. Shiv Bharti
Practice Director,
Oracle Business Analytics
shiv.bharti@perficient.com
Mazen Manasseh
Director,
Oracle Business Analytics
mazen.manasseh@perficient.com

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
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
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
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
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
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. Steps to Perform Data
Discovery & Analysis

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
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
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
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
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
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
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
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
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
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
Typical Workflow to Use ML with Data
~75% ~25%Historical Data
Training
Data
Validation
Data
Traditional Approach to ML

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
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
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. Demo

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. Q & A

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
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. Thank You