End-to-End Analytics Across Big Data and Data Warehouse for Data Monetization CON3296
Satya Bhamidipati, Senior Principal, Advanced Analytics Gokula Mishra, VP, Big Data & Advanced Analytics @GokulaMishra Business Analytics Product Group October 26, 2015
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Program Agenda
Drivers for End-to-End
Use Cases
End-to-End Analytics
Q&A
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Program Agenda with Highlight
Drivers for End-to-End
Use Cases
End-to-End Analytics
Q&A
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Data-Driven Decisions #1 for execs1
Analytics key to growth, competitive advantage2
Firms that adopt DDD perform better than firms that don’t
1Gartner 2014 CEO Study, CIO Magazine Study 6
2MIT Study “Strength in Numbers”
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Businesses
• 90% created in last two years • 50 billion devices by 2020 • Governance, risk and
complexity
7
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Data & Analytics Creates New Value
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Data & Analytics Disrupts Industries
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Bigger Smarter
Easier
Cheaper Better
Faster VALUE IN ANY FORM
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Speed over consistency
Visual data discovery
Embedded analytics
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Business user demands are changing
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IoT Cloud
Mobile Social
Data & Analytics
Digital Demands
End-to-End
Digital Forces Powering Growth and Innovation
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Key Components of End-to-End Analytics
SELF SERVICE REPORTS QUALITY &
GOVERNANCE
DISCOVERY & VISUALIZATION
ANALYTICS AT SCALE
UNIVERSAL DATA ACCESS
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1. How and what new data sources are being created that is very important and relevant to you?
2. Are you combining data and analytics to drive new insight?
3. Are you building a data analytics platform that allows for experimentation, self-service as well as SCALE end-to-end?
4. Are your analytics aligned with user analytics maturity?
5. How will you govern data and analytics end-to-end?
Five Questions…
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Program Agenda with Highlight
Drivers for End-to-End
Use Cases
End-to-End Analytics
Q&A
1
2
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4
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Creating Value with Data Combining New Data
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Combat Fraud
Near real Time Data
External Data
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Creating Value with Data Business Insight
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• Accelerate Insights
High Performance Analytics
Customer Segmentation
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Creating Value with Data Optimization
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Optimize Offerings
Large Volumes
Complex Algorithms
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Creating Value with Data IOT
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Improve Safety
Maintenance Data
Sensor Data
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Creating Value with Data Customer 360
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Online Interaction
Real Time Decisions Improve Service
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Program Agenda with Highlight
Drivers for End-to-End
Use Cases
End-to-End Analytics
Q&A
1
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4
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Predictive Analytics
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Data Mining Provides Better Information, Valuable Insights and Predictions
Customer Months
Cell Phone Churners vs. Loyal Customers
Insight & Prediction Segment #1 IF CUST_MO > 14 AND INCOME <
$90K, THEN Prediction = Cell Phone Churner
Confidence = 100% Support = 8/39
Segment #3 IF CUST_MO > 7 AND INCOME <
$175K, THEN Prediction = Cell Phone Churner, Confidence = 83% Support = 6/39
Source: Inspired from Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J. A. Berry, Gordon S. Linoff
R
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Data Challenges and Analytics
• Diverse Data Types
• Extremely Large Volumes
• Complex Algorithms
• 3D Visualization
• Simulation
• Highly scalable computing power
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Traditional Approach for Analysis
• Define the business challenge
• Data Preparation
– Identify
– Extract Data
– Ingest Data
• Build model
• Deploy Model
• Visualize the Results
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Analytics Process
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Data Analyze
Business Objective
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Oracle Solution
• Data in the Database
• Added Algorithms
• Visual Interface
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User tables
Oracle Database
Database Compute Engine
?x
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Analytic Methods
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Classification
Regression
Anomaly Detection
Attribute Importance
Association Rules
F1 F2 F3 F4
K Means Linear regression Naïve Bayes Neural Networks Support Vector Machines Singular Value Decomposition Principle Component Analysis
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Analytics on Hadoop Big Data Appliance
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Requires a Fundamentally New Approach
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quickly transform and enrich it to make
it better
unlock big data for anyone to discover
and share new value
A single intuitive and visual user interface, to...
find and explore big data to understand its
potential
find explore transform discover share
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Oracle Big Data Discovery. The Visual Face of Hadoop
find explore transform discover share
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Analytics on Hadoop
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BDD
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End to End Analytics
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?x
User tables
Oracle Database
Database Compute Engine
Connectors
Connectors
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Oracle Advanced Analytics—Best Practices
1. Start with a Business Problem Statement
2. Don’t Move the Data
3. Assemble the “Right Data” for the Problem
4. Create New Derived Variables
5. Be Creative in Analytical Methodologies
6. Quickly Transform “Data” to “Actionable Insights”
7. Automate and Deploy Enterprise-wide
Everything is Different; Nothing is Different
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Today’s Evolving Analytics Infrastructure
Data Fast Data
Events Actions
1 2 3
Streams
Data Management
Reservoir Factory Warehouse
Results
People
Data Services
Smart Things
Data Lab
Data Science Discovery Data Sets
Apps
Packaged
Custom
Business Analytics
Visualization
Reports
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• Built for cloud with enterprise class software
• Business driven self-service
• Advanced visualizations including mobile
• Fast cloud based data loading & metadata modelling
Most Complete
BI Cloud Service
BICS
BDDCS (Future)
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Program Agenda with Highlight
Drivers for End-to-End
Use Cases
End-to-End Analytics
Q&A
1
2
3
4
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Q & A
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Gokula Mishra @GokulaMishra gokula.mishra@oracle .com
Satya Bhamidipati [email protected].