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SAS MODERNIZATIONSEVERAL APPROACHES, SAME GOAL
DEEPAK RAMANATHAN
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DEFINITION
Mod·ern·ize:
To accept or adopt modern ways, ideas, or style
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IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTSDomain Expert
Makes Decisions
Evaluates Processes and ROI
BUSINESS
MANAGER
Model Validation
Model Deployment
Model Monitoring
Data Preparation
IT SYSTEMS /
MANAGEMENT
Data Exploration
Data Visualization
Report Creation
BUSINESS
ANALYST
Exploratory Analysis
Descriptive Segmentation
Predictive Modeling
DATA MINER /
STATISTICIAN
THE ANALYTICS LIFECYCLE
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PEOPLE
PROCESS
TECHNOLOGY
ANALYTICS
LIFECYCLE
MATURITY
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A PEEK INTO THE CUSTOMER’S WORLD
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WHAT IT ARCHITECTS HEAR
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RISE OF THE X86 MACHINES
• Lower acquisition costs Linux on x86-based servers represents significant cost savings
over running UNIX on proprietary hardware
• Lower ownership costs Because running Linux on commodity x86-based servers doesn’t
require specialized support staff or consultants with specific proprietary-system skills
• Easily scale on demand Enterprises can easily scale out to meet increasing performance
needs by adding inexpensive x86-based servers
• No vendor lock-in Migrating away from proprietary systems means enterprises are no
longer limited to only the features that legacy platform vendors choose to add to their
systems
• High levels of reliability, availability and serviceability Today’s x86-based platforms are
making rapid advances in these 3 areas, offering enterprise-class capabilities once thought
to be the exclusive domain of proprietary platforms
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MOORE’S LAW – NEW BOUNDARIES
Galaxy Note 3
Chipset Qualcomm Snapdragon 800
CPU Quad-core 2.3 GHz Krait 400
HTC ONE
Chipset Qualcomm MSM8974AB
CPU Quad-core 2.3 GHz
Chipset Apple A7
CPU Dual-core 1.3 GHz Cyclone
Iphone 5S
Xiomi Redmi
Chipset Mediatek MT6592
CPU Octa-core 1.4
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MODERNIZATIONTHREE DIFFERENT APPROACHES
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ENTERPRISE WIDE
INITIATIVESPRIMARY DRIVERS
COST
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Defining a way forward to a modern SAS HPA deployment.
PLATFORM CONSOLIDATION
• Multiple SAS deployments
• Mixture of SAS products
• Frustrated IT
• Interested in cost
• Divisional or Enterprise
level
PLATFORM Modernization
• Lacking capability
• Performance issues
• Frustrated Users
• Interested in a structured
analytics platform
• Departmental, Divisional or
Enterprise level
PLATFORM INTEGRATION
• New or legacy sites
• Data platform strategy
• Working with Partners
• Close alignment of data
and analytics platforms
• Partners: Teradata, Oracle,
Netezza & Greenplum
ENTERPRISE WIDE
INITIATIVESTHREE APPROACHES
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PLATFORM MODERNIZATION
AND OPTIMIZATION
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PLATFORM
MODERNIZATIONPRIMARY DRIVERS
COST Performance Drivers:
• Performance issues
• Slow-running jobs and processes
• Lack of capacity or capability
• Missed business opportunity
• Regulatory pressure
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PLATFORM
MODERNIZATIONSTRATEGY
Defining a way forward to a modern SAS HPA deployment.
PLATFORM Modernization
• Lacking capability
• Performance issues
• Frustrated Users
• Interested in a structured
analytics platform
• Departmental, Divisional or
Enterprise level
• Everything starts with the business process
• Identifying pain points / bottlenecks
• Understand Data usage
• Proactive: Move SAS to more strategic enterprise
level
• Aim is to define a way forward to a modern SAS
deployment utilising HPA products (Grid, In-Database
and In-Memory).
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PLATFORM
MODERNIZATIONUSING THE ANALYTICS LIFECYCLE
IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTS
Individual SAS components become (more)
relevant over lifetime of the deployment
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ENTERPRISE WIDE
INITIATIVESANALYTICS CAPABILITY ASSESSMENT
Define target state using SAS technologies
Gather information, diagnose and analyze
• Applications• Perceived Value• Information Gaps• Analytical Process• Users & Approach• Toolsets• Required SLAs• Distribution Channel• Information Services• Support Organisation
INFORMATION ARCHITECTURE
• Required Data Stores
• Volumetrics• Data Flows• Tools & Interfaces• Data Management Services
• Metadata• Service Levels
DATA ARCHITECTURE
• Server Architecture• Workloads• Installed Toolset• Networks• Restrictions• Support Organisation
• Standards
INFRASTRUCTURE & TECHNOLOGY
• Strategy & Direction• Current Challenges & Requirements
• User Experience• Analytical Processes• Governance & Stewardship
• Capability• Cost Drivers
BUSINESS PROCESS
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PLATFORM CONSOLIDATION
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ENTERPRISE ANALYTICS
PROGRAM ENTERPRISE ANALYTICS PROGRAM
Enterprise Analytics Platform
Business Initiatives
ImplementationExploitation of
New Opportunities
Busine
ss
Functio
ns
IT Function
Business Functions
Analytics Competency
Centre IT
Function
Operating
Model
Business As Usual
Enterprise
ArchitectureIT Governance
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PLATFORM INTEGRATION
(WITH IDENTIFIED PARTNERS)
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PLATFORM
INTEGRATIONPRIMARY DRIVERS
COST
Integration Drivers:
• Data platform strategy
• Storage refresh approach
• Data issues with EDW
• Increasing data volumes
Client Activity Triggers
• Procurement activity for EDW & RDBMS
appliance technologies (new or replacement)
• Building strategy to incorporate Hadoop & other
storage/processing mechanisms
• Business partner strategy for client account
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PLATFORM
INTEGRATIONSTRATEGY
• Aim is to identify an appropriate approach with the data
and analytics platforms that is based on SAS HPA
products (Grid, In-Database and In-Memory)
Defining a way forward to a modern SAS HPA deployment
PLATFORM INTEGRATION
• New or legacy sites
• Data platform strategy
• Working with Partners
• Close alignment of data
and analytics platforms
• Partners: Teradata,
Oracle, IBM, Pivotal &
SAP
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Enterprise Data Warehouse
Data Marts
3rd Party Applications Reliant
on the EDW
Inform
ation Management Layer for Real-time, Near Real-time , Batch
(ETL, ELT, MDM, Message Buses, Event Middleware etc.)
Operational System
Operational System
Operational System
External Data
Internal
Unstructured
Data
Other!!!!
MDM Hub / Reference Data
Hadoop
In-Memory Analytics Engine(s)
Event Streaming Engine
Reporting, Analytics, Business
Solutions and Operational
Decision Making
• Visualization / Exploration &• Reporting• Analytics• Risk• Fraud• Marketing• .. & more
Management
Business Users
Other
Analysts / Data Scientists
Operational Decision Making
PLATFORM
INTEGRATIONDATA & ANALYTICS LANDSCAPE
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PLATFORM
INTEGRATIONRECENT EXAMPLES
3 months
Model Development
Data Preparation
5 months 4 months 3.5 hours
Score
21 million
customers
1 month 55 secs
Score
21 million
customers
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BUSINESS ISSUE
• Audience segmentation and campaign optimization
SOLUTION
• SAS High Performance Analytics and Visual Analytics
running on Hadoop
RESULTS
• Aggregate various internal and external data sources
• Model and Visualize in Hadoop cluster
• Using all data vs. samples
• Faster scoring
• Modeling lifecycle