SAS Modernization — Several Approaches, Same Goal · 2016-03-11 · SAS MODERNIZATION SEVERAL...

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Copyright © 2013, SAS Institute Inc. All r ights reserved.

SAS MODERNIZATIONSEVERAL APPROACHES, SAME GOAL

DEEPAK RAMANATHAN



DEFINITION

Mod·ern·ize:

To accept or adopt modern ways, ideas, or style



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



PEOPLE

PROCESS

TECHNOLOGY

ANALYTICS

LIFECYCLE

MATURITY



A PEEK INTO THE CUSTOMER’S WORLD



WHAT IT ARCHITECTS HEAR



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



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



MODERNIZATIONTHREE DIFFERENT APPROACHES



ENTERPRISE WIDE

INITIATIVESPRIMARY DRIVERS

COST



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



PLATFORM MODERNIZATION

AND OPTIMIZATION



PLATFORM

MODERNIZATIONPRIMARY DRIVERS

COST Performance Drivers:


• Slow-running jobs and processes

• Lack of capacity or capability

• Missed business opportunity

• Regulatory pressure



PLATFORM

MODERNIZATIONSTRATEGY

Defining a way forward to a modern SAS HPA deployment.

PLATFORM Modernization

• Lacking capability


• 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).



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



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



PLATFORM CONSOLIDATION



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




(WITH IDENTIFIED PARTNERS)



PLATFORM

INTEGRATIONPRIMARY DRIVERS

COST

Integration Drivers:


• 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



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


• New or legacy sites


• Working with Partners

• Close alignment of data

and analytics platforms

• Partners: Teradata,

Oracle, IBM, Pivotal &

SAP



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



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

Copyr igh t © 2014 , SAS In s t i t u te In c . A l l r ig h ts reserved .

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

SAS Modernization — Several Approaches, Same Goal · 2016-03-11 · SAS MODERNIZATION SEVERAL...

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Transcript of SAS Modernization — Several Approaches, Same Goal · 2016-03-11 · SAS MODERNIZATION SEVERAL...