technology trends for big data analytics · sas analytics refers to features of sas software that...

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

TECHNOLOGY TRENDS FOR

BIG DATA ANALYTICS

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

WHAT IF YOU

COULD…

. . . predict the buying behavior and decision criteria of your prospects

weeks before your competition

. . . gain first-mover advantage by introducing new products and

services to micro market segments that haven't been identified by

anyone

. . . evaluate the impact of your marketing campaigns hourly and

make adjustments in real-time

Copyright © 2012, SAS Institute Inc. All rights reserved.

ANALYTICS INSIGHTS DATA

INFORMATION MANAGEMENT

DECISIONS


INFORMATION

MANAGEMENT CAPABILITY VIEW

DATA SERVICES

Analytics

Management

Data

Management

DATA & ANALYTIC SERVICES

DATA GOVERNANCE ANALYTICS GOVERNANCE

DATA

INTEGRATION

ENTERPRISE DATA ACCESS

Infrastructure

Support

INFRASTRUCTURE SUPPORT:

Text & Unstructured Data Support, Events, Security, Meta-data & Lineage, Monitoring & Deployment

DATA

QUALITY MDM

DECISION

MANAGEMENT

MODEL

MANAGEMENT

&

MONITORING

MODEL

DEPLOYMENT

&

INTEGRATION


SAS Analytics refers to features of SAS software that offer data-driven insight for better decisions.

SAS Analytics encompasses a range of techniques for collecting, analyzing, and interpreting data in order to reveal patterns, anomalies, key variables, and relationships.

We offer a comprehensive suite of analytics software to help you reduce uncertainty, to predict with precision, and to optimize performance.

SAS ANALYTICS - DEFINITION OUR PERSPECTIVE



FORECASTING

DATA MINING

TEXT ANALYTICS

OPTIMIZATION

STATISTICS

Finding treasures in unstructured data

like social media or survey tools

that could uncover insights

about consumer sentiment

Mine transaction databases

for data of spending patterns

that indicate a stolen card

Leveraging historical data

to drive better insight into

decision-making

for the future

Analyze massive

amounts of data in

order to accurately

identify areas likely to

produce the most

profitable results

ANALYTICS

INFORMATION

MANAGEMENT


EXTERNAL

VIEWPOINT CHALLENGES IN

ANALYTICS ADOPTION

Source: The Current State of Business Analytics: Where Do We Go From Here?

Prepared by Bloomberg Businessweek Research Services, 2011


VOLUME

VARIETY

VELOCITY

VALUE

TODAY THE FUTURE

DA

TA

SIZ

E

THE CHALLENGE?


OUR PERSPECTIVE

Big Data is RELATIVE not ABSOLUTE

Big Data

When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making


TRADITIONAL

ARCHITECTURE

ENTERPRISE ARCHITECTURE APPROACH OFTEN

NOT APPLIED TO ANALYTICS INFRASTRUCTURE


Domain Expert

Makes Decisions

Evaluates Processes and ROI

BUSINESS

MANAGER

Model Validation

Model Deployment

Model Monitoring

Data Preparation

ANALYTICAL

DBA

Data Exploration

Data Visualization

Report Creation

DATA

SCIENTIST

Exploratory Analysis

Descriptive Segmentation

Predictive Modeling

DATA MINER /

STATISTICIAN

IDENTIFY /

FORMULATE

PROBLEM

DATA

PREPARATION

DATA

EXPLORATION

TRANSFORM

& SELECT

BUILD

MODEL

VALIDATE

MODEL

DEPLOY

MODEL

EVALUATE /

MONITOR

RESULTS

NEW ANALYTICS LIFECYCLE


Technology

Checklist for

Big Data

Analytics

• A flexible architecture that supports many data types and usage patterns

• Upstream use of analytics to optimize data relevance

• Real-time visualization and advanced analytics to accelerate understanding and action

• Collaborative approaches to align Business and IT executives


Analytic Data

Warehouse / Marts

Relational

Data Store

SAS Analyst’s

Desktops

SAS Web Reporting

Clients

SAS Metadata

Server

SAS Compute

Server

Web Application

Server

SAS BUSINESS ANALYTICS FRAMEWORK

Server Tier Web Tier Client Tier Metadata Tier Data Tier


HIGH-

PERFORMANCE

ANALYTICS

KEY COMPONENTS


HIGH-

PERFORMANCE

ANALYTICS

SAS® GRID COMPUTING



Analytic Data

Warehouse / Marts

Relational

Data Store

SAS Analyst’s

Desktops

SAS Web Reporting

Clients

Primary

Failover

SAS Compute Nodes Web Application

Server(s)

SAS BUSINESS ANALYTICS FRAMEWORK (ADD GRID)


HIGH-

PERFORMANCE

ANALYTICS

SAS® IN-DATABASE


Analytic Data

Warehouse / Marts

Relational

Data Store

SAS Analyst’s

Desktops

SAS Web Reporting

Clients

Primary

Failover


Server(s)

Enterprise Data Warehouse

with SAS In-Database

Analytics

SAS BUSINESS ANALYTICS FRAMEWORK (ADD IN-DATABASE)



HIGH-

PERFORMANCE

ANALYTICS

SAS® IN-MEMORY ANALYTICS


SAS BUSINESS ANALYTICS FRAMEWORK (ADD IN-MEMORY)


Analytic Data

Warehouse / Marts

Relational

Data Store

SAS Analyst’s

Desktops

SAS Web Reporting

Clients

Primary

Failover


Server(s)

Enterprise Data Warehouse

with SAS In-Database

Analytics

Dedicated Analytics Data Warehouse with

SAS In-Memory Analytics


REQUIRES ENTERPRISE

ARCHITECTURE APPROACH

BIG DATA

ANALYTICS

MARKETING

IN-MEMORY

EDW

ADW

SALES

FINANCE

SUPPLY

CHAIN

RISK

HR


SAS

Enterprise Analytic

Architecture IT Value

• Superior performance and scalability

• Better data governance

• Optimal IT Resource usage

Business Value

• Highly accurate & decisive results

• Derive faster time-to-insights

• Expedite time-to-decision for

competitive advantage


INFORMATION

MANAGEMENT

DECISIONS / ACTIONS / DATA

RAW RELEVANT DATA

LOW COST STORAGE

ENTERPRISE

STREAM IT, SCORE IT, STORE IT


CUSTOMER

CASE STUDY TRADITIONAL ANALYTICS PROCESS

DATA EXPLORATION

MODEL DEVELOPMENT

MODEL DEPLOYMENT

3 HRS


DA

TA

EX

PL

OR

AT

IO

N

MO

DE

L

DE

VE

LO

PM

EN

T

MO

DE

L

DE

PL

OY

ME

NT

CUSTOMER

CASE STUDY HIGH-PERFORMANCE ANALYTICS PROCESS

12 minutes

Past Approach • Daily process begins

with flat file creation at

6:30am – SLA delivered

at ~9:30am.

In-Database Approach • Daily process begins

at 4:00am with EDW load.

• File transferred to SQL Server,

limited to ~350K

customer records based on

specific criteria.

• All operational data loaded

directly to EDW. No flat file or

intermediate processing is

needed.

• 300 step process to support

data mining life cycle.

30 MINUTES TO SCORE ~350k

customers

• 10 step process

• Scoring and customer selection

done in-database against ALL

customer rows

4 MINUTES TO SCORE ~40M

customers

- Scope of customer analysis: 350K vs. 40M

- Monthly collections: $1M-$3M per month

Business Value


CUSTOMER

CASE STUDY TRADITIONAL ANALYTICS PROCESS

DATA EXPLORATION

MODEL DEVELOPMENT

MODEL DEPLOYMENT

167 Hours


84 SECONDS

DA

TA

E

XP

LO

RA

TIO

N

MO

DE

L

DE

VE

LO

PM

EN

T

MO

DE

L

DE

PLO

YM

EN

T

167 Hours CUSTOMER

CASE STUDY IN-MEMORY ANALYTICS PROCESS

Bottom-line Impact:

Tens of Millions of

Dollars

DA

TA

EX

PL

OR

AT

IO

N

MO

DE

L

DE

VE

LO

PM

EN

T

MO

DE

L

DE

PL

OY

ME

NT

MO

DE

L

DE

PLO

YM

EN

T

MO

DE

L

DE

VE

LO

PM

EN

T

DA

TA

E

XP

LO

RA

TIO

N


CONCLUSION


WHY DO WE CARE?

YOUR COMPETITIVE ADVANTAGE

Orient

Observe

Act

Act

Orient

Decide MARKET

OPPORTUNITY

Decide


SAS HIGH-

PEFORMANCE

ANALYTICS


Trusted, analytical-based decisions are needed across the organization

IMPACT OF ANALYTICS SPANS THE ENTERPRISE


HIGHEST

PRECISION

UNRIVALED

PERFORMANCE

GREATEST

DEPTH AND

BREADTH

BEST

BUSINESS

OUTCOMES

SAS® HIGH-PERFORMANCE ANALYTICS


CONCLUSION Final Thoughts

Big Data represents opportunity to enhance market

leadership – how do we leverage this medium for timely

decisioning?

The analytical life cycle is accelerating – how do we help

our customers keep pace?

High-Performance Analytics is THE enabling technology

Business and IT alignment is critical to success – how

do we facilitate the establishment of common objectives

and priorities?

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d . www.SAS.com

[email protected]

http://www.sas.com/

technology trends for big data analytics · sas analytics refers to features of sas software that...

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