Technology Strategies 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 .

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

TECHNOLOGY STRATEGIES FOR BIG DATA ANALYTICS

BERNARD BLAIS PRINCIPAL, GLOBAL TECHNOLOGY PRACTICE




VOLUME VARIETY VELOCITY

TODAY THE FUTURE

DA

TA S

IZE

THE CHALLENGE?




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




IDENTIFY / FORMULATE

PROBLEM

DATA PREPARATION

DATA EXPLORATION

TRANSFORM & SELECT

BUILD MODEL

VALIDATE MODEL

DEPLOY MODEL

EVALUATE / MONITOR RESULTS

Domain Expert Makes Decisions Evaluates Processes and ROI

BUSINESS MANAGER

Model Validation Model Deployment Model Monitoring Data Preparation

IT SYSTEMS / MANAGEMENT

Data Exploration Data Visualization

DATA SCIENTIST

Exploratory Analysis Descriptive Segmentation Predictive Modeling

DATA MINER / STATISTICIAN

How can you create competitiveadvantage?

THE ANALYTICS LIFECYCLE




HIGH-PERFORMANCE

ANALYTICS KEY COMPONENTS




IDENTIFY / FORMULATE

PROBLEM

DATA PREPARATION

DATA EXPLORATION

TRANSFORM & SELECT

BUILD MODEL

VALIDATE MODEL

DEPLOY MODEL

EVALUATE / MONITOR RESULTS

Domain Expert Makes Decisions Evaluates Processes and ROI

BUSINESS MANAGER

Model Validation Model Deployment Model Monitoring Data Preparation

IT SYSTEMS / MANAGEMENT

Data Exploration Data Visualization

DATA SCIENTIST

Exploratory Analysis Descriptive Segmentation Predictive Modeling

DATA MINER / STATISTICIAN

How can you create competitiveadvantage?

HIGH-PERFORMANCE





HIGH-PERFORMANCE


DEPLOY FASTER

DECISIONS

PREPARE BIGGER

DATA

DEVELOP BETTER

RESULTS

CORE OPPORTUNITY

In Memory

Grid Computing / In Memory

In Database / In Memory




HIGH-PERFORMANCE

ANALYTICS SAS® GRID COMPUTING




HIGH-PERFORMANCE

ANALYTICS SAS® IN-DATABASE


1. Acquire 2. Determine Relevance

3. Store

Trash Cache Storage

HOW DO WE MANAGE DATA IN THE PHYSICAL WORLD?


Data Acquisition Data Transformations

Data Normalization

Queries

Systems Users

Relevance is traditionally determined at query time . . .

“Acquire, Store, Analyze”

A Big Data Analytics strategy requires a new approach . . . “Stream it, Score it, Store it”

DATA


HOW DO WE MANAGE INFORMATION IN THE IT WORLD?




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




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




HIGH-PERFORMANCE

ANALYTICS SAS® IN-MEMORY ANALYTICS




EXPLORATION AND VISUALIZATION IN-MEMORY

ARCHITECTURE

> 1.1 BILLION RECORDS

10 SECONDS




MODEL DEVELOPMENT & DEPLOYMENT IN-MEMORY

ARCHITECTURE

82 SECONDS

5½ HRS


Billions of Purchase

Transactions

Tailored and Real-time Marketing Campaigns

CUSTOMER CASE STUDY Customer Segmentation




CUSTOMER CASE STUDY TRADITIONAL ANALYTICS PROCESS

DATA EXPLORATION

MODEL DEVELOPMENT

MODEL DEPLOYMENT

167 Hours




84 SECONDS

DA

TA

EXP

LOR

ATIO

N

MO

DE

L

DE

VE

LO

PM

EN

T

MO

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L D

EP

LOY

ME

NT

167 Hours CUSTOMER

CASE STUDY IN-MEMORY ANALYTICS PROCESS

Bottom-line Impact: Tens of Millions of

Dollars




SAS HIGH-PEFORMANCE

ANALYTICS




SAS HIGH-PEFORMANCE

ANALYTICS




SAS HIGH-PEFORMANCE

ANALYTICS




BEST PRACTICE Business Analytics Maturity Assessment

Overview: Two-day on-site discovery session focused on understanding the client’s business and IT objectives, key initiatives, existing information management and analytics architecture, top challenges, and priorities.

Process: • Review current business requirements, timeframes, critical success factors, and key

business metrics (e.g. customer retention, customer acquisition). • Review operational data sources to support business priorities. • Review analytical priorities, strategy, process, and gaps.

Deliverables: • Technology roadmap to optimize the client’s current and future IT-enabled analytical

process. • Projected high-level ROI analysis resulting from proposed analytical architecture and

process improvements.



INDUSTRY

COMPANY

USE CASE

VALUE

SAS PROVEN VALUE PROPOSITION ACROSS MULTIPLE INDUSTRIES

FINANCIAL SERVICES

PUBLIC SECTOR TELCO RETAIL SERVICES

Risk Management

Revenue Leakage

Campaign Optimization

Inventory Management

Promotions Management

• 356X faster risk calculations

• Faster in/out markets

• Better able to audit

• Detect issues pre-refund

• 15% better campaign response rates

• Markdown optimization – from 30 hours to 2 hours

• More precise than competition

• Coupon redemption rate +15%

http://www.catalinamarketing.de/index.html




USE CASE In-database Model Scoring

Overview: The largest customer behavior marketing company in the world, Catalina Marketing analyzes and

predicts shoppers’ buying behaviors to generate customized point-of-sale color coupons, advertisements and informational messages for retail stores and pharmacies nationwide.

Process and Deliverables: Leveraging In-database scoring, automated the execution of scoring models against their entire

140 million consumer database;

Impact: Catalina Marketing has reduced its model-scoring times from 4.5 hours to around 60 seconds

using SAS Scoring Accelerator. As a result, it is able to use more complex, varied models to obtain analytical results faster for more efficient, reliable decisions -- improving brand performance on behalf of its food, drug, and mass advertising and marketing partners.

Implementation of marketing campaigns in days vs. more than 1 month before.

60 SECONDS

4½ HRS

RETAIL




USE CASE Credit Risk on Banking Data

Overview: Data Source: Bank loan portfolio covering: 3 million loans; 5,000 stress scenarios; 40 time horizons; Transition matrix approach

Process and Deliverables: Estimates of credit losses under stress over multiple horizons. Completed compute time: under 3 minutes.

Impact: Fast estimates of credit losses under stress over multiple horizons,

enables the Bank to make changes to lending practices throughout the day

3 MINUTES

FINANCIALSERVICES




USE CASE Text Mining on Unstructured Data

Overview: USA’s National Highway Traffic Safety Administration

700,000 accident reports on Vehicles make and models, manufacturing date, purchase date, failures, mileage, number of cylinders, etc… Car components, Accidents information, etc

Process and Deliverables: Text Mining on accident reports. Analyze, Understand, Validate and Predict contents.

Report on content categorization. Text mining process runs in 1 minute 22 second on a High Performance Analytics Server, instead of in 5 ½ hours on a regular server.

Impact: 99% time improvement means the whole process can now be considered an ITERATIVE,

DYNNAMIC process

Analyst can run it 20 times before lunch, each time fine-tuning the model and improving the output, instead of maybe twice during the whole week.

82 SECONDS

5½ HRS

PUBLIC SECTOR




USE CASE Forecasting On Smart Meter Data

Overview: Oklahoma Gas & Electric Company (OG&E) serves nearly 800,000 customers in

Oklahoma and western Arkansas. It was named the 2011 Utility of the Year.

Forecast energy demand with SAS Analytics, plan for future changes to its energy portfolio and optimize programs that encourage wiser use of energy.

Process and Deliverables: Use smart meter data coming from customers every 15 minutes (versus once a month) to

create and measure the effectiveness of programs that reduce energy consumption.

Impact: What previously took one to three days can now be done in a matter of hours.

We've gone from receiving 12 records for each customer to over 30,000 records per year.

30,000 records

12 records

UTILITIES




CONCLUSION What High Performance Analytics Really Mean

It’s not just about incredible speed, it’s also about:

Confidence: No more sampling, subsetting, summarizing

Accuracy: More complex models, more variables

Efficiency: Leverage the Analytical Brain on valuable tasks

Agility: Adapt and (re)Act faster




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

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