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SAS CLOUD ANALYTICS MAY 2015

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SAS CLOUD ANALYTICS

MAY 2015

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

SAS SOLUTIONS

ONDEMAND

HISTORY

Established in 2000

Formed as the

Application Service

Provider Group

HP ES40 6/833: Tru64 Unix V5.1

TruCluster. 4 CPU’s, 8 GB

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

SAS SOLUTIONS ONDEMAND

700+ employees worldwide

Multiple lines of business

representing 250+ SAS solutions

450 customer sites with users from

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GLOBAL ADVANCED ANALYTICS LAB

Formed by Dr. Goodnight in 2007

150+ dedicated Data Scientists

97% of analysts hold advanced

degrees in statistics, mathematics,

and operations research, with 40% of

these being PhDs

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

SAS DATA CENTERS

Multiple Global & Regional Data Centers

Customer and Third-party IT options

2500+ managed servers, of which 1700+

are virtual (67%) – 8 PBs managed

storage

Scalable infrastructure with physical and

logical security

Applications monitoring and support 24/7

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SAS SOLUTIONS

ONDEMAND

SECURITY AND CERTIFICATIONS

Hosting Environment

SOC 2 / SOC 3

Safe Harbor

Physical and logical security

Scalable infrastructure

Standard SLA of 99% uptime

Multiple data centers

ISO 27001 international data centers

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

SAS

CLOUD

ANALYTICS

Live

1.

Cary, NC, USA

2.

Charlotte, NC, USA

3.

Toronto, Canada

4.

Wynyard, UK

5.

Dublin, Ireland

6.

Johannesburg, South Africa

7.

Sydney, Australia (2)

In Progress

1.

Frankfurt, Germany

Available

1.

Alphaville + Sao Paulo, Brazil

2.

Barcelona, Spain

3.

L’Isle d’Abeau, France

4.

Cernusco, Italy

5.

Dubai, UAE

6.

Bangalore, India

7.

Seoul, Korea

8.

Beijing, China

9.

Tokyo, Japan

10.

Singapore

11.

Oregon, California, Virginia,

USA

Oregon

California

Sao Paulo

Alphaville

Virginia

Cary

Charlotte

Toronto

Dublin

London

Frankfurt

Cernusco

Barcelona

L’Isle d’Abeau

Johannesburg

Dubai

Bangalore

Singapore

Tokyo

Beijing

Sydney

Live

In Progress

Available

August 2014

Seoul

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SAS CLOUD ANALYTICS OFFERINGS

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

DEPLOYMENT

OPTIONS

WHAT MEETS THE BEST NEEDS OF THE CUSTOMER?

Private

Cloud

Public

Cloud

AWS

Azure

Google

Remote Managed

Services

Platform and Infrastructure

By cloud provider

*Software by SAS*

Platform and Infrastructure

by Customer

*Software by SAS*

SAS

Cloud

SaaS

Enterprise

Hosting

RaaS

IaaS

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CURRENT SOLUTION OFFERINGS

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

SAS CLOUD

ANALYTICS

SOLUTION OFFERINGS

Life Sciences

Organizations that discover and

develop new products, as well as

the companies that service those

firms

Government

State and federal

government organizations

Health Plans (Public/Private)

Organizations that manage

insurance and payments for

consumers and employers

SDD, CTDT, Key

Opinion Leader

Tax Fraud, Workers

Compensation Fraud,

Unemployment Fraud

(Tax and Insurance)

Fraud, Health

Outcomes

Analysis, Episode

Analytics

Child Welfare,

Eligibility Fraud,

Claims Fraud

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SAS CLOUD

ANALYTICS

SOLUTION OFFERINGS

Banking

Retail and Investment

banking and Financial

Services

Insurance

Property and Casualty

Insurance with Personal

and/or Commercial lines

Cyber

Insider Threat programs for

cross industry verticals

Asset

Performance

Management and

Basel 3

Fraud Detection services

for Claims and

organized activities

around providers

Comprehensive Insider

Threat monitoring

solution with real time

monitoring

Subrogation

Compliance and

Fraud Management

solutions for all

fraud types

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

SAS SECURITY

INTELLIGENCE

“TRUSTED ACCESS”

A

malicious insider threat

to an organization is a current or former

employee, contractor, or other business partner who has or had

authorized access to an organization's network, system, or data

and intentionally exceeded or misused that access in a manner

that negatively affected the confidentiality, integrity, or availability

of the organization's information or information systems.

Carnegie Mellon University

CERT Division of the

Software Engineering Institute (SEI)

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SAS SECURITY

INTELLIGENCE

DEMONSTRATION

Threat

Score

Automated

Business Rules

Anomaly

Detection

Text

Analytics

Link and

Predictive

Insider Threat Score

Combining analytical

methods and domain

expertise

White box

Ability to evolve

weblog/syslog

security/DLP

organization

Levels of

Detection

e-comms

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C op yr i g h t © 2 0 1 5 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . T h i s i n f or m a t i o n i s c on f i d e n t i a l an d c ov er e d u n d er t h e t er m s of an y S A S ag r e e m e n t s as exec u t e d b y c u s t om e r an d S A S I n s t i t u t e I n c .

PIONEERING DATA SCIENTISTS

“…

In God we trust; all

others must bring

data

.”

“…essentially, all

models are wrong,

but some are

useful.”

Box, George E. P.; Norman R. Draper

(1987).

Empirical Model-Building and Response

Surfaces

, p. 424.

Edwards

Deming Trevor Hastie

,

Robert

Tibshirani

, and Jerome Friedman, co-authors of

The Elements of Statistical Learning in their

Preface

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SAS SOLUTIONS ONDEMAND

For additional information, contact:

[email protected],

[email protected]

References

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