how do you insure yourself against the big data tsunami

Copyright © 2011, SAS Institute Inc. All rights reserved.

How do you insure yourself against the Big Data tsunami? Develop a Big Analytics policy! Keith Collins Senior Vice President & Chief Technology Officer SAS Institute

2


3


Who is SAS?

4


Industry Focus

5


Today’s Discussion

Hardware Landscape

Step Change in Data

Big Analytics

6


Changes in Technology

Storage Cost per Megabyte Shifts in Hardware

Source: PC Magazine, October 2, 2007

C. Moore, Data Processing in ExaScale-Class Computer Systems,

Salishan, April 2011

7


Step Change in Data

8


Step Change in Data

RDBS Specialized DB:

SAS, Netezza, Greenplum, Hadoop

9


PROBLEM:

The disconnect between the ease

of acquiring data and the ability to

make decisions on them.

10


Competitive Gap of Data Management

Source: Economist Intelligence Unit: Big data,

Harnessing a game-changing asset. September 2011

5–6% more productive

than competitors

11


“Big Data” in Insurance Industry

New Data

In-vehicle

Demographic

Social

Location

Importance of data quality

12


Big Analytics: Fraud

Problem: Fraud revenue loss across lines of business

Strategy:

Use analytics to detect fraud patterns in enterprise data

Prioritize investigator time and focus through alerts and scoring

Become proactive in detecting fraud (First Notice of Loss)

Case Study: Large Commercial Insurer

SAS Fraud Framework

» 100% ($16B) of company’s workers compensation and general liability claims processed

» Hosted solution with SAS OnDemand

» 57% uplift on existing fraud detection process

» Incremental estimated $10.3M saved annually

» SAS’s analytics drove 35% better analytics than competitors

13


Mitigating Risk in Minutes

United Overseas Bank

Portfolio of millions of loans and 44,000 security instruments

Complex economic capital and market risk models

Hosted grid appliance in SAS’s ASP environment

Re-entrant code is propagated across all notes so additional blades and processors do not degrade performance

Computation time went from 8-12 days down to just a few minutes

Imagine putting that computational power to work during a catastrophic event

14


Customer Analytics

Problems: No single view of customer, ineffective customer segmentation, inability to predict behavior, multiple distribution channels

Strategy:

Consolidate data management capabilities

Analyze data for trends to:

» calculate customer retention scores to provide early-warning indicators

» determine customer value

Case Study: Max New York Life

SAS Business Analytics (SAS Campaign Management, SAS Enterprise Miner, SAS Enterprise Guide)

» 20% increase in cross-sales

» 40% increase in premium revenue


http://www.sas.com/

how do you insure yourself against the big data tsunami

Documents