vv

Building the Foundation for Analytics

A Data Management Perspective Tracy A. Spadola CPCU, CIDM, FIDM V.P. Business Development – Insurance Data Management Association Practice Lead – Insurance – Teradata Corporation

Persistent Economic Turmoil, Market Uncertainty

Increased Frequency & Severity of Cat Events

Regulatory Environment Growing more Onerous

Tech Savvy, Demanding Customers Expensive & Hard to Manage

Distribution Channels

Increased Customer & Market Competition

Major Challenges in the Business Environment

Major Challenges in the IT Environment

Legacy Systems Upgrades Cyber Security

Big Data New Technologies

* Data Management Challenges

Insurers are Often Flying Blind

Each Discipline has its Own Data

No Common Understanding

No Complete View of Customer, Agent Decisions Based on Incomplete Information

BABEL

Claims Data Sources – Traditional & Non

6

Why Data Management in Claims is Important

• Data Silos – Claims data can include many information silos including subrogation,

litigation management, adjusting, financial, case management, vendor management and more.

• Data Management – assures better claims data integration for more accurate analytics and

information as well as faster claims investigations. – allows for more automation of processes including fraud detection. – enables better regulatory claims compliance and reporting – enables product development, reduction of costs and more.

7

Types of Multi-structured Data Outside the Enterprise Data Warehouse

†Source: Analytics Platforms – Beyond the Traditional Data Warehouse, Survey of 223 companies. BeyeNetwork 2012

Data Types Outside of the Enterprise Data Warehouse

53% of Companies Struggle Analyzing Data Types Not in the Traditional Data Warehouse

Big Data, Analytics & Its Challenges

• “Non-Traditional” data sources – Web activity, telematics, weather, social media, etc. – 3rd Party data – Limited data standards

• Beyond structured – Unstructured and Multi-Structured data requiring new

technologies (hardware and software)

• Highly iterative analysis

Big Data Requires multiple Information Management strategies and new technologies.

Integration across the Analytic Ecosystem is critical

Beyond the Traditional Data Warehouse

TERADATA UNIFIED DATA ARCHITECTURE

Security, Workload Management ERP

SCM

CRM

Images

Audio and Video

Machine Logs

Text

Web and Social

SOURCES

Marketing Executives

Operational Systems

Frontline Workers

Customers Partners

Engineers

Data Scientists

Business Analysts Math

and Stats

Data Mining

Business Intelligence

Applications

Languages

Marketing

USERS

ANALYTIC TOOLS & APPS

Search

Marketing Executives

Operational Systems

Knowledge Workers

Customers Partners

Engineers

Data Scientists

Business Analysts

USERS

INTEGRATED DATA WAREHOUSE

DATA PLATFORM

INTEGRATED DISCOVERY PLATFORM

Security, Workload Management REAL TIME PROCESSING

Final Thoughts

• More and More Big Data is not traditional Insurance Data – Understand the Data Quality implications

• Identify the right platform for the particular data source

– Reporting, Analytics, File Storage, Discovery

• Implement solid Data Management practices for increased Business Value