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ENTERPRISE

DATA STRATEGYCAS Ratemaking Seminar

March 2004

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Agenda

Introductions Data as a Corporate Asset Defining an Enterprise Data Strategy

– A Standards Organization Perspective– An Insurer Perspective– An Actuarial Perspective– An Industry Organization Perspective

Conclusions and Questions

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Panelists

Pete Marotta, Principal Data Management Consulting, ISO

Kim McMillon, Program Manager, ACORD Gary Knoble, Vice President, The Hartford Nathan Root, Assistant Vice President, CNA

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Data as a Corporate Asset

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Data - A Corporate Asset

Data, like all corporate assets, requires managing to ensure the maximum benefit is achieved by the organization

Well-managed, high-quality data aids good corporate governance by providing management with a cohesive and objective view of an organization’s activity and promotes data transparency

Poorly-managed can result in faulty business decisions

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Data and the Strategic Planning Process

Data supports corporate decision-making – In providing a cohesive and objective view of

corporate activities In viewing the external landscape In predicting the future In developing the corporate strategic plan In identifying process improvements and other

efficiencies In measuring results

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PWC Study

“Data is the currency of the new economy.” PWC

“Companies that manage their data as a strategic resource and invest in its quality are already pulling ahead in terms of reputation and profitability from those that fail to do so.” Global Data Management Survey 2001, PriceWaterhouseCoopers

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PWC Study

“…over the past two years, nearly seven out of ten companies have become increasingly reliant on electronic data to make company decisions and implement processes. Yet the survey points to dangerous levels of complacency regarding data management issues within these organizations.”

“Three quarters of companies surveyed had expressed significant problems as a result of faulty data.”

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PWC Study Findings

1/3 of business fail to bill or collect receivables as a result of poor data management

4 out of 10 businesses have a documented, board approved data strategy

Where data strategies exist, they tend to consist of a series of polices on areas such as privacy and security, rather than addressing true strategic issues, such as the value of data

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Defining an Enterprise Data Strategy

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Enterprise Data Strategy

“Not having a data strategy is analogous to a company allowing each department and each person within each department to develop their own charts of accounts.”

Data Strategy Initiatives by Sid Adelman, Data Management Review 11/2001

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Enterprise Data Strategy: A Definition

A plan that establishes a long-term direction for effectively using data resources in support of and indivisiblefrom of an organization's goals andobjectives

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Enterprise Data Strategy: A Definition

In addition to supporting corporate

business goals, an Enterprise data strategy

facilitates IT planning by promoting and

maintaining clearly and consistently

defined data across the corporation

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Enterprise Data Strategy

“An enterprise data strategy is a plan for improving the way an enterprise leverages its data, allowing the company to turn data into information and knowledge which, in turn, produces measurable improvements in business performance.”Information for Innovation: Developing an Enterprise

Data Strategy, by Nancy Muller, Data Management

Review 10/2001

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Enterprise Data Strategy and IT Architecture Supports Business

Strategy

Business Strategy

IT Architecture

Infr

astr

uct

ure

Ap

pli

cati

on

Dat

a

A set of guiding principles that define why and what we do

A set of guiding principles that define how we do what we do

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Enterprise Data Strategy: A Standards Organization

Perspective

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Who Should be Involved with Strategic Data Planning?

The data users, data definers and data enablers, including Business units Information Technology Finance and Accounting Actuaries Claims Government Affairs Sales and Marketing Research Data Management

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Industry Resources

Professional Associations: IDMA, CAS, etc. Trade Associations: RIMS, AIA, PAAS Technology Leaders: The Data Warehouse

Institute, Gartner, Celent, etc. Vendors & Consultants Industry Organizations: ACORD, ISO,

NCCI, etc.

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The following may be standardized by the industry through the ACORD Process

Paper or electronic forms (presentation) Spreadsheet Data element naming conventions Data definitions Codelists Processes Data relationships (is a coverage related to policy, location (state,

etc), unit at risk Format for representation

– xml– AL3

Implementation Guides Not through the ACORD process

– Enveloping structure, wrappers (security, authentication, etc.)

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Standards in the Insurance Process

Client Reinsurer

Intermediary Reins. Broker

Insurance cycle Reinsurance cycle

Quotes, contracts, premiums, claims, payment information

Reinsurance Cycle•Reinsurance standards - international•No gateway between insurance and ceded systems•With ACORD STP becomes possible

Insurance cycle•e-business initiatives between Intermediaries & carriers supportACORD standards

Insurer Cedent

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How ACORD Can Help

Central repository for industry:– Data dictionary– Data Models

Antitrust Protection– Sponsoring standards development across industry

competitors Networking

– Tackling industry implementation issues– Identifying and meeting with key trading partners– Evangelizing best practices

Managing relationships with other standards organizations to achieve interoperability (accounting, finance, human resources, collision repair)

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Implementation Success

Standards facilitate:– Internal system integration

– Conversions

– Extending the life of legacy systems

– Streamlines business process flows Policy issuance to billing to claims

servicing…

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Enterprise Data Planning: An Insurer Perspective

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Enterprise Data Management Practice

Vision:A true practice that presents a cohesive set of processes for enabling project teams to construct enterprise class business applications, services the information needs of the business and seamlessly integrates into the overall P&C enterprise vision.

Mission:

Enable business generate value to its customers, partners and shareholders through a holistic, realistic and accurate view of enterprise information.

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Enterprise Data Goals

Facilitate alignment and traceability of significant IT investments to their respective business driversProvide a process and a set of tools to facilitate Business and IT planning and decision-makingMaintain a common and consistent view of data that is shared company wide

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Participants Actuarial

– Most likely sponsor– Actuarial Standards No. 23 – Data Quality– Custodians of information

Business Units– Link data strategy to business strategy

Information Management– Maintain tools– Insure delivery of data

Data Management– Data quality– Data definitions– Data coordination– Compliance

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PRO

CESS

PRO

CESS

ORG

AN

IZATI

ON

ORG

AN

IZATI

ON

TECHNOLOGYTECHNOLOGY

EDMEDMPP

1 3

2 1. Organization: develop a body suitable for supporting the mission

2. Process: using identified assets in a meaningful and reusable way

3. Technology: analyzing the needs of the Organization and Process to build a supporting technical infrastructure

Components

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Target Reference ModelEnterprise Data Warehouse

Platform Infrastructure

Source Data Systems of Record

Internal Data

External Data

Extract – Transformation – Load

Information and Data ManufacturingETL

Warehouse Products

Information Products

Business IntelligenceBusiness Portal

Data

Manufacturing

Information Distribution

Data Sourcing

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Initiatives: Source

Common Data Standards (ACORD XML)

Quality Standards Quality controls “Source of Record” Stewardship Meta Data Repository

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Initiatives: Manufacturing

Information Dictionary Data Warehouses Data Models Business Models Platform Migration Consolidation of Operating Systems

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Initiatives: Distribution

Data Marts Vendor Contacts Shared Licenses for data access software Knowledge Management

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Business Intelligence Ladder

<GK to add>

User Count

Predictive Modeling

Forecast Analysis

Trend Analysis

Dimensional Data Analysis

Adhoc Reporting

Parameterized Query

Static Reporting

Too

l Sop

hist

icat

ion

& E

xpen

se

Advanced Analytics

Analytics

Reporting

Prim

e A

ctua

rial S

pace

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Enterprise Data Planning: An Actuarial Perspective

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“There is no royal road to geometry”

-Euclid 300 B.C.

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What Do We Want?

High Quality Data Metrics and Coding Structure Which

Directly Support Business Strategy Standardized Definitions Broad Access to Information

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Information FlowData Warehouse

Data in Data

Model

Metrics from Data

Reports/Info

Data Sources

Decision Makers

Policy

Claim

Billing

External

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Why Actuaries?

Value of Good Data/Cost of Bad Data Insurance Expertise Technical Expertise Leadership and Communication Skills ASOP 23

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Obstacles in Standardization

Inertia Active Resistance to Change Highly Complex Coding Systems Interdependent IT and Business Apps Varying Levels of Awareness of Multiple

Definitions

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Keys to Standardization

High Level Management Support Clearly Defined Benefits Right People with Right Skills Experience with Current Coding

Structure Strong Communication Skills Enforcement

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Key Lessons in Driving Change

Don’t take a ‘No’ from someone who can’t give you a ‘Yes’

Enter Data Once and Only Once Standardize, Standardize, Standardize The Right People Make the Difference Frame the Problem Before You Solve It.

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Enterprise Data Planning: An Industry Organization

Perspective

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Objectives Enable the re-use of data across the enterprise

to derive maximum value by creating new data analytics, and decision support offerings

Enable the enterprise and its trading partners to easily exchange new and existing data with minimal overlap to sustain and increase enterprise value

Enable the enterprise to protect its data assets to ensure quality and our position as a trusted intermediary

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Solution Sets

Data Dictionary and Data Lab Data Leverage Data Acquisition Data Quality Data Administration

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Data Dictionary and Data Lab

A knowledge management tool to cut through data access issues

A repository for:– Standards, procedures, guidelines, business

rules, metadata– Internal and external data elements– Record layout, # records, data field

descriptions, usage limitations, data elements/codes, database abstract

– Links to source documents to data feeds and data stores

Data Lab Business Intelligence

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Data Leverage

Ability to merge different data sources to increase their current value

3rd party matching referential linking

Linkage of current databases to create new products

A holistic view of data

It is data integration

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Data Acquisition: Components

Extract, Transform and Load (ETL) Enterprise Receipt and Acceptance New Data and Feeds Connect with 3rd Party Vendors (Policy

Mgt, Claims) Better Input to Business Cases and

Acquisitions

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Data Quality

Data quality, management and guidelines Data accuracy, validity, completeness … Quality standard and actual quality by

application Document data quality parameters and criteria

at application level Documented measures of data quality Expand utility beyond current use “Enterprise" criteria for use Cross SBU quality

assurance

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Data Administration

The “IO”s – EIO and SIO

Managing the processes related to data

The administration of the process put in place for the other solution sets

Standards

Administering & coordinating data changes

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CONCLUSIONS & QUESTIONS

Addenda: References and IDMA Value Statements – Actuaries

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References, Resources & Studies Celent “ACORD XML Standards in US Insurance”:

www.celent.com or www.acord.org IDMA: www.idma.org ACORD: www.acord.org PWC “Global Data Management Survey 2001”:

www.pwcglobal.com Gartner Research: www4.gartner.com TDWI “Data Quality and the Bottom Line”: www.dw-

institute.com CIO Magazine: “Wash Me: Dirty Data …” 2-15-01 edition,

www.cio.com Data Management Review: www.dmreview.com

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Data Management Value Proposition Value to Actuaries

Value: Data Quality

Good data management improves data:

Validity—Are data represented by acceptable values?

Accuracy—Does the data describe the true underlying situation?

Reasonability—Does the data make sense? How does it compare with similar data from a prior period?

Completeness—Do you have all the data you need?

Timeliness—Are the data current?

allowing the actuary to have more confidence in, and a better

understanding of, the data being used. This assists the actuary in

his/her professional responsibilities to certify data quality (e.g.,

Actuarial Standard 23 on Data Quality)

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Data Management Value Proposition Value to Actuaries

Value: Better Decisions

Better decisions result from better data.

Better priced risks—rates, increased limits, etc.—means improved bottom line, greater customer satisfaction, improved customer retention, increase in number of customers

Improved ability to explain, defend (and testify as necessary) decisions with better data behind the decision, documented controlled data management processes in place helps to prove the value of data being used

Improved data integrity, data utility

As data is and can be sliced ever more finely, attention to quality, privacy and confidentiality is critical. Data management skills can ensure that.

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Data Management Value Proposition Value to Actuaries

Value: Better Decisions (continued)• The actuary’s time is freed up for more focus on core professional

responsibilities, decisions and analysis when data quality is assured under the guidance of the data manager. Putting data management under the responsibility of a data management professional allows both disciplines to do what they do best and are best trained to do.

• In many cases, skilled data managers can assume handle functions such as responding to special calls.

• Predictive modeling is improved when better data are available, allowing for better existing products and better new product development.

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Data Management Value Proposition Value to Actuaries

Value: Internal Data Coordination

Reducing the cost and time associated with of data collection, storage, and dispersal, making data available more quickly

Promoting the interoperability of data and databases, allowing for better data integration thereby giving the actuary more options for how data can be used

Managing data content and definition across the organization

Advocating industry and enterprise data standards which ensure consistent definitions and values for enterprise data elements

Ensuring the quality of the enterprise data, enterprise communication among the various data sources

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Data Management Value Proposition Value to Actuaries

Value: Compliance

Protects the privacy and confidentiality of the enterprise data

Ensures compliance with data reporting laws and regulations

Assists in identifying solutions to data reporting issues

Communication/interface with regulators

Non-confrontational mechanism for dialog

Represents the company to the regulator and brings back information on regulatory perspectives, allowing for better decision making.