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					  ENTERPRISE
DATA STRATEGY
CAS Ratemaking Seminar
     March 2004

                         1
                   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

                                             2
                  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




                                               3
Data as a Corporate Asset



                            4
      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

                                                5
    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



                                                  6
               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



                                                 7
                   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.”




                                                      8
         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



                                                           9
Defining an Enterprise Data
          Strategy



                              10
  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




                                          11
Enterprise Data Strategy: A
        Definition
A plan that establishes a long-term
direction for effectively using data
resources in support of and indivisible
from of an organization's goals and
objectives



                                      12
  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



                                              13
   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


                                                         14
Enterprise Data Strategy and IT
Architecture Supports Business
           Strategy
                                               A set of guiding
                                               principles that
                   Business Strategy           define why and
                                               what we do
  Infrastructure




                          Application

                                               A set of guiding




                                        Data
                                               principles that
                                               define how we
                                               do what we do



                    IT Architecture


                                                             15
Enterprise Data Strategy: A
 Standards Organization
       Perspective


                              16
   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


                                                        17
        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.
                                            18
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.)



                                                                            19
Standards in the Insurance Process
              Insurance cycle                   Reinsurance cycle

     Client                                                  Reinsurer
                             Insurer   Cedent




                Intermediary                       Reins. Broker
  Insurance cycle                      Reinsurance Cycle
  •e-business initiatives between •Reinsurance standards - international
  Intermediaries & carriers support
                                  •No gateway between insurance and
  ACORD standards                   ceded systems
                                  •With ACORD STP becomes possible
        Quotes, contracts, premiums, claims, payment information
                                                                    20
          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)
                                                             21
    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…


                                             22
Enterprise Data Planning: An
    Insurer Perspective


                               23
Enterprise Data Management Practice

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


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.


                                                          24
         Enterprise Data Goals

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

                                           25
                     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


                                                  26
                 Components

        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
      EDMP              build a supporting technical
                        infrastructure



1   TECHNOLOGY         3


                                                    27
                Target Reference Model
                        Enterprise Data Warehouse


Business Portal                          Business Intelligence


                                                                                   Information
                                                                                   Distribution

     Information
     Products



    Warehouse
    Products

                                                                                  Data
                                                                                  Manufacturing

 ETL                                 Extract – Transformation – Load
                                  Information and Data Manufacturing




 Source Data       Internal                    Systems of              External   Data Sourcing
                    Data                         Record                 Data




                              Platform Infrastructure


                                                                                                  28
        Initiatives: Source
 Common   Data Standards (ACORD
  XML)
 Quality Standards
 Quality controls
 “Source of Record”
 Stewardship
 Meta Data Repository


                                   29
   Initiatives: Manufacturing
 Information Dictionary
 Data Warehouses
 Data Models
 Business Models
 Platform Migration
 Consolidation of Operating Systems




                                       30
     Initiatives: Distribution


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




                                         31
  Business Intelligence Ladder
                                               Predictive Modeling
                                             <GK to add>                  Advanced
                                                                          Analytics
                                                  Forecast Analysis


                                                   Trend Analysis
             Tool Sophistication & Expense




                                                                          Analytics
                                              Dimensional Data Analysis


                                                  Adhoc Reporting

                                                Parameterized Query       Reporting

                                                   Static Reporting
User Count

                                                                                  32
Enterprise Data Planning: An
    Actuarial Perspective


                               33
“There is no royal road to geometry”
                     -Euclid 300 B.C.




                                        34
         What Do We Want?
 High Quality Data
 Metrics and Coding Structure Which
  Directly Support Business Strategy
 Standardized Definitions
 Broad Access to Information




                                       35
           Information Flow
 Data      Data Warehouse        Reports/   Decision
Sources                           Info      Makers


Policy

 Claim      Data in    Metrics
             Data       from
Billing     Model       Data


External


                                                 36
          Why Actuaries?
 Value of Good Data/Cost of Bad Data
 Insurance Expertise
 Technical Expertise
 Leadership and Communication Skills
 ASOP 23




                                        37
  Obstacles in Standardization
 Inertia
 Active Resistance to Change
 Highly Complex Coding Systems
 Interdependent IT and Business Apps
 Varying Levels of Awareness of Multiple
  Definitions



                                        38
    Keys to Standardization
 High Level Management Support
 Clearly Defined Benefits
 Right People with Right Skills
 Experience with Current Coding
  Structure
 Strong Communication Skills
 Enforcement


                                   39
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.




                                           40
Enterprise Data Planning: An
   Industry Organization
         Perspective


                               41
                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


                                                 42
           Solution Sets
 Data Dictionary and Data Lab
 Data Leverage
 Data Acquisition
 Data Quality
 Data Administration




                                 43
    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


                                               44
                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

                                                 45
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


                                           46
              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


                                               47
          Data Administration

 The   “IO”s – EIO and SIO

 Managing    the processes related to data

 Theadministration of the process put in
 place for the other solution sets

 Standards

 Administering   & coordinating data
 changes
                                              48
  CONCLUSIONS &
    QUESTIONS
Addenda: References and IDMA
 Value Statements – Actuaries
                                49
    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
                                                   50
    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)
                                                                       51
    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.



                                                                       52
    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.




                                                                    53
    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



                                                                     54
    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.
                                                                55

				
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