ENTERPRISE DATA STRATEGY CAS Ratemaking Seminar March 2004

1. ENTERPRISE DATA STRATEGY CAS Ratemaking Seminar March 2004

2. 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

3. 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

4. Data as a Corporate Asset

5. 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

6. 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

7. 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

8. 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.”

9. 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

10. Defining an Enterprise Data Strategy

11. 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

12. 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

13. 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

14. 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

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

16. Enterprise Data Strategy: A Standards Organization Perspective

17. 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

18. 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.

19. 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.)

20. Reinsurance cycle Insurance cycle Standards in the Insurance Process Client Reinsurer Insurer Cedent Intermediary Reins. Broker • Insurance cycle • e-business initiatives between • Intermediaries & carriers support • ACORD standards • Reinsurance Cycle • Reinsurance standards - international • No gateway between insurance and • ceded systems • With ACORD STP becomes possible Quotes, contracts, premiums, claims, payment information

21. 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)

22. 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…

23. Enterprise Data Planning: An Insurer Perspective

24. 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.

25. 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

26. 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

27. 2 Organization: develop a body suitable for supporting the mission Process: using identified assets in a meaningful and reusable way Technology: analyzing the needs of the Organization and Process to build a supporting technical infrastructure PROCESS ORGANIZATION Components EDMP 1 TECHNOLOGY 3

28. Enterprise Data Warehouse Information Products Warehouse Products ETL Extract – Transformation – Load Information and Data Manufacturing Source Data Systems of External Internal Data Record Data Platform Infrastructure Target Reference Model Business Intelligence Business Portal Information Distribution Data Manufacturing Data Sourcing

29. Initiatives: Source • Common Data Standards (ACORD XML) • Quality Standards • Quality controls • “Source of Record” • Stewardship • Meta Data Repository

30. Initiatives: Manufacturing • Information Dictionary • Data Warehouses • Data Models • Business Models • Platform Migration • Consolidation of Operating Systems

31. Initiatives: Distribution • Data Marts • Vendor Contacts • Shared Licenses for data access software • Knowledge Management

32. Business Intelligence Ladder Predictive Modeling <GK to add> Advanced Analytics Forecast Analysis Prime Actuarial Space Trend Analysis Analytics Dimensional Data Analysis Tool Sophistication & Expense Adhoc Reporting Parameterized Query Reporting Static Reporting User Count

33. Enterprise Data Planning: An Actuarial Perspective

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

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

36. Data in Data Model Metrics from Data Information Flow Data Sources Data Warehouse Reports/Info Decision Makers Policy Claim Billing External

37. Why Actuaries? • Value of Good Data/Cost of Bad Data • Insurance Expertise • Technical Expertise • Leadership and Communication Skills • ASOP 23

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

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

40. 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.

41. Enterprise Data Planning: An Industry Organization Perspective

42. 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

43. Solution Sets • Data Dictionary and Data Lab • Data Leverage • Data Acquisition • Data Quality • Data Administration

44. 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

45. 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

46. 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

47. 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

48. 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

49. CONCLUSIONS & QUESTIONS Addenda: References and IDMA Value Statements – Actuaries

50. 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