The Actuary and Enterprise Data Strategies CAS MAY 2006 Part I: Why EDS and What Roles Should the Actuary Play?

1. The Actuary and Enterprise Data Strategies CAS MAY 2006 Part I: Why EDS and What Roles Should the Actuary Play?

2. Agenda • Strategic Data Planning • The Shifting Focus of Insurance Information • Impact of this Shift on the Actuary • Questions and Commentary

3. Panelists • Pete Marotta, ISO • Gary Knoble, USABFS • Bruce Tollefson, MN WC Rating Bureau • Christine Siekierski, WI Comp. Rating Bureau • Art Cadorine, ISO

4. Strategic Data Planning

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 data can result in faulty business decisions.

6. PWC 2004 Study “Data is pivotal to how most companies make money in today’s marketplace – and for many companies data is actually the product they are providing to the marketplace – yet it is not yet being treated as the crucial asset it clearly represents.” Global Data Management Survey 2004, PriceWaterhouseCoopers

7. Data and Strategic Planning 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.

8. PWC 2001 Study “Data is the currency of the new economy.” “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

9. PWC 2001 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. Enterprise Data Strategy: A Definition • A plan that establishes a long-term direction for effectively using data resources in support of, and indivisible from, an organization's goals and objectives. • An Enterprise data strategy requires both business and technology input to: • Facilitate IT planning. • Support the overall business plan. • Promote and maintain clearly and consistently defined data across the corporation.

11. Components of an Enterprise Data Strategy Organizational level: • Data Stewardship • Senior level oversight of corporate data. • From an enterprise-wide perspective. • Data Architecture – What to Run, Where to Run, How to Run – Software and Hardware: • Ownership: Customer and Data • Data Location • Software v. Service • Product Definition • Data and Process Models

12. Components of an Enterprise Data Strategy Data level : • Data Element Management • Data Definition and Attributes • Code Value and Data Set Management • Data Mapping Management • Data Quality and Transparency • Data Standards • Business and Efficiency Driven • Internal and External • Data Privacy and Security • Compliance with Privacy Polices and Regulations • Data from Reputable Sources • Data Security

13. Who Should be Involved with Strategic Data Planning? The data users, data definers and data enablers, including • Business units • Actuaries • Information Technology • Finance and Accounting • Claims • Government Affairs • Sales and Marketing • Research • Data Management

14. Strategic Data Planning • Strategic Data Planning is primarily a business, not an IT function. • IT critical to any enterprise data strategy. • Actuaries are uniquely positioned in an organization - data savvy as data definers and users, senior business level visibility, etc. – to be prime movers in Strategic Data Planning. Per the 2004 PWC Survey: 2/3 of respondents with a data strategy place the responsibility on IT

15. PWC 2004 Study Findings • 1/3 of have no company-wide data strategy • 42% of businesses have a formally documented, board approved data strategy • 23% of businesses have a data strategy not formally or board approved

16. 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

17. Actuaries and Data Managers: Roles in Strategic Planning Management of • data acquisition and quality assurance, • data storage, and • data disbursement processes to ensure that enterprise data will satisfy the needs of internal and external data users, that is, the data to meet corporate strategic objectives.

18. Actuaries and Data Managers: Roles in Strategic Planning How data management adds value - • Many of the Enterprise Data Strategy components are managed or supported by actuaries and data managers • Data management promotes systems alignment and interoperability - critical success factors to IT, and consequentially corporate, strategies • Provides consistent and documented perspectives about data

19. Enterprise Data Strategies: 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

20. What Is Needed To Accomplish These Goals • A Framework that articulates the scope, structure, and level of detail of Enterprise Data • A Governance Process that produces and manages a Set of Tools and Artifacts that constitute the deliverables of the Enterprise Data Process. Such as: • A Target State • A Roadmap • A Set of Data Qualities To Guide The Roadmap • A Organization to implement and conduct the process of Enterprise Data Management

21. Enterprise Data Strategy: Implementation • Identify current and planned core organizational functions and supporting business strategies • The objective of corporate strategy is to create clear direction with sustainable competitive advantages – our value proposition is better than our competitor’s • Technology can be an advantage, but technology is also reducing differentiation among competitors • Is data part of these sustainable competitive advantages? If so, data strategies must be aligned with business and IT strategies • Determine the data needs/constraints associated with the above functions

22. Enterprise Data Strategy: Implementation A typical strategic planning process includes the following steps - • Determine the strengths, weaknesses, opportunities and threats relating to the above data needs/constraints • Identify the actions needed to address the above SWOT • Determine any interrelationships of these actions • Integrate and validate proposed actions • Prioritize these actions • Develop a plan for implementing these actions

23. Results of a Successful Enterprise Data Strategy • 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 • Facilitate alignment and traceability of significant investments to their respective business drivers Actuaries are central to each of the above.

24. Results of a Successful Enterprise Data Strategy • Data coordination, interchange and acquisition • Designed to maximize utility and efficiency • Data utility and decision support • Process improvements • Measurement of results Each of the above relate to actuarial activities.

25. Results of a Successful Enterprise Data Strategy • Ease of doing business • Speed to market • Facilitate R&D • Customer Service • Compliance

26. The Shifting Focus of Insurance Information

27. Regulation • From Annual Statement to Market Conduct Annual Statements to NAIC Databases • Financial Data Repository (FDR) • National Insurance Producer Registry (NIPR) • Fingerprint Repository • On-Line Fraud Reporting System (OFRS) • Uninsured Motorist Identification Database • From financial data used to monitor solvency to financial, statistical data and analytics used to monitor solvency • From US driven regulations to EU and internationally driven regulations

28. Pricing • From traditional underwriting and pricing - using traditional data sources (risk data, industry statistics) to predictive modeling and analytics - using non-traditional data sources (demographics, GIS, 3rd party data, non-insurance data, non-verifiable data sources, etc.) • From a stable risk control and claims environment to a dynamic environment of new hazards - mold, terrorism, computer viruses, cyber terrorism, etc. • From risk-specific risk management to enterprise risk management

29. Data • From a data quality focus on validity, timeliness and accuracy to a data quality focus on transparency, completeness and accuracy  • From data available on a periodic basis to data available real-time • From statistical plans and edit packages to data dictionaries, schema and implementation guides • From sharing data for the common good to protecting data for the common good

30. Technology • From centralized highly controlled technologies to ASPs, the, Internet, XML, LANs, PCs, etc. • From IT as an business enabler to IT as a business driver • From mainframes to LANS and high powered PCs

31. Impact of this Shift on the Actuary

32. The Actuary and Data • Historically the actuary has been at the center of enterprise data activities – • Policy form development • Ratemaking • Pricing • Reserving • Etc. • More recent activities – • Predictive modeling • ASOP No. 23 • Reserve opinions • Third party data • Sarbanes Oxley, Basel II, Solvency II

33. Regulation Supports Compliance • Increased emphasis on: • Protecting the privacy and confidentiality of the enterprise data • Compliance with rating and reporting laws and regulations • Communication with regulators • Solvency and the measurement of solvency • International regulations • The need for transparency

34. Decision Making Supports Making 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.

35. Decision Making Supports Making Better Decisions • 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. • Predictive modeling is improved when better data are available, allowing for better existing products and better new product development.

36. Data Supports 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.

37. Data Supports 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 which promotes consistency across business units and across time – internally and externally. • Ensuring the quality of the enterprise data, enterprise communication among the various data sources

38. PWC 2004 Study “A formalised data quality management strategy that has been approved by the board provides a clear statement of business objectives for managing data … It also sets a framework to match those objectives to policies, processes, and an organisation structure that will ensure that the quality of critical business information is being managed.” Global Data Management Survey 2004, PriceWaterhouseCoopers

39. References, Resources & Studies • Celent “ACORD XML Standards in US Insurance”: www.celent.com or www.acord.org • IDMA: www.idma.org • PWC “Global Data Management Survey 2004” and “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

40. Questions and Commentary