ENTERPRISE DATA STRATEGY CAS MAY 2003

1. ENTERPRISE DATA STRATEGY CAS MAY 2003

2. Agenda • Introductions • Data as a Corporate Asset • Defining an Enterprise Data Strategy • Developing an Enterprise Data Strategy • Conclusions and Questions

3. Panelists • Moderator: Pete Marotta, Principal Data Management Consulting, ISO • Gary Knoble, Vice President, The Hartford • Beth Grossman, AVP Industry Relations, ACORD • Randy Molnar, Senior Analyst, NCCI

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 data aids good corporate governance by providing management with a cohesive and objective view of an organization’s activity • Poorly-managed can result in faulty business decisions

6. 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 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

7. 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

8. PWC Study “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 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.”

10. 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

11. Defining an Enterprise Data Strategy

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. 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 Look for condensation points • Data and process models • Integration: system and data

15. 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 • Data fit for its intended use • Characteristics • Data standards • Business and efficiency driven • Internal and External • Example: ACORD and SOA • Data privacy and security • Compliance with privacy polices and regulations • Data from reputable sources • Data Security

16. Components of an Enterprise Data Strategy Implementation : • Data coordination, interchange and acquisition • Designed to maximize utility and efficiency • Data utility, accessibility and decision support • Process improvements • Measurement of results

17. Who Should be Involved with Strategic Data Planning – Functionally & Organizationally? 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. Data Management’s Role 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.

19. Data Management’s Role in Strategic Planning How data management adds value - • Many of the Enterprise Data Strategy components are managed or supported by data managers • Data management promotes systems alignment and interoperability - critical success factors to IT, and consequentially corporate, strategies • Provides data perspective

20. Developing an Enterprise Data Strategy

21. Enterprise Data Strategy • 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 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. Enterprise Data Planning at The Hartford

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. What Is Needed To Accomplish These Goals • A Context Setting 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 Conceptual Design to communicate guiding concepts and themes, including Service Oriented Architecture and Layered Designs • A Roadmap That communicates the incremental accomplishment of the Target State • A Set of Data QualitiesTo Guide The Roadmap • A Organization to implement and conduct the process of Enterprise Data Management

26. Governance Process Inventory Of Current State Business Drivers create changes to Business Functions which in turn affect Business Process. Processes are comprised of Process Steps which are supported by Data Components. These Data Components reside within Applications and Services.

27. 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.

28. 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 Building the EDMP EDMP 1 TECHNOLOGY 3

29. CONCLUSIONS & QUESTIONS Handouts: IDMA Value Statements – General, Senior Management and Actuary

30. 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)

31. 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.

32. 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.

33. 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

34. 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.