1. Information Assurance The Coordinated Approach To Improving Enterprise Data Quality

2. Introduction Information Assurance requires the coordinated efforts of multiple teams working on strategy, tactics, and projects Information Assurance team members share responsibility, resources, and rewards Information Assurance requires the coordinated efforts of multiple teams working on strategy, tactics, and projects Information Assurance team members share responsibility, resources, and rewards 2

3. Agenda What is Information Assurance? Nationwide Activities and Results Your Benefits What is Information Assurance? Nationwide Activities and Results Your Benefits 3

4. What is Information Assurance? A Method for Addressing Data Quality Issues and Improving Business Value Using: –A Coordinated Team Interaction Model –A Standard IA Process Flow Model –A Focused Organizational Structure –A Defined Set of Responsibilities A Method for Addressing Data Quality Issues and Improving Business Value Using: –A Coordinated Team Interaction Model –A Standard IA Process Flow Model –A Focused Organizational Structure –A Defined Set of Responsibilities 4

5. Typical Data Quality Issues Have you encountered: –Data management processes that generate data inconsistent with your business operations? –User interfaces that encourage data entry personnel to select a specific data value whether or not it is the correct value? Have you encountered: –Data management processes that generate data inconsistent with your business operations? –User interfaces that encourage data entry personnel to select a specific data value whether or not it is the correct value? A Select Value (or accept default): E No Value D Account Locked C Closed Account B Open Account A Status Unknown {default} 5

6. Team Interaction Model 6 Business Systems Finance Business Information Architect Data Quality Administration Internal Audits nformation Assurance Team Information Assurance Team

7. Information Assurance Process Flow Start Data Analysis Project Initiation Data Steward Appoints Data Quality Analysis Team DQ Team identifies key elements & acceptable DQ compliance levels DQ Team & Data Architect(s) perform Data Quality Analysis Is element within DQ compliance limits? DQ Team identifies remediation options & recommendations Data Steward & DGC select appropriate remediation action(s) Remediation actions successfully implemented End Yes No Document – participants, roles, responsibilities, time commitments Document – which elements, how good is good enough, why, what metrics to use Document – who, what, why, how, when, and results for each pass thru data Document – what can we do, how much will it cost, what benefit will we see Document – selected option, reasons for selection, how it will be implemented Document – complete new project documentation 7

8. Stepping up to Business Value Data Information Shared Knowledge Wisdom Business Advantage + + + + Communication Complexity Business Value Metadata Repository Business Context Data Governance Agreed Meaning BI,EIS, & Data Mining Accurate Application Decisions & Implementation Timely Use OLTP & Data Capture Input Processes 8

9. Organizational Structure 9

10. Information Assurance Responsibilities Data Governance Committee –Guidance, Standards, Common Definitions, Metrics, Business Rules Data Stewardship Team –Validation, Metadata Management, Business Usage, Data Quality Analysis Data Quality Committee –Prioritization, Funding Allocation, Data Quality Oversight, Senior Escalation Point for Data Quality Issues Information Assurance Team –Data Quality Analysis and Reporting, Data Quality Training Data Governance Committee –Guidance, Standards, Common Definitions, Metrics, Business Rules Data Stewardship Team –Validation, Metadata Management, Business Usage, Data Quality Analysis Data Quality Committee –Prioritization, Funding Allocation, Data Quality Oversight, Senior Escalation Point for Data Quality Issues Information Assurance Team –Data Quality Analysis and Reporting, Data Quality Training 10

11. Nationwide Activities and Results Why an Information Assurance Focus Current Information Assurance State The Problems We Addressed Our Deliverables to Date The Results of Our Efforts Why an Information Assurance Focus Current Information Assurance State The Problems We Addressed Our Deliverables to Date The Results of Our Efforts 11

12. Why an Information Assurance Focus Information Assurance encourages a "Collaborative Assault" on data quality issues Information Assurance enables a Speed to Market strategy in support of business operations Information Assurance insures that Front-Line Decision Makers have access to reliable and timely information on which to base their decisions Information Assurance encourages a "Collaborative Assault" on data quality issues Information Assurance enables a Speed to Market strategy in support of business operations Information Assurance insures that Front-Line Decision Makers have access to reliable and timely information on which to base their decisions 12

13. Current Information Assurance State Data Governance Committee fully operational Information Assurance Team being staffed Data Stewards being identified for most areas Data Quality Committee established, supported by Metadata Management team Internal Audit approval of process models Data Quality Administration providing detailed data quality analysis Data Governance Committee fully operational Information Assurance Team being staffed Data Stewards being identified for most areas Data Quality Committee established, supported by Metadata Management team Internal Audit approval of process models Data Quality Administration providing detailed data quality analysis 13

14. The Problems We Addressed No standard review, approval, and certification process for new data warehouse projects Inconsistent definition, testing, and approval for new metrics Fragmented error management processes – no enforceable service level agreements Project and team based data quality analysis processes provided unverifiable results No standard review, approval, and certification process for new data warehouse projects Inconsistent definition, testing, and approval for new metrics Fragmented error management processes – no enforceable service level agreements Project and team based data quality analysis processes provided unverifiable results 14

15. Our Deliverables to Date Data Governance Certification Process New Metrics Development Process Error Management Process (2004) Data Quality Analysis Process (2004) Data Governance Certification Process New Metrics Development Process Error Management Process (2004) Data Quality Analysis Process (2004) 15

16. Data Governance Certification 16

17. New Metrics Development Guarantees Unique Names & Definitions Improves Data Quality Insures Accuracy & Reliability Promotes Reusability Provides for a "Single Version of the Truth" Guarantees Unique Names & Definitions Improves Data Quality Insures Accuracy & Reliability Promotes Reusability Provides for a "Single Version of the Truth" 17

18. Error Management Insures Common Error Reporting and Management Improves Error Tracking & Issue Resolution Operations Provides Common Issue Escalation Practices Release in 2004 Insures Common Error Reporting and Management Improves Error Tracking & Issue Resolution Operations Provides Common Issue Escalation Practices Release in 2004 18

19. Business Must Apply ROI Discipline Data Quality Analysis Release in 2004 19

20. The Results of Our Efforts Simplified, common review, approval, and certification for data warehouse projects Consistent, enforceable process for new metrics development and approval Common error management process, supported by realistic service level agreements Centrally managed data quality analysis processes for raw data sets providing verifiable business value for effort Simplified, common review, approval, and certification for data warehouse projects Consistent, enforceable process for new metrics development and approval Common error management process, supported by realistic service level agreements Centrally managed data quality analysis processes for raw data sets providing verifiable business value for effort 20

21. The Lessons We Learned Each process checkpoint must add value Process tasks must prevent bottlenecks in the design and development lifecycle Get the right people into a room and don't leave until the issues have been identified and addressed Each defined activity must be associated with an enforceable service level agreement Each process checkpoint must add value Process tasks must prevent bottlenecks in the design and development lifecycle Get the right people into a room and don't leave until the issues have been identified and addressed Each defined activity must be associated with an enforceable service level agreement 21

22. Future Programs Expanding Data Governance Committee structure and authority to include all business data sets Initiating Data Stewardship program for all business units Establishing Data Quality Committee as senior escalation point on data quality issues Establishing Information Assurance team and program as shared business resources Expanding Data Governance Committee structure and authority to include all business data sets Initiating Data Stewardship program for all business units Establishing Data Quality Committee as senior escalation point on data quality issues Establishing Information Assurance team and program as shared business resources 22

23. Your Benefits Improving Business Processes and Decision Making Leveraging Organizational Structure, Communication, and Cooperation Coordinating Technological Operations to Reduce Redundancy Improving Business Processes and Decision Making Leveraging Organizational Structure, Communication, and Cooperation Coordinating Technological Operations to Reduce Redundancy 23

24. Improving Business Processes Improved data quality Increased information value Value based decisions driven by measurable ROI Emphasis on quality, not quantity, of work Improved metadata accuracy and increased content Improved data quality Increased information value Value based decisions driven by measurable ROI Emphasis on quality, not quantity, of work Improved metadata accuracy and increased content 24

25. Leveraging Organizational Structure Shared effort among business, finance, and technology teams Team Interaction Model encourages idea exchange and joint development efforts New/improved processes emphasize organizational strengths Shared effort among business, finance, and technology teams Team Interaction Model encourages idea exchange and joint development efforts New/improved processes emphasize organizational strengths 25

26. Coordinating Technological Operations Enables use of common and standardized process models Encourages development of and adherence to best practices Coordinates review and improvement of Data Quality concepts and processes Leverages staff resource strengths Minimizes risks due to resource rebalancing Enables use of common and standardized process models Encourages development of and adherence to best practices Coordinates review and improvement of Data Quality concepts and processes Leverages staff resource strengths Minimizes risks due to resource rebalancing 26

27. Conclusion The goal of Information Assurance is to provide business units with the highest quality data possible The establishment of a business focused Information Assurance team is of utmost importance Information Assurance activities must involve the coordinated effort of multiple teams relying on skilled specialists Each Information Assurance activity must provide a verifiable net improvement in overall data quality The goal of Information Assurance is to provide business units with the highest quality data possible The establishment of a business focused Information Assurance team is of utmost importance Information Assurance activities must involve the coordinated effort of multiple teams relying on skilled specialists Each Information Assurance activity must provide a verifiable net improvement in overall data quality 27

28. The Authors Ann Moore, Officer, Strategic Projects With a background in sales management, Claims, NI Systems management, Internal Audits, and NI Data Governance, Ann brings both business and technical expertise to Information Assurance operations and processes Ronald Borland, Data Architect With three years in NIS data architecture and a background in project management, data quality, metadata management, and application design and development, Ron is able to bring a strong cross discipline approach to Information Assurance operations and processes Ann Moore, Officer, Strategic Projects With a background in sales management, Claims, NI Systems management, Internal Audits, and NI Data Governance, Ann brings both business and technical expertise to Information Assurance operations and processes Ronald Borland, Data Architect With three years in NIS data architecture and a background in project management, data quality, metadata management, and application design and development, Ron is able to bring a strong cross discipline approach to Information Assurance operations and processes 28

29. Information Assurance is a state of mind as much as a technological process. The goal is to provide the business with the highest quality information possible Information Assurance is a state of mind as much as a technological process. The goal is to provide the business with the highest quality information possible 29