FEA Data Quality Profile – Progress Report July 12, 2007 Presented to Federal Data Architecture Subcommittee (DAS)

Page 2 7/12/2007 FEA Data Quality Profile – Development Timeline EventDateStatus FEA Data Quality Profile Development Announced at DAS 3/8/07 √ DQ Profile Working Group Kick-off Meeting4/19/07 √ Development Plan/Timeline Presented to DAS5/10/07 √ 2 nd Meeting of Working Group6/21/07 √ DQ Profile Outline Presented to DAS7/12/07 √ Vet draft profile among DAS members8/31/07 Deliver draft profile to AIC9/30/07 Vet draft profile across agencies via FEA and Federal CIO Council leads FY 2008 Publication of Final DQ ProfileEnd FY 2008

Page 3 7/12/2007 Data Quality Profile Outline Introduction Business Case for Federal Data Quality What is Data Quality? FEA-Data Quality Value Proposition Implementing the Data Quality Improvement Environment Data Quality Tools Bibliography

Page 4 7/12/2007 Section I: Introduction Explains how a Data Quality Improvement Program can be implemented within Federal Agencies and/or Communities of Interest (COIs) within the context of the Federal Enterprise Architecture (FEA)

Page 5 7/12/2007 Section II: Business Case for Federal DQ Legislative Mandates - “Call for Action”: –Government Performance and Results Act of 1993 (GPRA) –Section 515 of the Treasury and General Government Appropriations Act (2002) Federal agencies struggle with developing a comprehensive approach to the quality of disseminated information Enterprise-wide data management must be developed, implemented, and enforced in the Federal Government to improve data quality in a holistic, cross-program way

Page 6 7/12/2007 Section III: What is Data Quality? New definitions for data quality emerged in the latter part of the 20th century with origins in Japanese manufacturing industry’s application of Deming’s principles of quality “Data are of high quality if they are fit for their intended uses in operations, decision making and planning” (J.M. Juran)

Page 7 7/12/2007 Section IV: FEA-DQ Value Proposition

Page 8 7/12/2007 Section IV-A: DQ and the PRM Performance Reference Model (PRM) Government-wide Performance Measures & Outcomes “Line of Sight” – Alignment of Inputs to Outputs (I/O) Data Quality Enhancements –Performance measures data-source validation –Better solicit customer satisfaction with product and results –“Balanced Scorecard” – DQ certifications and benchmarks to show progress –I/O value-cost chain Activate Agency-wide Data Quality Improvement

Page 9 7/12/2007 Section IV-B: DQ and the BRM Data Quality Enhancements –Executive management accountability –Data governance, data stewardship –Process improvement: 6 sigma, business process reengineering –Connects data creators with customers Activate Agency-wide Data Quality Improvement Business Reference Model (BRM) Lines of Business Government Resources – Mode of Delivery

Page 10 7/12/2007 Section IV-C: DQ and the SRM Service Component Reference Model (SRM) Service Layers, Service Types Components, Access and Delivery Channels Data Quality Enhancements –Focus data reconciliation efforts at the source –Implement data quality as a service within transactional processes –Scientific methods: PDCA, statistical process control Activate Agency-wide Data Quality Improvement

Page 11 7/12/2007 Section IV-D: DQ and the DRM Data Reference Model (DRM) Business Focused Data Standardization Cross Agency Information Exchanges Data Quality Enhancements –Minimize the data collection burden –Designate Authoritative Data Sources (ADS) –Establish enterprise data standards –Enterprise Data Repository – agency-wide metadata, application inventory Activate Agency-wide Data Quality Improvement

Page 12 7/12/2007 Section IV-E: DQ and the TRM Technical Reference Model (TRM) Service Component Interfaces, Interoperability Technologies, Recommendations Data Quality Enhancements –Improve the SDM (Software Development Methodology) –Optimize database performance –Align information architecture with data collection strategies Activate Agency-wide Data Quality Improvement

Page 13 7/12/2007 Section V: Implementing the DQ Improvement Environment Determine Data to Assess (Data in Scope) Perform Value Cost Chain Analysis Plan the Assessment Set DQ Standards and Metrics Assess Information Quality Assess Data Management Maturity Evaluate Costs of Non-Quality Information Assess Presence of Statistical Process Control Implement Improvements and/or Data Corrections Evaluate Impact (Shewart’s PDCA “Check” Step) Plan for Continued Data Quality Assurance Educate Save to Enterprise Repository (Data Sharing)

Page 14 7/12/2007 Section VI: Data Quality Tools Data Profiling (Business Rule Discovery) Tools Data Defect Prevention Tools Metadata Management & Quality Tools

Page 15 7/12/2007 Next Steps The FEA DQ Profile development – as well as Working Group membership itself - is a collaborative effort Anyone interested in participating should contact: –Suzanne Acar – –Ramesh Ramakrishnan – DQ Profile development timeline: –August 31, Vet draft profile among DAS members –September 30, Deliver draft profile to AIC by the end of Fiscal Year 2007 –FY 2008 – Vet draft profile across agencies via FEA and Federal CIO Council leads. –Publication of Final DQ Profile Pending FY 2008 Review Outcome.