Government Data Quality Guide: November 2007

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Plan. Record Purpose and Intended OutcomeFederal Data Quality Guide OverviewExamples of Federal Agency Data Quality PracticesAbout the Data Architecture Subcommittee (DAS). Reason. Couple of organizations practice information quality at the venture and expanded endeavor levelsThe Federal Data Quality Guide prompts offices on the key segments required for a compelling undertaking wide information quality improvem

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Government Data Quality Guide: November 2007 A Framework for Better Information Sharing "Form to Share" U.S. Elected Data Architecture Subcommittee

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Agenda Document Purpose and Intended Outcome Federal Data Quality Guide Overview Examples of Federal Agency Data Quality Practices About the Data Architecture Subcommittee (DAS)

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Purpose Few organizations rehearse information quality at the undertaking and augmented venture levels The Federal Data Quality Guide prompts offices on the key segments required for a viable endeavor wide information quality change program

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Intended Outcome Data quality projects among Federal offices and Communities of Interest (COIs) adjust to a typical depiction of information quality change hones Information that is shared enhances in quality Decision bolster in offices and COIs enhance

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Federal Data Quality Guide Overview Build an information quality system utilizing Enterprise Architecture (EA) The business case for information quality Value suggestion utilizing the reference models Data Quality Improvement usage Advice on information quality instruments Suggested extra reference material

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Key Advice Use Existing EA Program Establish information quality techniques and practices into existing office and group of intrigue business forms that are a piece of their EA Provides a structure for enhanced data sharing and choice support

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Data Quality Improvement The Challenge Federal offices and COIs have battled with facilitated ways to deal with the nature of spread data because of: Complexities of size and extension Need to institutionalize and modernize innovation and data innovation (IT) forms Retention and Turnovers

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Business Case for vast Data Quality Improvement Data Quality Improvement (DQI) furnishes offices and COIs with repeatable procedures for: identifying broken information, setting up information quality benchmarks, ensuring (measurably measuring) their quality, and constantly observing their quality consistence

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Enabling FEA Objectives with Data Quality Features Business-driven Approach (Citizen-focused Focus) Federal Enterprise Architecture (FEA) Data Quality Features Performance Reference Model (PRM) all inclusive Performance Measures & Outcomes "Viewable pathway" – Alignment of Inputs to Outputs (I/O) Performance measures information source approval Better request consumer loyalty with item and results "Adjusted Scorecard" – DQ accreditations and benchmarks to show advance I/O esteem cost chain Activate far reaching Data Quality Improvement Business Reference Model (BRM) Lines of Business Government Resources – Mode of Delivery Executive administration responsibility Data administration, information stewardship Process change: 6 sigma, business handle reengineering Connects information designers with clients Service Component Reference Model (SRM) Service Layers, Service Types Components, Access and Delivery Channels Focus information compromise endeavors at the source Implement information quality as an administration inside value-based procedures Scientific strategies: PDCA, factual process control Minimize the information gathering load Designate Authoritative Data Sources (ADS) Establish venture information norms Enterprise Metadata Repository – DQ appraisals, application stock Data Reference Model (DRM) Business Focused Data Standardization Cross Agency Information Exchanges Technical Reference Model (TRM) Service Component Interfaces, Interoperability Technologies, Recommendations Improve the SDM (Software Development Methodology) Optimize database execution Align data engineering with information accumulation methodologies

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Data Quality Improvement (DQI) Implementation Best Practices Determine Data to Monitor for Quality 13 intense DQI forms in complete Blue: endeavor level exercises - most extreme ROI. Dark: program (business office) level exercises – medium ROI Red: singular data frameworks – vital upgrades - slightest ROI if led exclusively without anyone else's input Set Data Quality Metrics and Standards Assess Data Quality Develop DQ Governance, Data Stewardship Roles & Responsibilities Assess Information Architecture and Data Definition Quality Evaluate Costs of Non-Quality Information Perform Information Value Cost Chain (VCC) Analysis Assess Presence of Statistical Process Control (SPC) Conduct Root Cause Analysis Develop Plan for Continued Data Quality Assurance Implement Improvements and Data Corrections Educate the Enterprise Save Assessment Results to Enterprise Metadata (EMD) Repository

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Some Agency Examples Agencies that have solid information quality projects at the venture level Defense Logistics Agency Housing and Urban Development

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Defense Logistics Agency (DLA) Data Quality Challenges Building comprehension of information and practical process streams of four feeder information frameworks into a DLA gateway Analyzing different information passage purposes of similar classes of mission-basic information Determining legitimate hotspot for numerous information "occasions" Determining information stewardship duties

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Defense Logistics Agency (DLA) DQI Implementation Identified 4-5 scratch business forms affecting office execution DQ Manual set edges for consistence with the measurements of Completeness, Uniqueness, Timeliness and Currency Enforced data stewardship by considering feeder frameworks' business procedure proprietors responsible for their quality Sampled information at key feeder framework focuses and contrasted and legacy cases, archiving the outcomes as indicated by required DQ measurements Identified and assigned authority record-of-birthplace, record-of-reference, and Authoritative Data Source Reengineered some business forms at the source to adjust feeder information to legacy prerequisites Developed progressing Data Quality Monitoring & Trend Analysis Educate the Enterprise

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Defense Logistics Agency (DLA) Internal DQI Scorecard

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Housing & Urban Development (HUD) Data Quality Challenges Information Architecture update required to better bolster precision and nature of data trade Legacy Grants Monitoring System Business Goal: Support work creation in underprivileged ranges Reporting Method: Data from various gathering directs accumulated toward write about employment creation insights in HUD's Annual Performance Plan Challenge: Allowable information section focuses did not utilize basic strategy to change over occupations information

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Housing & Urban Development (HUD) DQI Implementation "Number of occupations made" execution estimation from Annual Performance Plan distinguished as key business prepare DQ Handbook set limits for consistence with the measurements of Validity, Uniqueness and Completeness Assessment gave brilliant outcomes, however issue was in authorizing uniform business rules at the passage focuses Identified database of starting point, mapped information passage fields to database areas, & recognized business rules (reasonable qualities) for each Recommended Database Design and Data Definition enhancements Estimated expenses of non-quality data just "Employments made" can now be accounted for to administration with 6 sigma exactness, and steps are being made for upgrades in other key business forms Program zone finished essential reengineering of framework to uphold FTE work information passage on a solitary screen, and business runs over the database were made uniform Assessment comes about spared to EDM arranging zone

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Housing & Urban Development Internal DQI Scorecard

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Data Quality Tools Advice Enabling apparatuses for information quality at least: Data Profiling Data Defect Prevention Metadata Management Data Re-building and Correction Defect Prevention

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Current Status The Federal Data Quality Guide is in draft experiencing audit. A duplicate of the draft is accessible on the Data Architecture Subcommittee collab region on Core.gov. A duplicate of the draft can likewise be gotten by means of email demand: suzanne_acar@ios.doi.gov

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About the Authors: Federal Data Architecture Subcommittee (DAS)

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Federal CIO Council Organization Executive Committee Director, Administrator of the Office of e-Government (OMB) Chair, Deputy Director for Management (OMB) Vice Chair (Agency CIO) Architecture & Infrastructure Committee Workforce & Human Capital for IT Committee Best Practices Committee Emerging Technologies Subcommittee Governance Subcommittee Services Subcommittee The Federal Data Architecture Subcommittee is one of four subcommittees under the Architecture and Infrastructure Committee. Information Architecture Subcommittee

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Data Architecture Subcommittee Federal Data Architecture Subcommittee (DAS) Facts Chartered by Federal CIO Council 2 Co-seats selected by AIC Suzanne Acar, DOI Mary McCaffery, EPA Membership Federal CIO portrayal + supporters (120) Eight work bunches Key FY07/FY08 Activities/Deliverables Federal Data Quality Guide Final Draft Person Framework Standard

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Summary

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Summary The Federal Data Quality Guide educates offices on components of an undertaking wide information quality program. The key counsel is to use existing EA programs. The result is enhanced data sharing, interoperability, and choice support. Bolsters key rule to oversee data as a national resource.

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Questions Contact information : Suzanne Acar : U.S. Bureau of the Interior Senior Information Architect, and Co-Chair Federal Data Architecture Subcommittee suzanne_acar@ios.doi.gov

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