05535nam 2200673 a 450 991046301820332120211022220710.01-283-93318-70-12-397754-1(CKB)2670000000312360(EBL)1106491(OCoLC)823722256(SSID)ssj0000803128(PQKBManifestationID)11487141(PQKBTitleCode)TC0000803128(PQKBWorkID)10805949(PQKB)10331089(MiAaPQ)EBC1106491(CaSebORM)9780123970336(Au-PeEL)EBL1106491(CaPaEBR)ebr10642635(CaONFJC)MIL424568(EXLCZ)99267000000031236020130102d2013 uy 0engurunu|||||txtccrMeasuring data quality for ongoing improvement[electronic resource] a data quality assessment framework /Laura Sebastian-Coleman1st editionWaltham, Mass. Elsevier20131 online resource (404 p.)The Morgan Kaufmann Series on Business IntelligenceDescription based upon print version of record.0-12-397033-4 Includes bibliographical references and index.Front Cover; Measuring Data Quality for Ongoing Improvement; Copyright Page; Contents; Acknowledgments; Foreword; Author Biography; Data Quality Measurement: the Problem we are Trying to Solve; Introduction: Measuring Data Quality for Ongoing Improvement; Recurring Challenges in the Context of Data Quality; Definitions of Data Quality; Expectations about Data; Risks to Data; The Criticality of Metadata and Explicit Knowledge; The Business/Information Technology Divide; Data Quality Strategy; DQAF: the Data Quality Assessment FrameworkOverview of Measuring Data Quality for Ongoing Improvement Section One: Concepts and Definitions; Section Two: DQAF Overview; Section Three: Data Assessment Scenarios; Section Four: Applying the DQAF to Data Requirements; Section Five: Data Quality Strategy; Section Six: the DQAF in Depth; Intended Audience; What Measuring Data Quality for Ongoing Improvement Does Not Do; Why I Wrote Measuring Data Quality for Ongoing Improvement; 1: Concepts and Definitions; 1 Data; Purpose; Data; Data as Representation; The Implications of Data's Semiotic Function; Semiotics and Data Quality; Data as FactsData as a Product Data as Input to Analyses; Data and Expectations; Information; Concluding Thoughts; 2 Data, People, and Systems; Purpose; Enterprise or Organization; IT and the Business; Data Producers; Data Consumers; Data Brokers; Data Stewards and Data Stewardship; Data Owners; Data Ownership and Data Governance; IT, the Business, and Data Owners, Redux; Data Quality Program Team; Stakeholder; Systems and System Design; Concluding Thoughts; 3 Data Management, Models, and Metadata; Purpose; Data Management; Database, Data Warehouse, Data Asset, DatasetSource System, Target System, System of Record Data Models; Types of Data Models; Physical Characteristics of Data; Metadata; Metadata as Explicit Knowledge; Data Chain and Information Life Cycle; Data Lineage and Data Provenance; Concluding Thoughts; 4 Data Quality and Measurement; Purpose; Data Quality; Data Quality Dimensions; Measurement; Measurement as Data; Data Quality Measurement and the Business/IT Divide; Characteristics of Effective Measurements; Measurements must be Comprehensible and Interpretable; Measurements must be Reproducible; Measurements must be PurposefulData Quality Assessment Data Quality Dimensions, DQAF Measurement Types, Specific Data Quality Metrics; Data Profiling; Data Quality Issues and Data Issue Management; Reasonability Checks; Data Quality Thresholds; Process Controls; In-line Data Quality Measurement and Monitoring; Concluding Thoughts; 2: DQAF Concepts and Measurement Types; 5 DQAF Concepts; Purpose; The Problem the DQAF Addresses; Data Quality Expectations and Data Management; The Scope of the DQAF; DQAF Quality Dimensions; Completeness; Timeliness; Validity; Consistency; Integrity; The Question of AccuracyDefining DQAF Measurement TypesThe Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides pMorgan Kaufmann Series on Business IntelligenceData structures (Computer science)DatabasesQuality controlElectronic books.Data structures (Computer science)DatabasesQuality control.005.7/3Sebastian-Coleman Laura(Data quality author and practitioner)1040210MiAaPQMiAaPQMiAaPQBOOK9910463018203321Measuring data quality for ongoing improvement2462890UNINA