LEADER 05162oam 2200529 450 001 9910810784503321 005 20170523091546.0 010 $a1-283-93318-7 010 $a0-12-397754-1 035 $a(OCoLC)847551134 035 $a(MiFhGG)GVRL8CVV 035 $a(EXLCZ)992670000000312360 100 $a20130102d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aMeasuring data quality for ongoing improvement $ea data quality assessment framework /$fLaura Sebastian-Coleman 205 $a1st edition 210 $aWaltham, Mass. $cElsevier$d2013 210 1$aWaltham, MA :$cMorgan Kaufmann, an imprint of Elsevier,$d2013. 215 $a1 online resource (xxxix, 324, 39 pages) $ccolor illustrations 225 1 $aThe Morgan Kaufmann Series on Business Intelligence 300 $aDescription based upon print version of record. 311 $a0-12-397033-4 320 $aIncludes bibliographical references and index. 327 $aFront 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 Framework 327 $aOverview 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 Facts 327 $aData 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, Dataset 327 $aSource 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 Purposeful 327 $aData 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 Accuracy 327 $aDefining DQAF Measurement Types 330 $aThe Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides p 410 0$aMorgan Kaufmann Series on Business Intelligence 606 $aData structures (Computer science) 606 $aDatabases$xQuality control 615 0$aData structures (Computer science) 615 0$aDatabases$xQuality control. 676 $a005.7/3 700 $aSebastian-Coleman$b Laura$c(Data quality author and practitioner)$01662190 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910810784503321 996 $aMeasuring data quality for ongoing improvement$94018673 997 $aUNINA