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Measuring data quality for ongoing improvement [[electronic resource] ] : a data quality assessment framework / / Laura Sebastian-Coleman
Measuring data quality for ongoing improvement [[electronic resource] ] : a data quality assessment framework / / Laura Sebastian-Coleman
Autore Sebastian-Coleman Laura (Data quality author and practitioner)
Edizione [1st edition]
Pubbl/distr/stampa Waltham, Mass., : Elsevier, 2013
Descrizione fisica 1 online resource (404 p.)
Disciplina 005.7/3
Collana The Morgan Kaufmann Series on Business Intelligence
Soggetto topico Data structures (Computer science)
Databases - Quality control
Soggetto genere / forma Electronic books.
ISBN 1-283-93318-7
0-12-397754-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Framework
Overview 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
Data 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
Source 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
Data 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
Defining DQAF Measurement Types
Record Nr. UNINA-9910463018203321
Sebastian-Coleman Laura (Data quality author and practitioner)  
Waltham, Mass., : Elsevier, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Measuring data quality for ongoing improvement : a data quality assessment framework / / Laura Sebastian-Coleman
Measuring data quality for ongoing improvement : a data quality assessment framework / / Laura Sebastian-Coleman
Autore Sebastian-Coleman Laura (Data quality author and practitioner)
Edizione [1st edition]
Pubbl/distr/stampa Waltham, Mass., : Elsevier, 2013
Descrizione fisica 1 online resource (xxxix, 324, 39 pages) : color illustrations
Disciplina 005.7/3
Collana The Morgan Kaufmann Series on Business Intelligence
Soggetto topico Data structures (Computer science)
Databases - Quality control
ISBN 1-283-93318-7
0-12-397754-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Framework
Overview 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
Data 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
Source 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
Data 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
Defining DQAF Measurement Types
Record Nr. UNINA-9910786101103321
Sebastian-Coleman Laura (Data quality author and practitioner)  
Waltham, Mass., : Elsevier, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Measuring data quality for ongoing improvement : a data quality assessment framework / / Laura Sebastian-Coleman
Measuring data quality for ongoing improvement : a data quality assessment framework / / Laura Sebastian-Coleman
Autore Sebastian-Coleman Laura (Data quality author and practitioner)
Edizione [1st edition]
Pubbl/distr/stampa Waltham, Mass., : Elsevier, 2013
Descrizione fisica 1 online resource (xxxix, 324, 39 pages) : color illustrations
Disciplina 005.7/3
Collana The Morgan Kaufmann Series on Business Intelligence
Soggetto topico Data structures (Computer science)
Databases - Quality control
ISBN 1-283-93318-7
0-12-397754-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Framework
Overview 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
Data 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
Source 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
Data 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
Defining DQAF Measurement Types
Record Nr. UNINA-9910810784503321
Sebastian-Coleman Laura (Data quality author and practitioner)  
Waltham, Mass., : Elsevier, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui