| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910463018203321 |
|
|
Autore |
Sebastian-Coleman Laura (Data quality author and practitioner) |
|
|
Titolo |
Measuring data quality for ongoing improvement [[electronic resource] ] : a data quality assessment framework / / Laura Sebastian-Coleman |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Waltham, Mass., : Elsevier, 2013 |
|
|
|
|
|
|
|
ISBN |
|
1-283-93318-7 |
0-12-397754-1 |
|
|
|
|
|
|
|
|
Edizione |
[1st edition] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (404 p.) |
|
|
|
|
|
|
Collana |
|
The Morgan Kaufmann Series on Business Intelligence |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Data structures (Computer science) |
Databases - Quality control |
Electronic books. |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
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 |
|
|
|
|
|
|
Sommario/riassunto |
|
The 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 |
|
|
|
|
|
|
|
| |