Measuring data quality for ongoing improvement a data quality assessment framework
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 o...
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Waltham, Mass. :
Elsevier
2013.
Waltham, MA : 2013. |
| Edition: | 1st edition |
| Series: | Morgan Kaufmann Series on Business Intelligence
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| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628564906719 |
Table of Contents:
- 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
