Data Quality Requirements Analysis and Modeling (Classic Reprint) - Hardcover

Richard Y. Wang

Hardcover

ISBN 10: 0656172649 ISBN 13: 9780656172641

Publisher: Forgotten Books, 2018

Synopsis

Improve trust in your data with an attribute-based approach to data quality.

This book presents a practical model for tagging data with quality indicators and interpretable rules that help you assess believability and usefulness in real applications. It explains how data quality dimensions relate to concrete indicators and shows how to reason about data accuracy, timeliness, completeness, and consistency in everyday workflows.

The text outlines a workflow from defining data quality concepts to applying them in database design and querying. You’ll see how data tagging works, how quality indicators flow through data structures, and how integrity rules keep data and its quality indicators in sync. It also introduces a formal algebra for processing quality requirements alongside standard data queries, making quality an explicit part of data analysis.

• Learn the core dimensions of data quality and how they affect decision making.
• Discover how to tag data with origin, collection, and quality information to improve believability.
• Explore the idea of atomic data values paired with their quality indicators and how this affects updates.
• See how the extended query approach lets you demand quality constraints in your results.

Ideal for readers involved in data management, database design, and data quality initiatives who want a practical framework for improving data reliability.

Edition details

• Publisher: Forgotten Books
• Publication date: 2018
• Language: English
• ISBN 10: 0656172649
• ISBN 13: 9780656172641
• Binding: Hardcover
• Number of pages: 44