Data Resource Quality

Michael Brackett provides a wake-up call for information technology managers around the world. We can continue with our current practices, slowing down our ability to compete, or we can buckle down and focus on the basics of improving data quality. This book provides a look at the fundamentals of good data management practice. Use it well.
-From the Foreword by Ron Shelby, Chief Information Officer, e-GM

Poor data quality impacts every facet of today's private enterprises and public organizations. The deplorable condition of this critical resource in organizations around the world lowers productivity, impedes the creation of decision support systems (such as data warehousing), and hinders the development of e-commerce and other strategic initiatives. The future success of organizations will greatly depend on how well they design and maintain their data resources.

Written by a world expert in data resources, Data Resource Quality features the ten most fundamental and frequently exhibited bad habits that contribute to poor data quality, and presents the strategies and best practices for effective solutions. With this information, IT managers will be better equipped to implement an organization-wide, integrated, subject-oriented data architecture and within that architecture build a high-quality data resource. The result: reduced data disparity and duplication, increased productivity, and improved data understanding and utilization.

Covering both data architecture and data management issues, the book describes the impact of poor data practices, demonstrates more effective approaches, and reveals implementation pointers for quick results. Readers will find coverage of such vital data quality issues as:

The need for formal data names and comprehensive data definitions
Proper data structures, covering the entity-relation diagram and the combined three-tier and five-schema structure
Precise data integrity rules
Robust data documentation
Reasonable data orientation, including business subject, business client, and single-architecture orientation
Acceptable data availability issues, covering backup, recovery, and privacy
Adequate data responsibility, discussing authorized stewardship, centralized control, and procedures
Expanded data vision for improved business support
More appropriate data recognition leading to better data targeting within the organization
With these strategies for successful data resource development, IT managers will be able to set a proper course for an efficient and profitable long-term data resource solution.

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Auteur

Michael H. Brackett is an acknowledged leader in the field of data processing. During his forty-year career, he has originated many innovations, including the common data architecture concept, the data resource framework, and the business intelligence value chain. The founder of Data Resource Design and Remodeling, he served as the state of Washington's Data Resource Coordinator, where he developed a common data architecture for the state that spans multiple jurisdictions and disciplines. In addition, he has taught data design and modeling at the University of Washington and has written five books on the topic, including The Data Warehouse Challenge: Taming Data Chaos (Wiley). He currently serves as president of DAMA International.

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Résumé

Poor data quality hampers today's organizations in many ways: it makes data warehousing and knowledge management applications more expensive and less effective, presents major obstacles to e-Business transformation, slashes day-to-day employee productivity, and translates directly into poor strategic and tactical decisions. In this book, data expert Michael Brackett presents ten "bad habits" that lead to poor data -- and ten proven solutions that enable business managers to transform these bad habits into best practices. Data Resource Quality is organized around ten "bad habits" organizations have fallen into: habits that inevitably reduce data quality, waste resources, increase the cost of using and maintaining data resources, and compromise business strategies. In each case, Brackett shows how the "bad habits" evolved, and exactly how to replace them with best practices that can dramatically improve data quality, starting now. Along the way, Brackett demonstrates exactly how to implement a solid foundation for quality data -- an organization-wide, integrated, subject-oriented data architecture -- and then build a high-quality data resource within that architecture. For all IT managers, consultants, and application users -- in both large and small enterprises.

Contenu

Foreword.

Preface.

Acknowledgments.

About the Author.

1. State of the Data Resource.

Disparate Data Resource.

Business Information Demand.

Disparate Data.

Disparate Data Cycle.

Disparate Data Spiral.

Data Resource Drift.

Impact on Information Quality.

High-Quality Data Resource.

Disparate Data Shock.

Data Are a Resource.

Comparate Data Resource.

Integrated Data Resource.

Subject-Oriented Data Resource.

Terminology.

Comparate Data Cycle.

Business Intelligence Value Chain.

Data Risk and Hazard.

The Ten Sets of Habits and Practices.

Summary.

2. Formal Data Names.

Informal Data Names.

Meaningless Data Names.

Non-Unique Data Names.

Structureless Data Names.

Incorrect Data Names.

Informal Data Name Abbreviations.

Unnamed Data Resource Components.

Informal Data Name Impacts.

Limited Data Identification.

Perpetuated Data Disparity.

Lost Productivity.

Formal Data Names.

Data Naming Taxonomy.

Data Naming Vocabulary.

Primary Data Name.

Standard Data Names.

Data Name Word Abbreviation.

Data Name Abbreviation Algorithm.

Formal Data Name Benefits.

Readily Identified Data.

Limited Data Disparity.

Improved Productivity.

Best Practices.

Summary.

3. Comprehensive Data Definitions.

Vague Data Definitions.

Non-Existent Data Definitions.

Unavailable Data Definitions.

Short Data Definitions.

Meaningless Data Definitions.

Outdated Data Definitions.

Incorrect Data Definitions.

Unrelated Definitions.

Vague Data Definition Impacts.

Inhibited Data Understanding.

Inappropriate Data Use.

Perpetuated Data Disparity.

Lost Productivity.

Comprehensive Data Definitions.

Meaningful Data Definitions.

Thorough Data Definitions.

Correct Data Definitions.

Fundamental Data Definitions.

Comprehensive Data Definition Benefits.

Improved Data Understanding.

Limited Data Disparity.

Increased Productivity.

Best Practices.

Summary.

4. Proper Data Structure.

Improper Data Structures.

Detail Overload.

Wrong Audience Focus.

Inadequate Business Representation.

Poor Data Structure Techniques.

Improper Data Structure Impacts.

Poor Business Understanding.

Poor Performance.

Continued Data Disparity.

Lower Productivity.

Proper Data Structure.

Data Structure Components.

Proper Detail for the Audience.

Formal Design Techniques.

Proper Data Structure Benefits.

Improved Business Representation.

Reduced Data Disparity.

Improved Productivity.

Best Practices.

Summary.

5. Precise Data Integrity Rules.

Imprecise Data Integrity Rules.

Ignoring a High Data Error Rate.

Incomplete Data Integrity Rules.

Delayed Data Error Identification.

Default Data Values.

Nonspecific Data Domains.

Nonspecific Data Optionality.

Undefined Data Derivation.

Uncontrolled Data Deletion.

Imprecise Data Integrity Rule Impacts.

Bad Perception.

Inappropriate Business Actions.

Lost Productivity.

Precise Data Integrity Rules.

Data Rule Concept.

Data Integrity Rule Nam…