Data Resource Quality: Turning Bad Habits into Good Practices

Michael H. Brackett

  • 出版商: Addison Wesley
  • 出版日期: 2000-09-18
  • 售價: $1,620
  • 貴賓價: 9.5$1,539
  • 語言: 英文
  • 頁數: 384
  • 裝訂: Paperback
  • ISBN: 0201713063
  • ISBN-13: 9780201713060
  • 已絕版
    無現貨庫存(No stock available)




The complete "self-help guide" to dramatically improving business data quality.

  • Why business data goes bad -- and exactly what you can do it about it.
  • Ten specific data quality improvement techniques you can start using today.
  • Dramatically enhance the ROI of any data warehousing, customer relationship, and knowledge management application!
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.

Michael H. Brackett retired as data resource coordinator for the State of Washington in 1996. He has 37 years of information technology experience, and has written five books on the topic of data resources, including Data Sharing Using a Common Data Architecture, and The Data Warehouse Challenge: Taming Data Chaos.

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Appropriate Courses

Database Management and Design.

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Table Of Contents

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.
Comparate Data Cycle.
Business Intelligence Value Chain.
Data Risk and Hazard.
The Ten Sets of Habits and Practices.

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.

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.

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.

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 Names.
Data Integrity Rule Notation.
Data Integrity Rule Types.
Fundamental Data Integrity Rules.
Data Integrity Rule Enforcement.
Proactive Data Quality Management.
Precise Data Integrity Rule Benefits.
Higher Data Quality.
Limited Data Disparity.
Improved Productivity.
Best Practices.

6. Robust Data Documentation.
Limited Data Documentation.
Data Documentation Not Complete.
Data Documentation Not Current.
Data Documentation Not Understandable.
Data Documentation Redundant.
Data Documentation Not Readily Available.
Data Documentation Existence Unknown.
Limited Data Documentation Impacts.
Limited Awareness.
Continued Data Disparity.
Lost Productivity.
Robust Data Documentation.
Data Resource Data Concept.
Data Resource Data Aspects.
Complete Data Documentation.
Current Data Documentation.
Understandable Data Documentation.
Non-Redundant Data Documentation.
Readily Available Data Documentation.
Data Documentation Known to Exist.
Ancillary Data Documentation.
Robust Data Documentation Benefits.
Increased Awareness.
Halted Data Disparity.
Improved Productivity.
Best Practices.

7. Reasonable Data Orientation.
Unreasonable Data Orientation.
Physical Orientation.
Multiple Fact Orientation.
Process Orientation.
Operational Orientation.
Independent Orientation.
Inappropriate Business Orientation.
Unreasonable Data Orientation Impacts.
Lost Business Focus.
Continued Data Disparity.
Performance Problems.
Lost Productivity.
Reasonable Data Orientation.
Business Subject Orientation.
Business Client Orientation.
Five-Tier Concept.
Data Normalization.
Single Architecture Orientation.
Single Fact Orientation.
Reasonable Data Orientation Benefits.
Improved Business Support.
Promotion of Comparate Data Resource.
Improved Productivity.
Best Practices.

8. Acceptable Data Availability.
Unacceptable Data Availability.
Data Not Readily Accessible.
Inadequate Data Protection.
Inadequate Data Recovery.
Unprotected Privacy and Confidentiality.
Inappropriate Data Use.
Unacceptable Data Availability Impacts.
Limited Data Sharing.
Encourage Data Disparity.
Impact on Business.
Impact on People.
Acceptable Data Availability.
Adequate Data Accessibility.
Adequate Data Protection.
Adequate Data Recovery.
Protected Privacy and Confidentiality.
Appropriate Data Use.
Acceptable Data Availability Benefits.
Better Staff Use.
Shared Data Resource.
Fewer Impacts.
Best Practices.

9. Adequate Data Responsibility.
Inadequate Data Responsibility.
No Centralized Control.
No Management Procedures.
No Data Stewardship.
Inadequate Data Responsibility Impacts.
Limited Data Sharing.
Data Disparity Encouraged.
Adequate Data Responsibility.
Authorized Data Stewardship.
Reasonable Management Procedures.
Centralized Control.
Adequate Data Responsibility Benefits.
Shared Data Resource.
Best Practices.

10. Expanded Data Vision.
Restricted Data Vision.
Limited Data Scope.
Unreasonable Development Direction.
Unrealistic Planning Horizon.
Restricted Data Vision Impacts.
Short-Term Impact.
Future Impact.
Expanded Data Vision.
Wider Data Scope.
Reasonable Development Direction.
Realistic Planning Horizon.
Cooperative Establishment.
Expanded Data Vision Benefits.
Improved Business Support.
Best Practices.

11. Appropriate Data Recognition.
Inappropriate Data Recognition.
Wrong Target Audience.
Requiring Unnecessary Justification.
Search for Silver Bullets.
Attempt to Automate Understanding.
Belief in Standards.
Generic Data Models.
Inappropriate Data Recognition Impacts.
Business Impacts.
Encourage Data Disparity.
Appropriate Data Recognition.
Target Vested Interest.
Direct Business Involvement.
Tap the Knowledge Base.
Start within Current Budget.
Incrementally Cost Effective Approach.
Proof-Positive Perspective.
Be Opportunistic.
Building on Lessons Learned.
NoBlame-No Whitewash Attitude.
No Unnecessary Justification.
Appropriate Data Recognition Benefits.
Continued Business Support.
Best Practices.

12. Data Resource Quality Direction.
A Quick Review.
The Bad Habits.
Impacts of the Bad Habits.
The Good Practices.
Benefits of the Good Practices.
Best Practices to Implement First.
What Didn't Get on the List.
Data Resource Value Chain.
Data Architecture Value Chain.
Data Management Value Chain.
Data Resource Framework.
Setting a New Course for Quality.
An Awakening.
Information versus Technology.
Quick-Fix Hype.
What Happens with a Status Quo.
Principles and Techniques Available.
No Blessing Required.
The Cost of Quality.

Appendix A: Summary of the Ten Ways.
Appendix B: Summary of Evaluation Criteria.
Appendix C: Data Structure Examples.
Appendix D: Purchasing a Data Architecture.
Index. 0201713063T04062001

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