The Data Quality Approach To order this title, and for more information, click here
By David Loshin, President, Knowledge Integrity Incorporated, Silver Springs, MD, USA
Description Today, companies capture and store tremendous amounts of information about every aspect of their business: their customers, partners,
vendors, markets, and more. But with the rise in the quantity of information has come a corresponding decrease in its quality--a problem
businesses recognize and are working feverishly to solve.Enterprise Knowledge Management: The Data Quality Approach presents
an easily adaptable methodology for defining, measuring, and improving data quality. Author David Loshin begins by presenting an economic
framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches
to consolidating enterprise knowledge. Written for both a managerial and a technical audience, this book will be indispensable to the
growing number of companies committed to wresting every possible advantage from their vast stores of business information.
Audience
IT, Database, and Business Managers
Contents Preface
Chapter 1 - Introduction
Data Quality Horror Stories
Knowledge Management and Data Quality
Reasons for Caring about Data
Quality
Knowledge Management and Business Rules
Structure of this Book
Chapter 2 - Who Owns Information?
The Information Factory
Complicating Notions
Responsibilities of Ownership
Ownership Paradigms
Centralizing, Decentralization and Data Ownership Policies
Ownership and Data Quality
Summary
Chapter 3 - Data Quality in Practice
Data Quality Defined: Fitness for Use
The Quality Improvement
Program
Data Quality and Operations
Data Quality and Databases
Data Quality and the Data Warehouse
Data Mining
Data Quality and
Electronic Data Interchange
Data Quality and the World Wide Web
Summary
Chapter 4 - Economic Framework of Data Quality and the
Value Proposition
Evidence of Economic Impact
Data Flows and Information Chains
Examples of Information Chains
Impacts
Economic Measures
Impact Domains
Operational Impacts
Tactical and Strategic Impacts
Putting It All Together - the Data Quality Scorecard
Adjusting
the Model for Solution Costs
Example
Summary
Chapter 5 - Dimensions of Data Quality
Sample Data Application
Data Quality of
Data Models
Data Quality of Data Values
Data Quality of Data Domains
Data Quality of Data Presentation
Data Quality of Information
Policy
Summary: Importance of the Dimensions of Data Quality
Chapter 6 - Statistical Process Control and the Improvement Cycle
Variation and Control
Control Chart
The Pareto Principle
Building a Control Chart
Kinds of Control Charts
Example: Invalid Records
The Goal of Statistical Process Control
Interpreting a Control Chart
Finding Special Causes
Maintaining Control
Summary
Chapter
7 - Domains, Mappings, and Enterprise Reference Data
Data Types
Operations
Domains
Mappings
Example: Social Security Numbers
Domains,
Mappings, and Metadata
The Publish/Subscribe Model of Reference Data Provision
Summary
Chapter 8 - Data Quality Assertions and
Business Rules
Data Quality Assertions as Business Rules
The 9 Classes of Data Quality Rules
"Null Value" Rules
Value Manipulation
Operators and Functions
Value Rules
Domain Membership Rules
Domain Mappings and Relations on Finite Defined Domains
Relation Rules
Table, Cross-Table, and Cross-Message Assertions
In-Process Rules
Operational Rules
Other Rules
Rule Management, Compilation, and
Validation
Rule Ordering
Summary
Chapter 9 - Measurement and Current State Assessment
Identify Each Data Customer
Mapping the
Information Chain
Choose Locations in the Information Chain
Choose a Subset of the DQ Dimensions
Identify Sentinel Rules
Measuring
Data Quality
Measuring Data Quality of Data Models
Measuring Data Quality of Data Values
Measuring Data Quality of Data Domains
Measuring
Data Quality of Data Presentation
Measuring Data Quality of Information Policy
Static vs. Dynamic Measurement
Compiling Results
Summary
Chapter 10 - Data Quality Requirements
The Assessment Process, Reviewed
Reviewing the Assessment
Determining Expectations
Use
Case Analysis
Assignments of Responsibility
Creating Requirements
The Data Quality Requirements
Summary
Chapter 11 - Metadata,
Guidelines, and Policy
Generic Elements
Data Types and Domains
Schema Metadata
Use and Summarization
Historical
Managing Data Domains
Managing Domain Mappings
Managing Rules
Metadata Browsing
Metadata as a Driver of Policy
Summary
Chapter 12 - Rule-Based
Data Quality
Rule Basics
What is a Business Rule?
Data Quality Rules are Business Rules (and Vice-Versa)
Advantages of the Rule-Based
Approach
Integrating a Rule-Based System
Rule Execution
Deduction vs. Goal-Orientation
Evaluation of a Rules System
Limitations
of the Rule-based Approach
Rule Based Data Quality
Summary
Chapter 13 - Metadata and Rule Discovery
Domain Discovery
Mapping
Discovery
Clustering for Rule Discovery
Key Discovery
Decision and Classification Trees
Association Rules and Data Quality Rules
Summary
Chapter 14 - Data Cleansing
Standardization
Common Error Paradigms
Record Parsing
Metadata Cleansing
Data Correction
and Enhancement
Approximate Matching and Similarity
Consolidation
Updating Missing Fields
Address Standardization
Summary
Chapter 15 - Root Cause Analysis and Supplier Management
What is Root Cause Analysis?
Debugging the Process
Debugging the Problem
Corrective Measures - Resolve or Not?
Supplier Management
Summary
Chapter 16 - Data Enrichment/Enhancement
What is Data Enrichment?
Examples of Data Enhancement
Enhancement through Standardization
Enhancement through Provenance
Enhancement through Context
Enhancement
through Data Mining
Data Matching, Merging, and Record Linkage
Large Scale Data Aggregation and Linkage
Improving Linkage with Approximate
Matching
Enhancement through Inference
Data Quality Rules for Enhancement
Business Rules for Enhancement
Summary
Chapter 17
- Data Quality and Business Rules in Practice
Turning Rules into Implementation
Operational Directives
Data Quality and the Transaction
Factory
Data Quality and the Data Warehouse
Rules and EDI
Data Quality Rules and Automated UIs
Summary
Chapter 18 - Building
the Data Quality Practice
Recognize the Problem
Management Support and the Data Ownership Policy
Spread the Word
Mapping the Information
Chain
Data Quality Scorecard
Current State Assessment
Requirements Assessment
Choose a Project
Build Your Team
Build Your Arsenal
Metadata Model
Define Data Quality Rules
Archaeology/Data Mining
Manage Your Suppliers
Execute the Improvement
Measure Improvement
Build on Each Success
Conclusion
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The Data Quality Approach 
