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Enterprise Knowledge Management

David Loshin. Editorial: Morgan Kaufmann. Páginas: 493. Precio: 71,16$

Enterprise Knowledge Management – 1st Edition

Table of 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  


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.

Key Features

  • Expert advice from a highly successful data quality consultant
  • The only book on data quality offering the business acumen to appeal to managers and the technical expertise to appeal to IT professionals
  • Details the high costs of bad data and the options available to companies that want to transform mere data into true enterprise knowledge
  • Presents conceptual and practical information complementing companies’ interest in data warehousing, data mining, and knowledge discovery

Readership

IT, Database, and Business Managers

Fecha de publicaciónenero 17, 2001

BELT.ES no se hace responsable de las opiniones de los artículos reproducidos en nuestra Revista de Prensa, ni hace necesariamente suyas las opiniones y criterios expresados. La difusión de la información reproducida se realiza sin fines comerciales. 

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