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Enterprise knowledge management : the data quality approach / David Loshin.

By: Material type: TextTextPublisher: San Diego : Morgan Kaufmann, [2001]Copyright date: ©2001Description: xviii, 493 pages, 1 unnumbered pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 0124558402
  • 9780124558403
Subject(s): DDC classification:
  • 658.4038
LOC classification:
  • HD30.2. L67 2001
Contents:
Preface -- Chapter 1 - Introduction -- Chapter 2 - Who Owns Information? -- Chapter 3 - Data Quality in Practice -- Chapter 4 - Economic Framework of Data Quality and the Value Proposition -- Chapter 5 - Dimensions of Data Quality -- Chapter 6 - Statistical Process Control and the Improvement Cycle -- Chapter 7 - Domains, Mappings, and Enterprise Reference Data -- Chapter 8 - Data Quality Assertions and Business Rules -- Chapter 9 - Measurement and Current State Assessment -- Chapter 10 - Data Quality Requirements -- Chapter 11 - Metadata, Guidelines, and Policy -- Chapter 12 - Rule-Based Data Quality -- Chapter 13 - Metadata and Rule Discovery -- Chapter 14 - Data Cleansing -- Chapter 15 - Root Cause Analysis and Supplier Management -- Chapter 16 - Data Enrichment/Enhancement -- Chapter 17 - Data Quality and Business Rules in Practice -- Chapter 18 - Building the Data Quality Practice.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Book City Campus City Campus Main Collection 658.4038 LOS (Browse shelf(Opens below)) 1 Available A410423B

Includes bibliographical references (page 494) and index.

Preface -- Chapter 1 - Introduction -- Chapter 2 - Who Owns Information? -- Chapter 3 - Data Quality in Practice -- Chapter 4 - Economic Framework of Data Quality and the Value Proposition -- Chapter 5 - Dimensions of Data Quality -- Chapter 6 - Statistical Process Control and the Improvement Cycle -- Chapter 7 - Domains, Mappings, and Enterprise Reference Data -- Chapter 8 - Data Quality Assertions and Business Rules -- Chapter 9 - Measurement and Current State Assessment -- Chapter 10 - Data Quality Requirements -- Chapter 11 - Metadata, Guidelines, and Policy -- Chapter 12 - Rule-Based Data Quality -- Chapter 13 - Metadata and Rule Discovery -- Chapter 14 - Data Cleansing -- Chapter 15 - Root Cause Analysis and Supplier Management -- Chapter 16 - Data Enrichment/Enhancement -- Chapter 17 - Data Quality and Business Rules in Practice -- Chapter 18 - Building the Data Quality Practice.

Machine converted from AACR2 source record.

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