Data quality for the information age
著者
書誌事項
Data quality for the information age
(The Artech House computer science library)
Artech House, c1996
大学図書館所蔵 全3件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This informative book goes beyond the technical aspects of data management to provide detailed analyses of quality problems and their impacts, potential solutions and how they are combined to form an overall data quality program, senior management's role, methods used to make improvements, and the life-cycle of data quality. It concludes with case studies, summaries of main points, roles and responsibilities for each individual, and a helpful listing of "dos and don'ts".
目次
Why Care About Data Quality: Poor Data is Pervasive. Poor Data Quality Impacts Business Success. Data Quality Can be a Source of Competitive Advantage. Strategies for Improving Data Quality: Which Data to Improve? Improving Data Accuracy for One Database. Improving DataAccuracy for Two Databases. Improving Data Accuracy in the Warehouse. Data Quality Policy: What Should a Data Policy Cover? Needed Background on Data. A Model Data Policy. Deploying the Policy. Starting and Nurturing a Data Quality Program: A Model for Successful Change. Getting Started. Growth Stages. Becoming Part of the Mainstream. The Role of Senior Management. Process Management: Future Performance of Processes. Step 1 -- Estabish a Process Owner and Management Team. Step 2 -- Describe the Process and Understand Customer Needs. Step 3 -- Establish a Measurement System. Step 4 -- Establish Statistical control and Check Conformance to Requirements. Step 5 -- Identify Improvement Opportunities. Step 6 -- Select Opportunities. Step 7 -- Make and Sustain Improvements. Process Representation and the Functions of Information Processing Approach: Basic Ideas. The Information Model/The FIP Chart. Enhancements to the Basic Information Model. Measurement and Improvement Opportunities. Data Quality Requirements: Quality Function Deployment. Data Quality Requirements for an Existing Information Chain. Data Quality Requirements at the Design Stage. Statistical Quality Control: Variation. Stable Processes. Control Limits -- Statistical Theory and Methods of SQC. Interpreting Control Charts. Conformance to Requirements. Measurement Systems, Data Tracking, and Process Improvement: Measurement Systems. Process Requirements. What to Measure. The Measuring Device and Protocol -- Data Tracking. Implementation. Just What is (or are) Data?: The Data Life-Cycle. Data Defined. Management Properties of Data. A Model of an Enterprise's Data Resource. Information. Dimensions of Data Quality: Quality Dimensions of a Conceptual View. Quality Dimensions of Data Values. Quality Dimensions of Data Representation. More on Data Consistency. Data Quality and Re-Engineering at AT&T: First Steps. Re-Engineering. Data Quality Across the Corporation -- Telstra?s Experience: Program Definition. First Steps. Full Program. Results. Summary -- Roles and Responsibilities: Roles for Leaders. Roles for Process Owners. Roles for Information Professionals. Final Remarks -- The Three Most Important Points. Glossary. Index.
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