Principles of Data Management Facilitating information sharing.
Data is a valuable corporate asset and its effective management is vital to success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. The new edition adds chapters on linked data, concept systems and big data and artifi...
Saved in:
| Online Access: |
Full Text (via ProQuest) |
|---|---|
| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Swindon :
BCS Learning & Development Limited,
2022.
|
| Edition: | 3rd ed. |
MARC
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| 100 | 1 | |a Gordon, Keith. | |
| 245 | 1 | 0 | |a Principles of Data Management |b Facilitating information sharing. |
| 250 | |a 3rd ed. | ||
| 260 | |a Swindon : |b BCS Learning & Development Limited, |c 2022. | ||
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| 505 | 0 | |a Front Cover -- Half-Title Page -- BCS, The Chartered Institute for It -- Title Page -- Copyright Page -- Contents -- List of figures -- About the author -- Foreword to the first edition -- Abbreviations -- Glossary -- Preface -- Introduction -- PART 1: PRELIMINARIES -- 1. DATA AND THE ENTERPRISE -- Information is a key business resource -- The relationship between information and data -- The data landscape -- The importance of the quality of data -- The common problems with data -- An enterprise-wide view of data -- Managing data is a business issue -- Summary. | |
| 505 | 8 | |a 2. DATABASES AND THEIR DEVELOPMENT -- The database architecture of an information system -- Types of database -- An overview of the database development process -- Conceptual data modelling (from a project-level perspective) -- Relational data analysis -- The roles of a data model -- Physical database design -- Summary -- 3. WHAT IS DATA MANAGEMENT? -- The problems encountered without data management -- Data management responsibilities -- Data management activities -- Roles within data management -- The benefits of data management. | |
| 505 | 8 | |a The relationship between data management and enterprise architecture -- Summary -- PART 2: DATA ADMINISTRATION -- 4. CORPORATE DATA MODELLING -- Why develop a corporate data model? -- The nature of a corporate data model -- How to develop a corporate data model -- Corporate data model principles -- Summary -- 5. DATA DEFINITION AND NAMING -- The elements of a data definition -- Data naming conventions -- Summary -- 6. METADATA -- What is metadata? -- Metadata for data management -- Metadata for content management -- Metadata for describing data values -- Summary -- 7. DATA QUALITY. | |
| 505 | 8 | |a What is data quality? -- Issues associated with poor data quality -- The causes of poor data quality -- The dimensions of data quality -- Metadata quality -- Data model quality -- Approaches to improving data quality -- Improving data quality is a business issue -- Summary -- 8. DATA ACCESSIBILITY -- Data security -- Data integrity -- Data recovery -- Summary -- 9. MASTER DATA MANAGEMENT -- What is master data? -- How do problems with master data occur? -- How do we manage master data? -- Summary -- PART 3: DATABASE AND REPOSITORY ADMINISTRATION -- 10. DATABASE ADMINISTRATION. | |
| 505 | 8 | |a Database administration responsibilities -- Performance monitoring and tuning -- Summary -- 11. REPOSITORY ADMINISTRATION -- Repositories, data dictionaries, encyclopaedias, catalogs and directories -- Repository features -- The repository as a centralised source of information -- Metadata models -- Summary -- PART 4: THE DATA MANAGEMENT ENVIRONMENT -- 12. THE USE OF PACKAGED APPLICATION SOFTWARE -- What are application software packages? -- The impact on data management -- Summary -- 13. DISTRIBUTED DATA AND DATABASES -- The rationale for distributing data. | |
| 500 | |a The perfect distributed database system? | ||
| 520 | |a Data is a valuable corporate asset and its effective management is vital to success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. The new edition adds chapters on linked data, concept systems and big data and artificial intelligence. | ||
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