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Data governance tools /
Data governance programs often start off using programs such as Microsoft Excel and Microsoft SharePoint to document and share data governance artifacts. But these tools often lack critical functionality. Meanwhile, vendors have matured their data governance offerings to the extent that today's...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boise, ID :
MC Press,
©2014.
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright; About the Author; Contents; Forewords; By Aditya Kongara; By John R. Talburt; By Aaron Zornes; Preface; Part I-Introduction; 1: An Introduction to Data Governance; Definition; Case Study; The Pillars of Data Governance; Summary; 2: Enterprise Data Management Reference Architecture; Edm Categories; Big Data; Data Governance Tools; Summary; Part II-Categories of Data Governance Tools; 3: The Business Glossary; Bulk-load Business Terms in Excel, CSV, or XML Format; Create Categories of Business Terms; Facilitate Social Collaboration.
- Automatically Hyperlink Embedded Business TermsAdd Custom Attributes to Business Terms and Other Data Artifacts; Add Custom Relationships to Business Terms and Other Data Artifacts; Add Custom Roles to Business Terms and Other Data Artifacts; Link Business Terms and Column Names to the Associated Reference Data; Link Business Terms to Technical Metadata; Support the Creation of Custom Asset Types; Flag Critical Data Elements; Provide OOTB and Custom Workflows to Manage Business Terms and Other Data Artifacts; Review the History of Changes to Business Terms and Other Data Artifacts.
- Allow Business Users to Link to the Glossary Directly From Reporting ToolsSearch for Business Terms; Integrate Business Terms with Associated Unstructured Data; Summary; 4: Metadata Management; Pull Logical Models From Data Modeling Tools; Pull Physical Models From Data Modeling Tools; Ingest Metadata From Relational Databases; Pull in Metadata From Data Warehouse Appliances; Integrate Metadata From Legacy Data Sources; Pull Metadata From ETL Tools; Pull Metadata From Reporting Tools; Reflect Custom Code in the Metadata Tool; Pull Metadata From Analytics Tools.
- Link Business Terms with Column NamesPull Metadata From Data Quality Tools; Pull Metadata From Big Data Sources; Provide Detailed Views on Data Lineage; Customize Data Lineage Reporting; Manage Permissions in the Metadata Repository; Support the Search for Assets in the Metadata Repository; Summary; 5: Data Profiling; Conduct Column Analysis; Discover the Values Distribution of a Column; Discover the Patterns Distribution of a Column; Discover the Length Frequencies of a Column; Discover Hidden Sensitive Data; Discover Values with Similar Sounds in a Column.
- Agree on the Data Quality Dimensions for the Data Governance ProgramDevelop Business Rules Relating to the Data Quality Dimensions; Profile Data Relating to the Completeness Dimension of Data Quality; Profile Data Relating to the Conformity Dimension of Data Quality; Profile Data Relating to the Consistency Dimension of Data Quality; Profile Data Relating to the Synchronization Dimension of Data Quality; Profile Data Relating to the Uniqueness Dimension of Data Quality; Profile Data Relating to the Timeliness Dimension of Data Quality.