Read dama knowledge system 12 data design
Read dama knowledge system 12 data design. Png
Tools
Data modelling tools
1. 2. Data blood tools
1. 3. Data analysis tools
1. 4. Metadata library
1. 4. 3. The metadata repository must be user-friendly and accessible to access its contents
1. 4. 4. Data modelling tools usually contain a limited functionality library
1. 5. Data model
1. 5. 3. Package model (assembly patterson) means a set of building blocks across the range of business and data modelers
1. 5. 4. Integration pattern provides a framework for integrating packages in a common way
1. 6. Industry data models
2. Best practice in naming an agreement
2. 1. Iso 11179 metadata registry is an international standard for the expression of metadata in an organization and contains several parts related to data standards, including naming attributes and preparation definitions
2. 2. Data modelling and database design standards are guiding principles for effectively meeting business data needs and are consistent with enterprise and data architecture requirements to ensure data quality standards
2. 3. Dissemination of data models and database naming criteria for each type of modeling and database object
2. 4. Names should be unique and as descriptive as possible
2. 5. Logical names should be meaningful to business users, using complete words as far as possible and avoiding words other than the most familiar acronyms
2. 6. Physical names must meet the maximum length permitted by dbms and therefore abbreviations will be used as necessary
2. 7. Categorization (class word), the last term in an attribute name such as quantity, name and code, which can be used to distinguish between entity and listing properties from a list name
Best practices in database design
3. 1. Performance and ease of use
Reuseability
3. 3. Integrity
Safety
3. 5 maintenance
4. Development of data modelling and design standards
4. 1. Data analysts and designers, as intermediaries between information consumers (persons with data business needs) and data producers, must balance the data-use requirements of information consumers with those of data producers
4. 2. Data professionals must also balance short-term commercial interests with long-term commercial interests
4. 3. Data models and database design should be a reasonable balance between short-term and long-term needs of enterprises
4. 4. Lists and descriptions of deliverables for standard data modelling and database design
4. 5. List of standard names, acceptable abbreviations and rules for very wordindicators applicable to all data model objects
4. 6. List of standard name formats for all data model objects, including attributes and classification terms
4. 7. Listing and description of standard methods used to create and maintain these deliverables
4. 8. Listing and description of roles and responsibilities for data modelling and database design
4. 9. Listing and description of all metadata properties captured in data modelling and database design, including business metadata and technology metadata
4. 10. Metadata quality expectations and requirements
4. 11. Guidance on the use of data modelling tools
4. 12. Guidance for preparing and leading the design review
4. 13. Data model version control guidelines
4. 14. List of prohibited or avoidable matters
5. Evaluation of data models and database design quality
5. 1. Project teams should conduct needs and design reviews of conceptual data models, logical data models and physical database design
5. 2. Organization of expert panels in different areas with different backgrounds, skills, expectations and views to evaluate data models and database design
5. 3. Participants must be able to discuss different points of view and eventually reach a consensus in the group, without any personal conflict, as all participants share the common goal of promoting the most practical, performing and usable design
5. 4. If the review is unsuccessful, the modelling staff must adopt modifications to resolve all the issues raised by the evaluation team
5. 5. If there are problems that the modelling staff cannot solve on their own, the problem should be fed back to the system owner and a final solution should be sought
6. Management of data model versions and integration
6. 1. Data models and other design specifications require careful change control, as do demand specifications and other sdlc deliverables
6. 2. Each change shall be recorded
7. Measurement indicators
7. 1. To what extent does the model reflect business needs
7. 2. What is the integrity of the model
7. 2. 2. Completeness of metadata
7. 3. What is the match between models and models
What is the structure of the model
7. 5. How common are models
7. 6. What is the status of model compliance with naming criteria
7. 7. What is the readability of models
7. 8. What is the definition of the model
7. 9. How is the model consistent with the enterprise data architecture
7. 10. What is the match with metadata
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