Recently, we published a whitepaper on the Data Modelling approach and processes involved. There, we had discussed the top eight considerations of standard and logical data models. The following is a summary of the important data modelling guidelines.
Data Modelled Well:
- Aligns with business very well
- Connects with data and scales for the future
- Enables good governance and integrity of data across the organization
The following are the top eight considerations:
- Model Correctness:
- Ensure that the model accurately captures the material. the material?
- Make sure that the design represents the data requirements.
- Ensure the correctness of data elements with different formats than industry standards.
- Fix incorrect cardinality and keys defined incorrectly
- Model Completeness:
- Does the scope of the model exactly match the requirement?
- Can a model be complete yet incorrect? Incomplete yet correct?
- If relationships are not shown, then they should clarify any ambiguously defined terms.
- Model Structure:
- Standard modeling practices, independent of content
- Entity Structure Review
- Data Element Review
- Relationship Review
- Model Flexibility
- Ensures that the correct level of abstraction is applied to capture new requirements.
- Achieves the right level of flexibility.
- Proves there is value in every abstraction situation.
- Modeling Standards & Guidelines
- Ensures correct and consistent enterprise, conceptual, logical, and physical level as per standards & guidelines.
- Uses the correct names and abbreviations
- Model Representation
- Optimal parent and child entities placement
- Intelligent use of color in grouping or highlighting entities
- Proper relationship lines crossing each other or through unrelated entities
- Optimal use of subject area
- Maximizes readability and understanding
- Physical Design Accuracy:
- Ensures that the design is for the real world & also specific to application
- Considers null values
- Uses partitioning
- Utilizes proper indexing and space
- Considers denormalization
- Data Quality:
- Ensures that the design and actual data are in sync with each other.
- Determines how well the data elements and their rules match reality.
- Avoids costly surprises later in development.
