Top 8 Considerations of Data Modeling

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:

1. Aligns with business very well
2. Connects with data and scales for the future
3. 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.