Completeness (Attributes)
The degree to which all required attributes in the dataset are present.
- •Ensures data models and datasets capture all necessary characteristics of an entity for business processes.
- •Facilitates comprehensive data analysis and richer feature engineering for AI/ML.
- •Reduces the need for ad-hoc data collection or inferring missing structural information.
- •Supports better data understanding and context as all relevant facets are defined.
- •Inability to perform required business functions if critical attributes are missing from the data structure.
- •Limited analytical capabilities due to lack of necessary descriptive data points.
- •Increased effort to augment datasets with missing structural elements.
- •Poor data modeling leading to systems that don't fully support business needs.
%
Logistics: The 'Supplier' dataset includes all necessary attributes like Name, Address, Tax ID, Contact Person, and Payment Terms.
Product Management: The 'Item Master' dataset schema contains all mandatory attributes such as 'Item Description', 'Unit of Measure', 'Weight', 'Dimensions', and 'Safety Stock Level'.
HR: The 'Employee' dataset design includes attributes for all legally required information plus essential operational fields.
Logistics: The 'Customer' dataset is missing the 'Tax ID' attribute, which is required for compliant invoicing in certain regions.
Product Management: A new 'Product' dataset schema omits the 'Country of Origin' attribute, which is legally required for import/export documentation.
HR: The employee dataset template does not include an attribute for 'Emergency Contact Information'.