Naturalness
The degree to which the composition of datasets is aligned with the real-world objects that they represent.
- •Data models that intuitively reflect real-world business entities and processes.
- •Easier understanding and use of data by business users and analysts.
- •More stable and maintainable data structures that adapt well to business changes.
- •Reduced complexity in data modeling and database design.
- •Complex and counter-intuitive data models that are difficult for users to understand and query.
- •Increased risk of misinterpreting data due to unnatural or convoluted structures.
- •Higher maintenance costs and difficulties in evolving data models as business needs change.
- •Overly denormalized or poorly structured data leading to redundancy and update anomalies.
Grade
Logistics: Having separate, well-defined datasets (or tables) for Vessels, Containers, Moves, Customers, and Invoices, each reflecting distinct real-world entities and their relationships.
E-commerce: Separate datasets for 'Products', 'Customers', 'Orders', and 'Order_Items' naturally model the business domain.
University: Datasets are structured around 'Students', 'Courses', 'Faculty', and 'Enrollments', aligning with how the university actually operates and thinks about its data.
Logistics: Storing individual container move details, vessel visit information, and customer billing data all within the same highly complex, denormalized table.
E-commerce: A single 'Product' table contains attributes for physical products, digital downloads, and service subscriptions, making the schema overly complicated and hard to manage.
University: A 'Student_Course_Enrollment_Grade_Faculty' dataset combines too many distinct real-world concepts, leading to data redundancy and update anomalies.