How do you identify patterns, trends, and insights using ER diagrams?

1 Use cardinality and modality

Cardinality and modality are two aspects of the relationships between entities in an ER diagram. Cardinality indicates how many instances of one entity can be associated with another entity. Modality indicates whether the association is mandatory or optional. By using these concepts, you can identify the frequency, distribution, and variation of your data. For example, you can see how many customers have bought a certain product, how many products are sold by a certain vendor, or how many orders are placed by a certain region.

2 Use attributes and keys

Attributes are the properties or characteristics of an entity that describe its identity, quality, or quantity. Keys are special attributes that uniquely identify an entity or a relationship. By using attributes and keys, you can identify the features, values, and identifiers of your data. For example, you can see what are the common or rare attributes of your customers, what are the average or extreme values of your products, or what are the primary or foreign keys of your tables.

3 Use generalization and specialization

Generalization and specialization are two ways of organizing entities in an ER diagram based on their similarities and differences. Generalization is the process of combining entities that share common attributes into a higher-level entity. Specialization is the process of dividing an entity into lower-level entities that have specific attributes. By using generalization and specialization, you can identify the categories, subcategories, and hierarchies of your data. For example, you can see how your products are classified into different types, how your customers are segmented into different groups, or how your employees are organized into different levels.

4 Use aggregation and composition

Aggregation and composition are two ways of representing the relationships between entities in an ER diagram based on their dependencies and lifetimes. Aggregation is the process of grouping entities that are related but independent into a higher-level entity. Composition is the process of combining entities that are dependent and have the same lifetime into a lower-level entity. By using aggregation and composition, you can identify the parts, wholes, and associations of your data. For example, you can see how your orders are composed of order items, how your order items are aggregated from products, or how your products are associated with vendors.

5 Use normalization and denormalization

Normalization and denormalization are two techniques for optimizing the performance and quality of your database. Normalization is the process of eliminating redundancy and inconsistency from your data by dividing it into smaller and simpler tables. Denormalization is the process of increasing redundancy and complexity from your data by combining it into larger and richer tables. By using normalization and denormalization, you can identify the trade-offs, benefits, and costs of your data model. For example, you can see how normalization improves data integrity, security, and flexibility, or how denormalization improves data access, speed, and analysis.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?