How do you choose between logical and physical data models for your system analysis?

1 What is a logical data model?

A logical data model is a conceptual representation of the data and its meaning, without considering how it is stored or implemented in a specific system. It focuses on the entities, attributes, and relationships that are relevant to the business domain and the user needs. A logical data model is usually expressed in a graphical notation, such as an entity-relationship diagram (ERD), or a textual notation, such as a data dictionary. A logical data model is independent of any hardware, software, or data format.

2 What is a physical data model?

A physical data model is a detailed representation of how the data is actually stored and manipulated in a specific system. It takes into account the technical aspects, such as the data types, formats, sizes, constraints, indexes, keys, and performance. A physical data model is usually expressed in a schema language, such as SQL, or a diagram that shows the tables, columns, and relationships of a database. A physical data model is dependent on the choice of hardware, software, and data format.

3 Why use logical and physical data models?

Logical and physical data models serve different purposes and audiences in system analysis. A logical data model helps to capture the essential meaning and structure of the data, without being influenced by the implementation details. It is useful for communicating with the stakeholders, such as the users, managers, and analysts, who are interested in the business rules and requirements of the system. A physical data model helps to optimize the data storage and manipulation, according to the technical specifications and constraints. It is useful for communicating with the developers, programmers, and administrators, who are responsible for building and maintaining the system.

4 How to choose between logical and physical data models?

When deciding whether to use a logical or physical data model for your system analysis project, there is no definitive answer. It depends on the scope, complexity, and stage of your project, the availability and skills of your team, and the expectations and preferences of your stakeholders. Generally speaking, if you are in the early or intermediate stages of your project, you should start with a logical data model to validate and verify assumptions, identify inconsistencies or gaps, and facilitate collaboration with stakeholders. Once you have a clear and stable understanding of the data requirements and business logic of your system, then you should move to a physical data model to implement and test your system. If you want to have a comprehensive view of your system from both conceptual and technical perspectives, use both models. You can use one as a blueprint for creating or updating the other, as well as compare and contrast similarities and differences between them.

5 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?