Data models: CDM vs. LDM vs. PDM
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With the right data model, you can visualize how your database works—from the data elements to the relationships among them and how data is used in business processes. There are different types of data models and they have different uses and value in their own contexts.
Conceptual vs. logical vs. physical data models
Data modeling is a core part of enterprise data management. Database development is one area where data models are useful, but data modeling actually offers value in a broader range of use cases. By visualizing data elements and their relationships, teams can use data models to shape business processes and technical designs. For business information systems, it is absolutely critical to understand and communicate about data effectively—placing data modeling at the center of how organizations leverage data.
The three general types of data modeling, in other words, do have a lot in common, but there are also important distinctions between them. Choosing the right data model for each use case helps you maximize the value your team gains from data modeling.
Conceptual data model (CDM)
CDMs are very high-level. This high-level perspective makes conceptual data modeling ideal for presenting information to stakeholders and those in non-technical roles in your organization. With further development, you can modify the data model into other models with greater complexity and detail, making conceptual models a great starting point as you move into other diagrams.
During the early stages of scoping and defining requirements, conceptual data models are often used to build use cases and establish context.
Popular use cases for CDMs include:
- Creating an initial data model: You can start with a CDM and evolve it over time into more complex models.
- Displaying high-level information: If you don’t need to show detailed information, a CDM may be appropriate.
Conceptual models have many practical benefits for your team, including:
- Roadmap and scope development: Stakeholders can use conceptual data models to understand the resource and time requirements associated with a project—viewing this information in the context of business outcome goals.
- Promoting collaboration and communication: By using conceptual data models, your team can connect outside stakeholders to your project and improve understanding with participants in non-technical roles.
- Data modeling and review: From an initial conceptual model, you can build new models that offer increased detail and context.
Logical data model (LDM)
Working from a CDM, a LDM brings in the elements, relationships, and context details necessary for the data structure in order to step closer to implementation. Your team can use a logical data model to visualize data elements and relationships—showing how the data system works. Rather than prioritizing high-level information, the LDM introduces detail. Database analysts and designers can use LDMs in their work as a means of communication.
Nevertheless, the LDM is still not fully prepared for the implementation phase. This diagram provides technical details and perspectives, but is not completely implementation-ready. As a consequence, you might want to think of the LDM as still associated with visualizing higher-level ideas than with a physical data model.
- Reviewing data models through a tech-agnostic lens: Without focusing on a specific technology, you can use a logical data model to consider data elements and relationships.
- Revamping business processes: In order to hone in on specific processes, a logical data model is somewhat more high-level than a physical model (while also being less high-level than a conceptual model).
- Improves existing designs: Using the attributes associated with data elements in the LDM, organizations can strengthen their data models.
- Reveals areas for improvement: LDMs can help you find potential improvements within your business processes and data model design.
- Prevents accidental oversights: Defining your data model through a LDM allows you to define your data elements and avoid inconsistencies. From here, you can create more effectively targeted data models, centering them around specific technologies.
Physical data model (PDM)
Directly depicting data objects and their relationships, physical data models are database-specific and contain the detailed information necessary for the implementation phase. Using a PDM, you can create the scripts you need to develop your database. Metadata and detail are included as part of the physical data model, which is also created around specific technology, location, data storage, and other characteristics.
Using a physical model, implementation of the database itself can begin. PDMs offer the most detail and are specific enough to guide designers and developers.
- Building an implementation model: Prepare for implementation with the features, constraints, triggers, indexes, and other aspects of database structure added to your data model.
- Planning around a specific technology: Once you are certain about the technologies and data elements incorporated into your model, you can create a physical model to help you plan.
- Finalize your visualization: After working on your data model through the conceptual and logical stages, you can use a physical model to finalize any remaining components and details.
- Create consistency: Protect naming convention, quality, semantic, and default consistency across the entire data project.
- Ensure accuracy: Make sure that data objects, data mapping, and information are all documented accurately.
Putting data models to work
Starting with a conceptual model allows you to refine with more detail. Choose any data model type you want from there! Creating a preliminary model with the information you have allows you to get started quickly and begin making decisions about your database.
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