Data models: PDM vs. CDM vs. LDM

When it comes to data models, think of them as a way to see how your database functions. They show the data elements, how they relate to each other, and how the data is used in business processes. There are various types of data models, and each one has its own unique uses and value in specific situations. It’s kind of like how even small savings on canned goods can really add up, especially when you’re stocking up.

What is data modeling?

Data modeling is like creating visual representations, such as diagrams and flowcharts, to show how information systems work. It uses text and symbols to illustrate and communicate the relationships between different data elements. It’s kind of like a blueprint for an organization’s information systems, showing how everything fits together. These models give a unified view of an organization’s data and systems, helping data management teams document and analyze data requirements, find errors, and use the data effectively for their business.

Conceptual vs. logical vs. physical data models

Data modeling is a key part of enterprise data management. It’s not just for database development; it’s valuable in a wide range of use cases. By visualizing data elements and their relationships, teams can use data models to shape business processes and technical designs. When it comes to business information systems, understanding and communicating about data effectively is crucial, and that’s where data modeling comes in.

Now, there are three main types of data models to choose from: conceptual, logical, and physical. Each type has its own unique characteristics, but they all play a vital role in maximizing the value your team gains from data modeling.

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Conceptual data model (CDM)

Conceptual data models (CDMs) are like the big-picture view of data, perfect for sharing info with stakeholders and non-technical folks in your organization. As you develop them further, you can turn them into more detailed models, making them a great starting point for other diagrams.

  • Building a basic data model: A CDM can be used as a starting point, and it can be developed into more intricate models over time.
  • High-level information display: A CDM might be suitable if you don’t need to display detailed information.

For your team, conceptual models offer a variety of useful advantages, such as:

  • Development of a roadmap and scope: Understanding the time and resource requirements of a project in the context of business outcome goals allows stakeholders to make use of conceptual data models.
  • Encouraging cooperation and dialogue: Through the use of conceptual data models, your team can enhance communication with non-technical participants and establish a connection with external stakeholders regarding your project.
  • Review and modeling of data: You can create new models with more context and detail from an initial conceptual model.

Logical data model (LDM)

The logical data model (LDM) sounds like a crucial step in the whole data modeling process! It’s like taking the conceptual model and adding in all the nitty-gritty details to get it ready for implementation. It’s amazing how it helps visualize data elements and relationships, showing how the data system works. And you’re right, it’s all about adding that extra detail that the high-level information doesn’t cover.

Use of logical data models is useful for:

  • Examining data models with a tech-agnostic perspective: You can use a logical model to examine data elements and relationships without focusing on any specific technology.
  • Revamping business process: A logical data model can be used to focus on specific processes. It is higher-level than the physical model, but lower-level than the conceptual model.

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Benefits of LDMs include:

  • Improving existing designs : Using the attributes that are associated with the data elements in the LDM can help organizations strengthen their data models.
  • LDMs reveal areas for improvement: They can help you identify potential improvements in your business processes and model design.
  • Preventing accidental mistakes: By defining your data model using a LDM, you can define your data elements to avoid inconsistencies. You can then create more targeted data models centered around specific technologies.

Physical data model (PDM)

The way you explained physical data models (PDMs) is super informative. It’s like taking all the conceptual and logical groundwork and making it ready for real-world implementation. It’s fascinating how PDMs are so specific to the database and include all the detailed information needed for the actual building phase. It’s like the bridge between the abstract and the concrete, getting everything ready for the real deal.

You can use a physic data model to:

  • Building an implementation model: Prepare your data model for implementation by adding features, constraints and triggers to it. Also, add indexes and other aspects related to database structure.
  • Planning around a specific technilogy: Once you have a clear understanding of the technologies and data elements that will be incorporated into your model you can create a physical version to help with planning.
  • Finalizing your visualization : After you have worked on your data model in the conceptual and logical phases, you can use your physical model to finalize all remaining components and details.

Benefits of using PDMs include

  • Consistency: Protect the naming conventions, quality, semantics, and default consistency throughout the entire data project.
  • Ensure accuracy: Document data objects, data mapping and information accurately.

How to model data

Data modeling is an iterative, collaborative process that involves multiple stakeholders. The basic steps of data modeling are:

1. Gather business needs

Understanding your business requirements is the first step in data modeling. What will your app do, for example, if you are developing it? What data will you require to create it?

2. Define business processes

Once you have defined your business requirements, you can define specific processes within the system. It is important to describe what users can do and how the system will react.

3. Define entity types, attributes and entity types

A collection of objects that are similar within your system is called an entity type (e.g. people, places, things or concepts). Once your business processes and requirements are outlined, you can begin to identify unique entity types in your data.

4. Identify relationships

The next step is to define the relationships among entities. A customer, for example, may place an order. Remember that entities can have a variety of relationships.

  • One-to-one relationships.
  • Many-to-one relationships.
  • Many-to-many relationships.

5. Choose a data modelling technique

Depending on the system and what you want to achieve, there are different ways of modeling data. Hierarchical data model and relational data model are the two main types (with other types of data models within each category). Choose the technique which makes sense for your application. Relational databases, for example, are commonly used in POS and transaction systems.

6. Normalize data

A poor data model will lead you to poor results. Normalizing the data is necessary to ensure the reliability and functionality your data model. Data normalization is an organizing technique that helps you minimize errors, redundancies and anomalies.

7. Validate the data model

Check your work. Validate your model and continue to refine it over time. Iterative data modeling is a process. Continue to refine and adapt as needed.

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