What is Data Modeling? A Comprehensive Guide for Beginners

The statistics show that the volume of data created and consumed worldwide is growing exponentially. Impossible to imagine, this information is described in zettabytes (one zettabyte is a trillion gigabytes) or simply as ‘big data.’ By 2025, we’re forecasted to have around 181 zettabytes of data globally. 

In the face of what we can only describe as a ‘data deluge,’ how can businesses harness such impressive amounts of data to gain meaningful insights and make informed decisions? In this article, we’ll explore the fundamentals of data modeling, its types, and how data engineers can implement it.

What is data modeling?

Data modeling is the process of capturing the business requirements of a system or application and turning them into a database design. That design communicates the relationships between the entities and how they work together.  But to what aim? The objective is simple: to provide a clear and visual representation of data to make it easier to understand. Essentially, the model is like a building blueprint.

Understanding data modeling

Organizations with good, usable data can set clear objectives with benchmarks and baselines. But how can organizations harness data more effectively?

The answer lies in data modeling. Data models range from abstracts, which show basic relationships between data, to implementation plans, which interpret data's flow from creation to consumption. Data modeling organizes information through description, semantics, and constraints. It ensures standardization and allows for actionable results. The process applies formal techniques to get its results. It reduces the amount of time to build and maintain the database. You would never build a home without a blueprint, so why would you create a database without a model?

Since data modeling increases naming consistencies, semantics, and rules, it improves data processes such as analytics.  

Types of data models

There are different data model types, each addressing a specific aspect of information organization. For precision and efficiency, it’s essential to understand the different types. These are:

• Conceptual

• Logical 

• Physical

Each type of data model has a unique role to play in data modeling processes and contributes to an information system’s overall effectiveness.

Conceptual Data Models (CDM)

The whole data modeling process begins with a conceptual data model. This high-level, abstract representation of data defines what a system should contain. However, it doesn’t discuss specifics about implementation.

Conceptual data models help address challenges in data organization. They’re like a bird’s-eye view of the landscape. Stakeholders can use them to understand the overall structure of data and how different entities relate to one another. Think of this as a home blueprint with the basic layout of each floor and adjoining space.

Logical Data Models (LDM)

After abstracting a conceptual model, we move into the logical model. An LDM highlights data relationships and dependencies and shows how different elements connect within the structure, creating a map to aid data organization and manipulation. The relationships between entities are described further by indicating the cardinality as to whether they are one-to-one, one-to-many, or many-to-many.

The logical model helps data analysts establish a framework to understand an application's entities and attributes, whether transactional or analytical. It can also help data analysts and stakeholders provide feedback early in the development cycle, which saves time. 

The logical model is created without a particular database management system or storage technology. This independence is crucial to creating an abstraction that genuinely represents a domain of information. So visualize this as adding where the electrical wiring and plumbing will go throughout the house onto the blueprint.

 Physical Data Models (PDM)

The physical model uses the conceptual and logical models to create a database design. Its primary purpose is to put the data organization strategy into action. In essence, physical data models bring the theoretical into the real world. They serve as the groundwork developers and administrators can use to construct an efficient database system.

A well-designed physical model for a relational database implements the correct data types, primary and foreign key relationships, and constraints (e.g., NOT NULL) to ensure data integrity and fundamental data quality at the database's inception. In addition, it is best practice to add source comments to tables and columns so that other engineers can understand the design details more deeply.

From our home blueprint perspective, this is where electrical outlets are placed with the correct voltage. At this final stage of the home design, details matter.

Recognizing and addressing pain points in data management

Have you ever struggled to find crucial data when you need it? Data disorganization is a significant challenge. The absence of data modeling is a contributing factor. A structured approach through data modeling makes navigating the vast quantities of data much more manageable.

For instance, let’s take a software company building a new telephone billing system. The first version was created without a data model, resulting in challenges associated with data disorganization with missing primary keys, orphaned rows, and many other issues a data model sets to eliminate. The result was no simple refactoring of a few tables, but relatively close to 80% of the schema changed. The changes caused many rework hours for the application and reporting system. This is a testament to taking the time to create a data model first.

Data models aren’t perfect, though. They can be inflexible and complex, making it challenging to adapt them to new requirements. Data modeling is also highly time-consuming, which is another barrier for many stakeholders. Many organizations are positioning themselves at the forefront of advancements using AI to help with their data models. The thought is that AI can make recommendations from common models to help speed up the process.

How to implement data modeling

The data modeling process has several steps. In simple terms, these are:

1. Understanding the requirements

2. The conceptual design

3. The logical design

4. The physical design

5. The implementation

Each step requires stakeholders to work with data modelers to understand the requirements for creating a data model that represents data in a usable way.

Examples of data modeling

Data modeling isn’t an easy concept to get your head around. The following examples may help clarify how data models and data modeling work in reality.

The Entity-Relationship (ER) model

An ER model is like a blueprint for organizing information in a database. There are two main components to this: entities and relationships. In a database for employees, an entity would be a single employee, i.e., a real person. Each ‘entity’ would have different details (attributes) in the database, such as their name, age, and job title. In this scenario, the ‘relationships’ describe how different entities (employees) are connected. For instance, it would describe a relationship between an employee and a manager and whether the relationship is required or optional. 

Several different notations are used for ER models, including Crow’s foot, UML, Bachman, IDEF1X, and others.

Crow's foot notation example

The hierarchical model

The Hierarchical Model organizes information in a family tree-like structure. There’s a root, child nodes, and relationships. If the ‘root’ is the company itself, the child nodes could represent different departments, like sales, marketing, and HR. There might then be further branches that represent teams within departments. For example, within the Sales department, you could have Inside Sales, Outside Sales, and Customer Support branches.

The network model

This model allows for many relationships. If we consider a library with books, authors, and genres, this model will enable authors to be connected to more than one book and genre as well as books to be connected to multiple genres. Essentially, the model allows for complex and overlapping connections. 

The relational model 

The relational model is popular because it arranges data into tables with rows and columns, allowing for easily identifying relationships. Many e-commerce sites use this model to track inventory and purchases.

Best practices in data modeling

Data challenges occur in many organizations, leading to communication breakdowns and general disorganization. Well-executed and accurate data modeling ensures that the data models work well and will continue to do so in the future. Here is a list of best practices in process order:

Stakeholder involvement

Stakeholders collaborate with departments to understand data requirements and needs. Soliciting input from end-users is also vital here. In these initial stages, many business owners communicate their data modeling ideas while looking at the bigger picture. 

An excellent example is my experience working on the data model over twenty years ago to track users' opt-in choices. The product manager (business owner) was involved from day one. As the model started to take shape, there was an opportunity to make it into a broader preferences model to incorporate opt-in/opt-out. Without stakeholder involvement, the model would have been less impactful.

Clearly define entities and relationships

Each entity needs to represent a distinct aspect of the business. Relationships between entities should describe each side of the relationship for clarity.

Ensure consistency

The process needs to be robust to ensure standardized naming conventions are enforced. Clear documentation should be made available as a reference for the data model's structure, definitions, and rules. For instance, when integrating with operational systems like an order entry system, adherence to these practices becomes paramount to maintaining data efficiency. Look for a modeling tool that can provide standardization checks.

Beyond documenting database standards, implement checks in your CI/CD pipelines. There is no better way to catch any non-conformity issues. The person who did the code check-in will get a notification of a build failure and what the non-conformant item is. Having these checks in place ultimately ensures consistency.

Use normalization

Normalization is the process of creating data integrity and consistency by having the proper structure to avoid errors such as duplicate data. This includes understanding the natural primary key and developing the proper constraints to guarantee uniqueness.

Complete the model and validate it.

A model is only valuable if it is maintained. To maintain accuracy, regularly update the data models to accommodate new elements or modifications. Version control can help with this. 

Consider data collection methods

Incorporating effective data collection methods is essential to ensure the accuracy and relevance of the data being modeled. Having sample data to review helps to understand if the model meets the stakeholders' requirements.

Why is a data catalog important for data modeling?

A data catalog supports data modeling in several key ways:

1. Centralized View of All Data Assets: A data intelligence platform serves as a central hub for metadata, offering a comprehensive view of all data assets. This aids in better data model design and maintenance.

2. Data Discovery and Understanding: With a data intelligence platform, users can easily discover and understand data assets, seeing detailed information about data sources, types, relationships, and business definitions.

3. Collaboration and Knowledge Sharing: Catalog features, like trust flags, comments, and common queries promote collaboration, leading to more accurate and efficient data models.

4. Data Quality and Governance: Cataloging data assets and quality metrics ensures more high-quality, governed data is used in modeling, improving reliability.

5. Impact Analysis and Lineage Tracking: Tools for impact analysis and lineage tracking help modelers understand the effects of changes to data sources or transformations.

6. Automated Data Profiling and Classification: Automated profiling and classification help users quickly understand data characteristics, patterns, outliers, and anomalies – critical details for data models. 

7. Compliance and Security: Visibility into sensitive data, access controls, and usage patterns ensures models adhere to regulatory requirements and policies.

8. Integration with Modeling Tools: Integration with modeling tools provides seamless access to cataloged data assets, streamlining workflows and ensuring consistency.

By leveraging these capabilities, data catalogs can enhance the efficiency, accuracy, and governance of data modeling, leading to better decision-making and business outcomes.

Final thoughts on data modeling

Data modeling is central to organized information systems. This process provides a structure that helps us to understand, interpret, and use vast amounts of data. From blueprints to implementations, the processes are all as crucial as one another.

Good data modeling practices empower businesses to navigate the data soup, resulting in streamlined vision and clarity.

These practices become imperative as organizations grapple with increasing amounts of complex data. Through data modeling, businesses access insights that add value to their work. Future-proofing is required here too. Artificial Intelligence (AI) is becoming transformative in data management. Wherever data volumes lead us, it’s clear that industries driven by data need to commit to staying ahead.

Curious to see how Alation can help your organization build better data models, faster? Book a demo with us today.