Learn about data modeling to improve your business

Collecting large amounts of data supports organizational goals and helps businesses of all kinds to improve their competitive positioning.

But before you can put that data to work for your company, you must first learn how to visualize the various ways it’s connected. Doing so allows you to organize, store, and access it better. To really bring your data to life, you’ll need to understand data modeling.

In this article, we’ll explain the key concepts and motivations behind data modeling to help you use your data more effectively. Specifically, we’ll cover the following:

• What is data modeling?
• Types of data models
• Data modeling examples
• Data modeling process
• Benefits of data modeling

What is data modeling?

Data modeling is a way of visualizing how information is connected across a system of software through the use of representative symbols and text. The process creates a roadmap for revamping existing software solutions or designing a new, more efficient database, showing how data is used and stored within a system.

Once you understand how data flows throughout your database, you can identify potential bottlenecks and make improvements to your information management strategies. Optimizing data usage and storage pathways will lead to more accurate analytics and provide insights that your organization can take advantage of to promote growth.

Purpose of data modeling

The main purpose of data modeling is to facilitate better, more efficient data management. Your business undoubtedly devotes significant time and resources to its data collection efforts, but these investments won’t yield strong returns unless you can effectively manage, use, and store your data. That’s the premise behind data modeling.

By creating a more robust, living data model, your business can pinpoint the information it needs for various processes and departments. As your organization evolves, so too should your data model and information management practices. Failing to do so will lead to a lack of visibility, the development of data silos, and overall stagnation.

Types of data models

Data modelers can use three different types of models to visualize their business workflows. Each data model provides unique insights into the flow of information throughout an organization. Data modelers will often use a combination of all three models rather than limiting themselves to a single framework.

Let’s take a closer look at the three types of data models.

1. Conceptual data model

Conceptual data models represent high-level visualizations of an organization’s data. These are almost always created before the other model types and are essential during the early stages of a project. The conceptual data model focuses on the entities that will be represented, their characteristics, and the relationships that exist between them.

Conceptual models differ from the other two types of models in that they are not linked to a particular technology or database. These models are created at the beginning of a project or in the early stages of organizing data by stakeholders and executives to lay out the big picture. As such, they provide a broad overview of the connections between departments or the business itself and its vendors.

2. Logical data model

A logical data model is usually created after the conceptual model is presented to stakeholders. It’s less abstract and provides greater detail about an organization’s entities and the flow of information across the entire business.

The logical data model doesn’t specify any technical system requirements. But it will define important data structures and data types, allowing organizational members that are more technologically savvy to use data models to understand the requirements of database designs.

3. Physical data model

A physical data model provides a schema for how data will be physically stored within a database. It can and often does include system-specific requirements and properties, making it the most detailed of the three models.

A physical data model is specific to the application software or database management solution that an organization intends to implement. Some of the software elements defined in a physical data model include:

• Fields
• Columns
• Indexes
• Triggers
• Constraints

Due to its complexity, a physical data model is the most time-consuming and labor-intensive to create, but it will lay the foundation for the development of a new database or the reimagining of an existing solution.

Data modeling examples

Now let’s shift our focus to some examples of data modeling methods. These are five of the most commonly used methods of data modeling:

Relational data modeling

Relational data modeling is a technique that IBM developed in the 1970s, and it maps the relationship between data elements stored in different tables. The relational data modeling method is popular among many modelers because it doesn’t require a deep understanding of physical information storage.

Entity-relationship data modeling

Entity-relationship (ER) data modeling is a more complex variant of the relational data model, mapping entities and identifying specific attributes as they relate to these entities. An entity could be customer data, employee data, product information, or invoice, while an attribute might be a customer email address, employee last name, product price, or date that an invoice was created.

Hierarchical data modeling

Hierarchical data models typically feature a tree-like visualization of parent and child relationships. IBM’s Information Management System (IMS) is the most well-known example of a hierarchical data model and is still used by many businesses, though relational data models have otherwise largely replaced hierarchical ones. Extensible Markup Language (XML, as it’s more commonly known) is an alternative to IMS that is also still in use.

Dimensional data modeling

Dimensional data modeling is primarily used in data marts and warehouses for business intelligence (BI) applications. The technique is designed to optimize data retrieval speeds so organizations can efficiently tap into the information stored within their data warehouses. A dimensional data model consists of fact tables, each of which contains information about events or transactions, such as product purchases.

Object-oriented data modeling

Object-oriented data modeling is similar to ER modeling, but in this framework entities are abstracted into objects. Objects that share similar attributes can be moved into classes and grouped hierarchically. Object-oriented databases can incorporate tables, but they also support more complex data relationships. Many hypertext and multimedia databases use object-oriented modeling.

Data modeling process

Data modeling is a fluid process that can be shaped to the needs of your organization. However, most teams start by creating a conceptual model, followed by a logical model, and finally a physical data model. This enables your organization to progress from a high-level visualization of your data to a more technical representation of the database itself.

Following the established data modeling process will support stakeholders as they meet to discuss the company’s data processing and storage needs in extreme detail. During these discussions, decision-makers will generally progress through a workflow that goes as follows:

1. Identify the entities. Organizational leaders will identify and list the various entities and departments that exist within the business.
2. Identify any key properties for each entity. Decision-makers will identify attributes that differentiate each of the company’s entities.
3. Identify entity relationships. Business leaders will develop a model that outlines interconnected entities and also shows how they relate to each other visually.
4. Identify the data attributes that need to be in the model. Company leaders will list the specific attributes that need to be included in the model, such as customer email addresses and customer names.
5. Map the attributes. Once data attributes are created, they will then be mapped to each entity.
6. Finalize and validate its accuracy. Before finalizing the data model, decision-makers will validate its accuracy and ensure that all relevant entities and attributes are included in the initial framework.

By following this reproducible process, organizational leaders can effectively develop increasingly technical data models and clearly identify the information needs of the business.

Benefits of data modeling

When you empower your team members to apply their skills in data modeling by investing in the right technologies, you can more efficiently organize the information your company collects. In turn, you’ll get the most out of your data and leverage it to support critical business processes.

You can also achieve other benefits, such as:

• Data consistency. Data modeling tears down silos and ensures information consistency across all departments, systems, and databases.
• Improved data quality. When your team can easily share and access information, overall data quality and usability increase significantly.
• More efficient database design. Data modeling reveals potential information logjams, meaning you can eliminate these bottlenecks and create a more efficient data management system.
• Reduced errors and redundancies. Through data modeling, you can optimize workflows, reduce the need for redundant manual input, and decrease the likelihood of costly data entry errors.
• Improved transparency throughout the company. By using data modeling, you can improve information transparency across the entire company and provide decision-makers with the information they need to capitalize on emerging opportunities.

When you optimize the visibility, accessibility, and usability of information through data modeling, the possibilities for your business expand exponentially.

Evaluate a platform to actualize your data model

Having a visual representation of how your data is connected will help you organize your data so you can meet your company’s need for information. However, before you can start designing your data model, you must first implement a robust data collection and storage platform.

And until someone invents mind reading, there’s Adobe Real-Time Customer Data Platform. Real-Time CDP collects B2C and B2B data from across systems and immediately unifies it into profiles ready for activation across any channel.

To learn more about Adobe Real-Time CDP, watch the overview video or take an interactive tour.