Data Modeling: What It Is and Why It’s Essential

Data modeling involves creating a visual representation of the different types of data an organization collects and the relationship between those data objects.

A data modeling process can help you achieve several goals.

First, you can identify missing and redundant data. As a result, you can improve efficiency by making the necessary adjustments to your data.

Second, data modeling allows you to define how a database should be structured, including the conceptual, logical, and physical layers.

Third, data modeling eliminates confusion by providing a clear picture of organizational data needs. Developers or data experts will know how to get started, which saves significant time and effort.

This article will help you understand the process of data modeling and its importance and provide different data modeling techniques and examples.

What is data modeling?

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Think of data modeling as a process that allows you to define the type of data you’ll collect and consume, including the association between different data types, relational rules, and how data will flow or move across various systems.

Data modeling enables you to understand the data, performance, and technical requirements needed by specific business processes. The data modeling process usually includes flowcharts, symbols, and diagrams for data representation.

A data model determines the data structure and attributes required by a particular organization. It also shows the connection between data elements, which supports the development of effective information systems.

The three different types of data models are conceptual, logical, and physical. Each of these data models has a specific purpose, discussed further in the following sections.

Conceptual data model

A conceptual data model provides an overview of data structure and organizational data needs and is usually developed in the first stage of the data modeling process.

Conceptual data models show the relationship between business entities using Object-Role Models (ORM) and Entity Relationship Diagrams (ERD). ORM and ERD diagrams outline how different objects or entities are connected.

A conceptual data model can display entities that an organization is currently working on as well as those required for future improvements. This makes the model a crucial source of information that can act as a basis for future reference.

Data architects and other relevant business stakeholders use conceptual data models to understand the required entities and data objects and how these concepts interrelate. This is why it’s crucial to have a conceptual model that’s easy to interpret—even by an audience with less technical knowledge.

Failure to develop a conceptual data model can cause you to miss crucial insights, especially about entity relationships.

Database objects can relate to one another in the following ways.

• Many-to-many. In this relationship, one or more database records relate to items in another table. To illustrate, a complaints table can have issues raised by multiple clients in the customer table.
• One-to-many. This relationship means that a parent node is connected to multiple child nodes. For example, a customer representative can deal with several complaints or several customers.
• One-to-one. An object in a table can relate to only one object in another table. For instance, a customer can be assigned to only one customer representative.

Logical data model

Once you understand your project scope, data, and entity relationships, you can move on to the logical model.

Logical data models provide more information about data structure and entity relationships. These models also highlight primary keys and foreign keys that define the relationships between different data types. A primary key is a unique attribute for identifying and retrieving specific records. On the other hand, foreign keys help connect two tables or entities.

A logical model also shows the data attributes of a particular object. For instance, if the model is about an employee, the name can be a string (a list of characters); the age is an integer (whole number); and the payroll number can also be an integer.

Skipping the logical data modeling phase can cause you to miss certain business requirements, ending up with a poor database design.

Physical data model

Physical data models offer a final design or schema of how data is stored in a database. It includes database-specific concepts such as procedures, indices, constraints, views, columns, data types, and tables.

Database management systems (DBMS) such as MySQL, Oracle Database, and MongoDB usually have different rules. Therefore, system and database administrators must consider such business rules when implementing physical data models.

For example, they must follow the naming convention for rows and columns defined by the DBMS.

The importance of data modeling

Data modeling has numerous benefits, including the following:

• Accurate data representation. Data modeling allows developers, system designers, database administrators, and other professionals to have a clear picture of the business needs. Conceptual and logical data models use flowcharts to show the required data objects and their connection to business processes. These data models also determine the actual values that the database will store. For instance, a customer data model can have strings, integers, and Boolean values. Therefore, you can say that a data model acts as a roadmap that enables developers to accurately represent all the required data objects in a database.
• Relationship between database objects. Flowcharts in the conceptual and logical data models allow you to understand the relationship between different data objects. These relationships simplify complex business processes and eliminate confusion during development.
• Control of data redundancy. In this context, redundancy involves having several copies of the same data in a database. Apart from potentially affecting data quality, duplicate data also consumes extra storage space. Data modeling enables you to perform tests and set rules on how to deal with data redundancies. For example, you can remove a particular table in your database if there’s another with similar data.
• Security. With data modeling, you can identify security issues likely to affect the application before you push it to production. Resolving these issues allows you to end up with a more secure application. Modeling can also assist you in fulfilling different data integrity requirements.
• Faster software builds. Conceptual, logical, and physical data models guide developers on what needs to be implemented at different application stages. As a result, these models eliminate confusion and allow developers to dedicate more time and focus on the actual code implementation, leading to faster software builds.
• Reduced cost. Data modeling can help you save significant time and costs. For example, the planning or conceptual stage allows you to discover problems that could have otherwise affected your system during production. Identifying and handling these issues at an early stage is less frustrating and less cost-intensive than discovering them farther into the process. You can also use data modeling tools, such as Erwin, to scan for errors and automate different data processes and structures, which improves data quality.
• Improved performance. Data modeling can make your application run more efficiently. Data models serve as a high-level plan, enabling developers to know which type of data the application will need and how to consume or store it. Such requirements can help meet the needs of data-intensive fields like machine learning, business intelligence, artificial intelligence, and big data.
• Better documentation. Data modeling allows developers to document their data structures, relationships, and other important business requirements. Developers and other stakeholders can use these documents for future reference.
• High-quality applications. A huge advantage of data modeling is that it facilitates the creation of high-quality applications, which are easy to maintain. Such applications follow a detailed plan and structure, making them less likely to crash. Such apps are also highly scalable.
• Quality decision-making. A data model helps break down complex data architecture into simple terms that are more understandable to business stakeholders outside the tech bubble. Business analysts can use this information to ensure that all data requirements are fulfilled.
• Data analytics and visualization. Data modeling can help you transform large amounts of data into valuable information for decision-making, business intelligence, and other uses.

Techniques and examples

Over the years, data modeling techniques have evolved to address constantly changing data storage needs.

Here are some common data modeling techniques.

Hierarchical model

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A hierarchical data model allows developers to store data in a parent-child or tree-like structure. In this model, a child is only connected to one root node, while the parent may have many child nodes.

The hierarchical data model is used when collecting data that is structured in a particular order, such as gadgets in an electronic shop. In this example, the electronic shop acts as the parent node and gadgets like CD and MP3 players are child nodes.

Here are some of the advantages of hierarchical data models.

• The hierarchical data model is simple to use. The one-to-many relationship allows developers to traverse through the database quickly.
• Physical storage structures and database tables are also separate in a hierarchical model, making it easy to add, modify, or delete records.
• Many programming languages support the tree-like database structure.

One major drawback of the hierarchical data model is its inflexibility. This model accepts only one-to-many relationships. It does not account for a situation where a child may have multiple parent nodes.

Relational model

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The relational model is the most popular data modeling technique. It allows information or data to be structured in columns, rows, and tables, making it easier to understand how different data structures relate.

Relational data models enable developers to establish relationships or links between information by joining tables using a foreign key.

Tables in a relational database usually have a unique attribute on each row, commonly known as the primary key. Each row in a table can be identified using the primary key. This feature makes searching and extracting particular records from a database simple.

The above image illustrates two tables (teachers and classes) in a relational database. The unique attribute or primary key in the teachers’ table is teacherID, which has also been used as a foreign key in the classes table.

Some examples of relational database management systems include MySQL, MariaDB, Oracle Database, PostgreSQL, and Microsoft SQL Server.

Relational databases usually utilize a query language, such as SQL, for data management. Some benefits of relational database models include the following:

• Security. Relational databases use a role-based security technique that limits data access to specific users.
• Flexibility. You can easily create, read, update, and delete records without impacting the entire database structure.
• Normalization. Relational data models use a normalization technique to identify and eliminate data redundancies.

The disadvantage of relational databases is that they may become difficult to maintain in the long run, especially as the amount of data increases.

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Network model

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The hierarchical database model confines child nodes to only one parent, which is considered a major flaw. In other words, it utilizes only the one-to-many relationship. This method is largely obsolete today.

The network model was introduced as an improvement on the hierarchical model. It allows child nodes to belong to several parent nodes, which translates to a many-to-many relationship. An illustration of the network model is shown in the figure above.

The advantage of the network model is that it's quite flexible. You can easily search and retrieve information due to the many-to-many relationship.

Despite improvements, the network model has the following shortcomings.

• The network model is quite challenging to implement, especially for beginners.
• Modifying data in a network model is difficult since slight alterations can affect the entire database structure.

Object-oriented model

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An object-oriented model helps you work with complex data types in object-oriented programming languages like C#, Swift, Kotlin, and JavaScript.

In the above image, the sales report and sales activity are objects whose properties can be easily stored in an object-oriented database, such as Realm.

Object-oriented models have the following advantages:

• Object-oriented databases allow you to run fast queries on complex data. This feature can translate into a high-performing application.
• Developers can easily retrieve data from a database since it resembles familiar programming objects.

The disadvantage of object-oriented database models is that they’re usually tailored to a specific programming language. For instance, a database that works with JavaScript may not be compatible with another language. Developers may have a harder time configuring the database to fit their needs.

Entity-relationship model

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An entity relationship (ER) model is a high-level model that outlines data structures and the relationship between different data objects.

ER models are based on two main concepts: data entities or tables that store information and relationships that determine the connection between entities.

In the figure above, student and course are entities that hold data about the student and course, respectively. The course entity is connected to the student table because it stores academic grades. You can identify specific records using a primary key. In this example, the Student_ID and Course_ID serve as the primary keys.

The advantages of ER models include the following:

• The physical implementation of ER database models is quite straightforward. Developers can use ER diagrams to identify the required entities and how they relate to one another.
• ER models can also be converted to hierarchical and network models, allowing system designers to target multiple systems.
• ER models are compatible with relational databases, which increases their functionality.

However, ER data models lack an industry standard notation. Therefore, confusion may arise due to a lack of consistency.

Work with data modeling experts

When done right, data modeling can bring numerous benefits to your development environment. Therefore, it’s important to evaluate the different data sets and structures your business processes need before creating an application. You should also follow conceptual, logical, and physical data models to ensure that you don’t miss any steps.

If you need help or are just looking for inspiration, Upwork can connect you to hundreds of data modeling experts. Get started by finding and hiring data modeling professionals on Upwork today.

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