Data modeling is the process of creating a visual representation of databases and information systems. They can be made to represent part or all of a database with the goal of simplifying access to and understanding the types of data within the system as well as the relationship between the various data points and groups.
For companies, individual data models are built around the specific needs and requirements of the organization, and they can be visualized on various levels of abstraction depending on the information that needs to be extracted for the dataset. This type of work is often done by a team of data engineers, data analysts, and data architects, along with database administrators who are familiar with both the original database and the organization’s needs.
Before implementing a data modeling framework into your company’s information systems, it’s important to first understand what makes a database useful and usable for information extraction and how it can help you map out the connections and workflows needed at the database level.
This article can help you gain a thorough and wide-scale understanding of how data modeling works, what its various types are, and how it can benefit your business.
Table of Contents
- 3 Types of Data Modeling Categories
- 4 types of Data Modeling Infrastructure
- How Data Modeling Works
- What are the Features of Data Modeling
- 5 Benefits of Data Modeling
- Top 4 Data Modeling Tools
3 Types of Data Modeling Categories
There are different types of data modeling techniques that can be divided into three main categories: conceptual, logical, and physical. Each type serves a specific purpose depending on the format of data used, how it’s stored, and the level of abstraction needed between various data points.
Conceptual Data Model
Conceptual data models, also referred to as conceptual schemas, are high-level abstraction forms of representing data, but they’re also the most simple. This approach doesn’t go in-depth into the relationship between the various data points, simply offering a generalized layout of all of the most prominent data structures.
Thanks to their simple nature, conceptual data models are often used in the first stages of a project. They also don’t require a high level of expertise and knowledge in databases to understand, making them the perfect option to use in shareholder meetings.
Key Differentiators
High-abstraction conceptual data models are used to showcase what data is in the system. Generally, they include surface-level information about the data such as classes, characteristics, relationships, and constraints. They’re suitable for gaining an understanding of a project’s scope and defining its basic concepts.
Pros
- Starting point for future models.
- Defines the scope of the project.
- Includes shareholders in the early design process.
- Offers a broad view of the information system.
Cons
- Low returns on time and effort.
- Lacks deep understanding and nuance.
- Not suited for larger systems and applications.
- Insufficient for the later stages of a project.
Examples
There are countless applications of conceptual data modeling outside of the need for developing or improving an information system. It can be used to showcase the relations between different systems or steps ofr a process.
For an order management system, an abstract diagram can help present the relationship between the various operations that go on when a customer places an order. It can also draw a clear relationship between the storefront — digital or physical — and the invoicing system, order fulfillment department, and order delivery.
Logical Data Model
Logical data models, also referred to as logical schemas, are an expansion on the basic framework laid out in conceptual models, but it considers more relational factors. It includes some basic annotations regarding the overall properties or data attributes, but it still lacks an in-depth focus on actual units of data.
Key Differentiators
This model is particularly useful in data warehousing plans, as it’s completely independent of the physical infrastructure and can be used as a blueprint for used data in the system. It allows for a visual understanding of the relationship between data points and systems without being too invested in the physicality of the system.
Pros
- Performs feature impact analysis.
- Easy to access and maintain model documentation.
- Speeds up the information system development process.
- Components can be recycled and readapted according to feedback.
Cons
- The structure is difficult to modify.
- Lack of in-depth details of data point relations.
- Errors are difficult to spot.
- Time- and energy-consuming, especially for larger databases.
Examples
Logical data modeling is more suitable for databases with a number of complex components and relationships that would need mapping. For instance, using logical modeling to map an entire supply chain, you can have easy access to not only the attribute names but also the type of data and its indicators for mandatory and non-nullable columns.
This approach to data representation is considered database-agnostic, as the data types are still abstract in the final presentation.
Physical Data Model
Physical data models, also referred to as physical schemas, are a visual representation of data design as it’s meant to be implemented in the final version of the database management system. They’re also the most detailed of all data modeling types and are usually reserved for the final steps before database creation.
Key Differentiators
Physical data models conceptualize enough detail about data points and their relationships to create a schema or a final actionable blueprint with all the needed instructions for the database built. They represent all rational data objects and their relationships, offering a high-detail and system-specific understanding of data properties and rules.
Pros
- Reduces incomplete and faulty system implementations.
- High-resolution representation of the database’s structure.
- Direct translation of model into database design.
- Facilitates detection of errors.
Cons
- Requires advanced technical skills to comprehend.
- Complex to design and structure.
- Inflexible to last-minute changes.
Examples
Physical data modeling is best used as a roadmap that guides the development of a system or application. By being a visual representation of all contents of a database and their relations, it enables database administrators and developers to estimate the size of the system’s database and provide capacity accordingly.
4 Types of Data Model Infrastructure
In addition to the three primary types of data modeling, you can choose between several different design and infrastructure types for the visualization process. Choosing the infrastructure would determine how the data is visualized and portrayed in the final mapping. For that, there are four types you can pick from.
Hierarchical Data Model
Hierarchical data models are structured in a way that resembles a family tree, where the data is organized in parent-child relationships. This type allows you to differentiate between records with a shared origin, in which each record can be identified by a unique key belonging to it, determined by its place in the tree structure.
Key Differentiators
Hierarchical data modeling is most known for its tree-like structure. Data is stored as records and connected through identifiable links that represent how they influence and relate to one another.
Pros
- Simple and easy to understand.
- Readable by most programming languages.
- Information can be removed and added.
- Fast and easy to deploy.
Cons
- Structural dependence.
- Can be bloated with duplicate data.
- Slow to search and retrieve specific data points.
- Cannot describe relations more complex than direct parent-child links.
Examples
Hierarchical data modeling is best used with easily-categorized data that can be split into parent-child relations.
One example where this is highly beneficial is for the fulfillment of sales, in which numerous items exist under the same name but can be differentiated by associating with one sale order at a time. In this scenario, the sale order is the parent entity, and the items are the child.
Relational Data Model
Unlike hierarchical data models, relational data models aren’t restricted to the parent-child relationship model. Data points, systems, and tables can be connected to each other in a variety of manners. This type is ideal for storing data that needs to be retrieved quickly and easily with minimal computing power.
Key Differentiators
Relational data models can be differentiated by checking whether they follow ACID characteristics, which are atomicity, consistency, isolation, and durability.
Pros
- Simplicity and ease of use.
- Maintains data integrity.
- Supports simultaneous multi-user access.
- Highly secure and password-protected.
Cons
- Expensive to set up and maintain.
- Performance issue with larger databases.
- Rapid growth that’s hard to manage.
- Requires a lot of physical memory.
Examples
Relational data models are best suited for use with serial information that’s related but can be beneficial separately.
One example is maintaining a database of members, customers, or users of an establishment. The structure of rows and columns can be used to store the first and last names, birth dates, Social Security numbers, and contact information that are grouped within one another as relating to a single individual.
Entity-Relationship (ER) Data Model
Entity-relationship data models, also referred to as entity relationship diagrams (ERDs), are a visual way of representing data that relies on graphics depicting the relationship between data points, usually people, real-world objects, places, and events, in the information system.
This type is most commonly used to better understand and analyze systems in order to capture the requirements of a problem domain or system.
Key Differentiators
ER data models are best used to develop the base design of a database as it delves into the basic concepts and details required for implementation, all using a visual representation of the data and relationships.
Pros
- Simple and easy to understand.
- Compatibility with database management systems (DBMSs).
- More in-depth than conceptual modeling.
Cons
- Difficult to expand and upscale.
- Retains some ambiguity.
- Only works best for relational databases.
- Long-winded and wordy.
Examples
ER diagrams represent how databases are related as well as the flow of processes from one part of the system to the next. The overall representation resembles a flowchart but with added special symbols to better explain the various relations and operations occurring in the system.
One prominent example of ER models is used with public institutions like universities to help them better categorize and parse their demographic of students. ER diagrams showcase student names and connect them with their taken courses, mode of transportation, and occupation.
Object Oriented Data Model
Object oriented data models are a variation on conceptual data modeling that instead uses objects to make complicated real-world data points more legible by grouping entities into class hierarchies. Similarly to conceptual modeling, they’re most often used in the early stages of developing a system, especially data-heavy multimedia technologies.
Key Differentiators
Instead of focusing solely on the relationship between data points and objects, object-oriented data modeling centers the data of the real-world object, clustering them along with all related data, such as all personal information and contact information of an individual.
Pros
- Easy to store and retrieve data.
- Integrates with object-oriented programming languages.
- Improved flexibility and reliability.
- Requires minimal maintenance efforts.
Cons
- Lacks a universal data model.
- Highly complex.
- Higher chances of performance issues.
- Lack of adequate security mechanisms.
Examples
Object-oriented data models allow businesses to store customer data by separating individual attributes into various tables but without losing the links between them.
An object in the data model represents the type of customer, which can then be followed in either direction to collect the remainder of the customer’s information without having to involve unnecessary parts of the database.
How Data Modeling Works
Data modeling is the process of visualizing the relationship between and the locations of various data points by a data modeler — usually a database administrator or data architect that works in close proximity to the data. The first and most important step of data modeling is determining the right type for the applications.
Depending on whether you’re using conceptual, logical, or physical data modeling, the resulting diagram could carry varying degrees of simplicity, detail, and abstraction. Identifying user access patterns can also help to determine the most critical parts of the database to represent in order to adhere to your business’s needs.
Before concluding the data modeling process, it’s important to run a handful of test queries to identify the validity of the data model.
What are the Features of Data Modeling
When it comes to searching for a suitable data modeling tool or picking out the appropriate data modeling approach, there are functionalities and capabilities you should expect. The following are some of the key features of any approach to data modeling.
Data entities and their attributes
Entities are abstractions of real pieces of data. Attributes are the properties that characterize those entities. You can use them to find similarities and make connections across entities, which are known as relationships.
Unified modeling language (UML)
UML are the building blocks and best practices for data modeling. They’re a standard modeling language that help data professionals visualize and construct appropriate model structures for their data needs.
Normalization through unique keys
When building out relationships within a large dataset, you’ll find that several units of data need to be repeated to illustrate all necessary relationships. Normalization is the technique that eliminates repetition by assigning unique keys or numerical values to different groups of data entities.
With this labeling approach, you’ll be able to normalize, or list only keys, instead of repeating data entries in the model every time entities form a new relationship.
5 Benefits of Data Modeling
Data modeling offers several distinct benefits to enterprises as part of their data management.
Improves data quality
Data modeling allows you the opportunity to clean, organize, and structure data beforehand. This enables you to identify duplicates in data and set up monitoring to ensure its long-term quality.
Saves time and energy
Despite being an added step that may need to be repeated multiple times throughout the project’s development process, modeling a database before work begins sets up the scope and expectations for the project.
Clear-cut data modeling ensures you don’t end up spending more time and resources on a step than is necessary and justified by the data itself.
The inclusion of nontechnical departments
The early stages of a project’s development are oftentimes too abstract for individuals with little to no technical experience to fully understand.
The visual nature of data modeling, especially conceptual data modeling, allows for more collaboration and discussions among shareholders and nontechnical departments such as marketing and customer experience.
Promotes compliance with regulations
Privacy and security regulations need to be included from the earliest stages of a system’s development. Data modeling enables developers to fit all of the necessary parts for compliance into the design’s infrastructure.
By understanding how data points relate and interact with one another, you can better set the bar for secure and safe data governance.
Improves project documentation
Documentation is essential to encapsulate the development process of a system and helps with solving any future problems or inconsistencies that may arise as well as with training future employees. By building an in-depth data model early on in the development process, you’ll be able to include that into the system’s documentation to allow for a deeper understanding of how it works.
Top 4 Data Modeling Tools
Data modeling has become a pillar of the growing data governance market, particularly because of the streamlined data visibility data models allow enterprises to provide to non-data professionals within their organizations.
The data governance market is expected to grow at a compound annual growth rate of over 21% between 2021 and 2026, with an estimated value of $5.28 billion by 2026, according to a study by ReportLinker. Much of this growth will be attributed to increasing global data regulations, most notably the General Data Protection Regulation (GDPR) in the EU.
This highly lucrative market has been the driving factor of countless tech services providers creating their own data modeling tools — some open source and free to use.
Enterprise Architect
Enterprise Architect is a graphical tool designed for multi-user access, suitable for both beginner and advanced data modelers. Through a number of built-in capabilities ranging from data visualization, testing, and maintenance to documentation and reporting, it can be used to visually represent all of the data in your system’s landscape.
Apache Spark
Apache Spark is an open-source processing system for large data management and modeling. It can be used completely free of charge with no licensing costs, providing users an interface for programming clusters with implicit fault tolerance and parallelism.
Oracle SQL Developer Data Modeler
The Oracle SQL Developer Data Modeler is part of the Oracle environment. While not open source, it’s free to use for developing data models and creating, browsing, and editing conceptual, logical, and physical data models.
RapidMiner
RapidMiner is an enterprise-grade data science platform and tool that can be used to collect, analyze, and visually represent data. It’s perfect for beginner and less-experienced users with a user-friendly interface.
It integrates seamlessly with a wide variety of data source types, ranging from Access, Teradata, and Excel to Ingres, MySQL, and IBM DB2 to name a few. Furthermore, it’s capable of supporting detailed data analytics across a broad artificial intelligence (AI) life cycle.
Bottom Line: Data Modeling
Data modeling is an approach to visually representing data in graphs and diagrams that vary in abstraction, level of detail, and complexity. There are multiple types and approaches to data modeling, but its primary benefit is to help conceptualize and lead the development of a database-reliant system.
From free, open-source tools to enterprise-ready solutions and platforms, you can automate and simplify the bulk of the data modeling process, making it more accessible to smaller teams and urgent projects on a limited budget.
