Databases help run applications and provide virtually any kind of insight a business may need. But what makes a database useful and usable? How can you be sure you’re creating a database that will meet all of your needs? Consider data modeling as the solution between procuring data and turning it into an actionable database.
Data modeling is the practice of cleaning and organizing data into a visual representation, or a plan that helps you map out the connections and workflows needed at the database level. Regardless of their exact contents, data models act as a blueprint for building an optimized database.
The practice is completed by a data modeler who works directly with data entities and attributes to find their relationships and create an appropriate model. Data architects also work on data models, focusing on physical blueprint development.
See below to learn more about data modeling, how it’s used, and what tools exist to help companies through the data modeling process:
A Summary of Data Modeling
- Types of data models
- Data model infrastructure
- Data modeling features
- Benefits of data modeling
- Data modeling use cases
- Data modeling market
See more: What is Data Analysis?
The three primary types of data models are conceptual, logical, and physical. Think of them as a progression from an abstract layout to a detailed mapping of the database setup and final form:
Conceptual data model
Conceptual data models are the most simple and abstract. Little annotation or data use occurs in this model, but the overall layout and rules of the data relationships are set. You’ll find elements like basic business rules that need to be applied, the categories or entity classes of data that you plan to include, and any other regulations that may limit layout options. Conceptual data models are frequently used in the discovery stage of a project.
Logical data model
The logical data model expands on the basic framework laid out in the conceptual model, but it considers more relational factors. You’ll see some basic annotation related to overall properties, or data attributes, but not many annotations that focus on actual units of data. This model is particularly useful in data warehousing plans.
Physical data model
Since the physical data model is the most detailed and usually the final step before database creation, it often accounts for database management system-specific properties and rules. You’ll illustrate enough detail about data points and their relationships to create a schema or a final actionable blueprint with all needed instructions for the database build.
Beyond the three main types of data modeling, organizations can choose from several different design and infrastructure methods for visualizing their data model:
Hierarchical data model
Hierarchical data models resemble a family tree layout. Your data entities look like “parents” or “children” and branch off from other data that shares a relationship with them.
Relational data model
This model is similar to the hierarchical data model, but instead of parent-child relationships, it maps out the connections among various tables of data.
Entity-relationship (ER) data model
The ER data model showcases data entities and creates a diagram to show how they connect to each other. This model is often used with the relational model to understand how your data should connect in a database.
Object-oriented data model
This design method makes complicated real-world data points more legible by grouping entities into class hierarchies. You’ll often find object-oriented design in the early development stages of multimedia technologies.
These 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’ll use them to find similarities and make connections across entities, which are known as relationships.
- Unified modeling language (UML): Think of UML as a set of building blocks and best practices for data modeling. UML is a standard modeling language that helps data professionals visualize and construct appropriate model structures for their data needs.
- Normalization through unique keys: When building out relationships within a large set of data, 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.
Data modeling offers several distinct benefits to enterprises as part of their data management:
- Before you even create a database, you’ve cleaned, organized, and modeled your data to plan what your next step should look like. Data modeling improves data quality and makes databases less prone to errors and poor design.
- Data modeling creates a visual flow of data and how you plan to organize it. This helps employees understand what’s happening with data and how they fit into the data management puzzle. It also improves data-related communication across departments in an organization.
- Data modeling enables smarter database design, which can bring forth better applications and data-based business insights down the line.
Here’s how a couple of different users have applied their data modeling tools:
“I am extremely impressed with the ease with which we can move from conceptual to physical models via logical models using the pull down menu…The insurance client of my company adopted this mainly for creating reverse engineered physical models for understanding and ongoing maintenance, given the intuitive interface and ease of use, as well as excellent modelling capability.” -Data analyst in the finance industry, review of Erwin Data Modeler at Gartner Peer Insights
“ER studio has been a pillar in our company’s data strategy both in the design and maintenance of models…We have used ER studio extensively to visualize data structures and understand relationships across models. Features we really like includes the ability to draw up models on the conceptual level and transform to relational/physical. The UI makes for easy modeling and it is easy enough to get different views extracted to PDF/JPG and various other formats.” -Production support engineer in the services industry, review of ER/Studio Data Architect at Gartner Peer Insights
Data modeling has become a pillar of the growing data governance market, particularly because of the streamlined data visibility that data models allow enterprises to provide to non-data professionals within their organization.
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.
Data modeling software makers
Data modeling software helps an organization scale with growing data types, databases, and the reliance on data.
Here are some of the top data modeling solutions for your business:
- Archi Archimate Modelling
- Erwin Data Modeler
- IBM Infosphere Data Architect
- Idera ER/Studio Data Architect
- MySQL Workbench
- Navicat Data Modeler
- Oracle SQL Developer Data Modeler
- SAP PowerDesigner
- SAS Model Manager
See more: Best Data Modeling Tools & Software 2021