What is data modeling?
Data modeling is the process of visualizing complex software systems using simple diagrams, including text and symbols, to depict how data will flow within enterprise information systems. It helps illustrate the types of data stored and used within the system, how the data can be organized or grouped, and the relationships among different data types.
In other words, data modeling is the process of creating data models. Data models are conceptual representations of data objects, along with the relationships between them and the rules. In effect, a data model can be considered similar to an architect’s building plan or blueprint, which helps create conceptual models and, at the same time, sets relationships between different data items.
Data models help maintain consistency in naming conventions, semantics, default values, and security, all while ensuring data quality. This helps to provide a consistent and predictable way of defining and managing data resources across an organization. They are built encompassing business needs. Business stakeholders help define the rules and requirements through feedback. This allows stakeholders to identify and rectify errors before the actual code of a new system is written.
They are typically living documents that evolve based on changing business requirements. They offer a deeper understanding of what is being designed and play a crucial role in planning IT architecture and strategy and supporting various business processes.
Types of data models
Similar to most design processes, data modeling starts at a high level of abstraction and gradually becomes more specific. Based on their degree of abstraction, data models can be divided into three types:
- Conceptual data model: This type of data model is the visual representation of database concepts and also the relationship between them. It provides a high-level description of a database design that presents how data is interrelated and what kind of data can be stored. It is also referred to as a domain model and is typically created as part of the initial project requirements gathering process. Conceptual data models are aimed to provide a better understanding of data for a business audience and not a technical one. Once a conceptual model is created, it can be transformed into a logical data model.
- Logical data model: This data model defines the structure of data entities and describes data from a technical perspective. It is less abstract and offers better detail about data concepts and relationships. In a logical data model, the attributes of each entity are clearly defined. It is used as a detailed representation of database design, and it serves as the basis for creating a physical data model.
- Physical data model: This category of data models is used for database-specific modeling. It offers a schema for how data will be stored within the database. This type of data model describes the database design for specific database management systems (DBMS) and goes into detail about primary and foreign keys, column keys, and restraints.
Types of data modeling
Data modeling enables organizations to establish consistency, discipline, and repeatability in data processing. It has evolved apace with DBMS. The following are some of the data modeling approaches:
- Hierarchical data modeling: This data modeling approach has a tree-like structure in which each record has a single parent or root. It represents one-to-many relationships. Hierarchical data modeling is used in geographic information systems (GISs) and Extensible Markup Language (XML) systems, even though it's relatively less efficient when compared to recently developed database models.
- Relational data modeling: This database modeling technique was suggested as an alternative to the hierarchical data model. It doesn’t demand developers to define data paths, and in it, data segments are specifically joined using tables, which reduces database complexity.
- Entity-relationship (ER) modeling: ER modeling uses diagrams to graphically show the relationships between different entities in a database. Data architects use ER modeling tools to convey database design objectives by creating visual maps.
- Object-oriented modeling: Object-oriented data modeling gained popularity as object-oriented programming became popular. It is similar to ER modeling techniques but differs because it focuses on object abstraction of real-world entities. It can support complex data relationships and groups objects in class hierarchies.
- Dimensional data modeling: This data modeling technique was designed to optimize retrieval speeds once data is stored in a data warehouse software. Unlike ER and relational models that focus on efficient storage, dimensional data models increase redundancy to make it easier to locate information.
Key steps in the data modeling process
A data model is nothing more than a drawing. They are just shells without populated data. A data model can be considered as a guide that becomes the basis for building a detailed data schema. It can also be used to support data schema later in the data lifecycle. The following are some of the key steps involved in the data modeling process:
- Identifying the entities or business objects that are represented in the dataset that is to be modeled
- Identifying the key properties of each entity to differentiate between them in the data model
- Identifying the nature of relationships each entity has with one another
- Identifying the different data attributes that should be incorporated into the data model
- Mapping the data attributes to the entities so that the data model reflects the business use of the data
- Assigning keys appropriately and determining the degree of normalization by considering the need to reduce redundancy, along with performance requirements
- Finalizing the data model and validating it
Benefits of data modeling
Data modeling presents several distinct advantages to organizations as part of their data management. It makes it easier for data architects, developers, business analysts, and stakeholders to view and understand relationships between the data stored in a database or in a data warehouse. The following are some of the benefits of data modeling:
- Makes databases less prone to errors and improves data quality
- Facilitates smarter database design, which can translate to better applications
- Creates a visual flow of data, which helps employees understand what is happening with the data
- Improves data-related communication across an organization
- Increases consistency in documentation
- Makes data mapping easier throughout an organization
- Fastens the process of database design at the conceptual, logical, and physical levels
- Reduces development and maintenance costs
- Portrays business requirements in a better way
- Helps to identify redundant or missing data
Data modeling best practices
A data model must be comprehensive and resilient to help organizations lower risks, reduce errors, increase consistency, and ultimately reduce costs. The following are some best practices of data modeling:
- Verify the logic
- List all involved entity types
- Refer and utilize recommended naming conventions
- Map all entities along with their relationships
- Check for data redundancy and remove it using normalization
- Apply denormalization methods to improve performance if not optimal
Amal Joby
Amal is a Research Analyst at G2 researching the cybersecurity, blockchain, and machine learning space. He's fascinated by the human mind and hopes to decipher it in its entirety one day. In his free time, you can find him reading books, obsessing over sci-fi movies, or fighting the urge to have a slice of pizza.
