Data modeling helps provide solutions to business needs, with rules and requirements implemented through feedback from stakeholders so they can be utilized in the design of a new system or an adapted iteration of an existing system.
What Is a Data Model and What Is Its Purpose?
A data model is an abstract representation of data elements that standardizes data relationships and their real-world representations.
Data models are abstract, visual representations of data points mapped out in a way that makes it easier to understand the connections and workflows needed at a database level – essentially a blueprint of how to build the most optimized database based on the data set.
Data models feature data entities and their attributes, unique keys to reduce redundancies when data is repeated and new relationships are formed throughout a model, and the unified modeling language (UML), which provides a set of best practices for constructing appropriate model structures.
Data models offer several benefits to organizations when produced correctly, including producing better data quality and databases less prone to error due to data cleansing and organization, providing a clear way for employees to understand an organization’s data and cross-departmental use, and creating better database design when building applications and procuring insights.
What Are Data Model Types?
The three primary types of data models include conceptual data models, logical data models and physical data models.
Conceptual Data Models
Conceptual data models are the simplest and most abstract variety, producing an overall layout and set rules of the data relationships with little annotation or data use. Business rules, entity classes, categories and other limitations are typically found here, with conceptual data models typically being found in a project’s discovery phase.
Logical Data Models
Logical data models expand on conceptual frameworks by considering more relational factors and utilizing basic annotation related to all attributes, but stop short of including annotations that feature units of data. Logical data models are typically utilized when creating data warehousing plans.
Physical Data Models
Physical data models are the final step before database creation and account for system-specific rules to provide detail on data points and their relationships. This results in the creation of a schema, or a final set of instructions used to build a database.
What Are the 5 Steps Under Data Modeling?
There are five steps that can be used to design proper data models, all depending on your data modeling software:
- Understanding Workflow: To understand the workflow of the application being worked within, including maintaining knowledge of all tools and having data that's well organized.
- Modeling Queries: The queries are then modeled to pull data by the requirements of the application being used.
- Designing Tables: Tables are designed to organize data in an easily understandable manner.
- Determine Primary Keys: Determine the primary keys that will be used to access data.
- Efficacy of Data Types: The final step is to ensure data types are being used effectively.
