Data modeling is the process of transcribing a complicated software system design into a simple diagram that uses text and symbols to show how data needs to move forward in it. This data structure ensures the optimal use of information to guide business intelligence processes, such as the creation of a novel software or the design and implementation of a database.
Since data modeling highlights what information is necessary and the processes it needs to go through, it serves the same purpose as an architectural building plan. In this analogy, it tells workers how each step is related to others to ensure a smooth construction process.
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Read More about “Data Modeling”
A data model is basically a flowchart that shows the relationships between multiple processes. Stakeholders must check each step for mistakes before actual program coding. In some instances, a data model is built from finish to end via reverse engineering. In this method, developers dissect a system to see its components and how each works to recreate a similar program.
Critical Steps in Data Modeling
- Step 1: Identify the data required in the modeling process.
- Step 2: Determine a notable property for each data type to identify each within the model quickly.
- Step 3: Create a draft that shows how each component is related to others.
- Step 4: Set data attributes that must be present in the model.
- Step 5: Match each attribute to a component to provide an adequately useful context for each data type.
- Step 6: Verify and finalize the data model and make the necessary changes to keep it updated.
Types of Data Modeling
Data modeling is of three types—physical, logical, and conceptual. Each uses a different process model described in more detail below.
- Physical data model: This defines all of the logical database components and services that are necessary to build a database. Think of it as the layout of an existing database. It comprises the tables’ structures, column names and values, foreign and primary keys (sets of columns used in databases to define many-to-one relationships), and the relationships among the tables. It is used by database administrators to estimate the size of systems and perform capacity planning.
- Logical data model: This describes the data in as much detail as possible without considering how they would look in the database. It includes all entities and the relationships among them. All attributes and the primary key for each entity are specified. Foreign keys or those that identify the relationship between different entities are also specified. Normalization happens at this level.
- Conceptual data model: This is a structured business view of the data required to support business processes, record business events, and track related performance measures. It identifies the data used in the business but not how it is processed or how it physically looks. It does not consider any underlying business applications. It represents the overall structure of data required to support business requirements without considering the software or data storage structure.
In a nutshell, the conceptual data model shows the big picture as to what the data modeling process should do. The logical model then defines how each of the bits of information in the process relates to the others. And finally, the physical data model puts the concept and logic together to show how the entire process works.