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The Role of Data Modeling in Database Design: Connecting the Dots




Information systems are complicated enough as they are so it is important that they are designed and presented in a well-thought-out manner for team members to better understand how these systems work, thus enabling them to be more efficient in their roles.

 

A data model provides the needed clear visualization of the organization’s information system and how it connects and creates relationships between different data points and structures, as well as illustrating the ways the data can be grouped and organized and its formats and attributes. Ultimately, the data model serves as a guide for the organization to effectively use data in meeting their business information needs

 

Why do data modeling?

 

At its heart, data modeling is a core data management discipline. It helps address the organization’s information needs for various business purposes, including proper identification of the elements that need to be included in specific applications or databases. It also helps define the organization’s data standards in accordance with its data governance programs and specific needs.

 

In addition, data modeling offers other key benefits for the organization, among them being:

 

  • Reduces errors in software and database development

  • Greater consistency in documentation and system design across the enterprise

  • Improvement of application and database performance

  • Ease data mapping throughout the organization

  • Improvement of communication between developers and business intelligence teams

  • Faster and easier process of database design at the conceptual, logical, and physical levels

 

Data modeling and database design are related in such a way that together, they serve as the linchpin in constructing robust and efficient data management systems.

 

Types of data models

 

Data modeling has evolved in such a way that multiple model types have been conceived to cope with the increased complexity and the varying data storage needs among businesses. 3 data model types are commonly used:

 

Conceptual data model – It offers a high-level visualization of the business or analytics processes that a system will support. It maps out the kinds of data that are needed, how different business entities interrelate, and associated business rules. This particular model is not tied to specific database or application technologies.

 

Logical data model - Once a conceptual data model is finished, it can be used to create a less abstract logical model that would show how data entities are related and describe the data from a technical perspective. The technical side of an organization uses logical models to help understand the required application and database designs and is also not connected to a particular technology platform.

 

Physical data model – The physical data model is then created out of the logical model. This time, the physical model is created specifically for the database management system (DBMS) or application software that will be implemented. This model would serve to define the structures that the database or a file system will use to store and manage the data, which may include tables, fields, indexes, triggers, and other DBMS elements. Database designers use physical data models to create designs and generate schema for databases.

 

The data modeling process and best practices

 

Regardless of the different data models adopted by different organizations, they all follow formalized workflows that include a sequence of tasks to be performed in an iterative manner that ensures a successful data modeling process. The ideal process workflow must follow these specific steps:

 

  1. Identify the entities. It is important to first identify the things, events, or concepts that are represented in the data for modeling. It is also important that each entity should be cohesive and logically discrete from all others.

  2. Identify key properties of each entity. Each entity type has one or more “attributes” which are the entity’s unique properties. As such, it is important to identify these attributes and single them out in each entity.

  3. Identify relationships among entities. With the attributes of each entity identified, it would be easier to determine how each entity is related to others, such as if they share specific attributes or if their attributes can be linked together to form a more cohesive structure that will help build the overall process. These relationships are usually documented via unified modeling language (UML).

  4. Map attributes to entities completely. Creating a map will let the business see how it is going to use the data. Several formal data modeling patterns are in widespread use. Object-oriented developers often apply analysis patterns or design patterns, while stakeholders from other business domains may turn to other patterns.

  5. Assign keys as needed and decide on a degree of normalization that balances the need to reduce redundancy with performance requirements. Normalization is a technique for organizing data models (and the databases they represent) in which numerical identifiers, called keys, are assigned to groups of data to represent relationships between them without repeating the data. This helps reduce the amount of storage space a database will require though this comes at the cost of its query performance.

  6. Finalize and validate the data model. Data modeling is an iterative process that should be repeated and refined as business needs change.

 

The journey from conceptualizing data models to implementing database structures is an iterative and collaborative process that demands a nuanced understanding of both the theoretical underpinnings and practical implications. By unraveling the intricacies of data modeling and seamlessly connecting them with the principles of database design, organizations can optimize performance, adapt to evolving business needs, and foster a dynamic environment where information can be stored and retrieved with great ease and efficiency.

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