Data Modeling: What is it and Why does it matter?

All companies collect data, which must be stored somehow. Those organizations can then make data-driven decisions and get useful, business insights that can help increase company profit. Data-driven decisions are key to your business success. Data modeling is not as easy as it sounds though. First, you need to understand the structure of your business. There are a lot of techniques you can use to ensure successful data modeling.

What is Data Modeling?

Data Modelling is the process of analyzing the data objects and their relationship to the other objects. It is used to analyze the data requirements that are required for business processes. The data models are created for the data to be stored in a database. The data model’s main focus is on what data is needed and how we have to organize data rather than what operations we have to perform.

The data model is an architect’s building plan. It is a process of documenting complex software system design as in a diagram that can be easily understood. The diagram will be created using text and symbols to represent how the data will flow. It is also known as the blueprint for constructing new software or re-engineering any application.

Why are Data Modeling techniques important for your company?

Do you wonder why it is important to project ideas with data modeling techniques? Using the right databases, which will give you a clear representation of data, lets you analyze data more easily and quickly. It provides a quick overview of data, which developers can use in many applications.

It minimizes the risk of data redundancy by clearly and understandably representing collected data. How can it be used to improve data quality and produce useful and reliable insights for your company? Many businesses depend on it, so it is necessary for them to learn and adopt effective data modeling techniques for better results.

Types of Data Models

There are three main types that organizations use. These are produced during the planning of a project in analytics. They range from abstract to discrete specifications, involve contributions from a distinct subset of stakeholders, and serve different purposes.

Conceptual 

This is 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. Business executives are the main audience for conceptual data models, to help them see how a system will work and ensure that it meets business needs. Conceptual models aren’t tied to specific databases or application technologies.

Logical 

They are less abstract and provide greater detail about the concepts and relationships in the domain under consideration. One of several formal data modeling notation systems is followed. These indicate data attributes, such as data types and their corresponding lengths, and show the relationships among entities. Logical data models don’t specify any technical system requirements. This stage is frequently omitted in Agile or DevOps practices. Logical data models can be useful in highly procedural implementation environments, or for projects that are data-oriented by nature, such as data warehouse design or reporting system development.

Physical 

This type is used for database-specific modeling. Just like with the logical model, a physical model is used for a specific project but can be integrated with other physical models for a comprehensive view. The model goes into more detail with column keys, restraints, and primary and foreign keys. The columns will include exact types and attributes in this model, and the data should be normalized as well. A physical model designs the internal schema.

Some benefits

Data modeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships among the data in a database or data warehouse. In addition, it can:

• Reduce errors in software and database development.
• Increase consistency in documentation and system design across the enterprise.
• Improve application and database performance.
• Ease data mapping throughout the organization.
• Improve communication between developers and business intelligence teams.
• Ease and speed the process of database design at the conceptual, logical, and physical levels.

Common challenges

• A lack of organizational commitment and business buy-in. If corporate and business executives aren’t on board with the need for data modeling, it’s hard to get the required level of business participation. That means data management teams must secure executive support upfront.
• A lack of understanding by business users. Even if business stakeholders are fully committed, data modeling is an abstract process that can be hard for people to grasp. To help avoid that, conceptual and logical data models should be based on business terminology and concepts.
• Modeling complexity and scope creep. Data models often are big and complex, and modeling projects can become unwieldy if teams continue to create new iterations without finalizing the designs. It’s important to set priorities and stick to achievable project scope.
• Undefined or unclear business requirements. Particularly with new applications, the business side may not have fully formed information needs. Data modelers often must ask a series of questions to gather or clarify requirements and identify the necessary data.

Conclusion

Data modeling is a crucial IT discipline for any organization. When building an app, it depicts 360-degree data dependencies and preempts bottlenecks. It helps maintain data-driven cloud services like e-commerce and provides better user experiences. It also keeps enterprise data repositories up-to-date so that you can extract the most valuable insights. By knowing the different types of data models, data modeling techniques, and best practices, one can unlock its full potential.