Data Modeling has recently emerged as one of the best skills to have in the extremely competitive industry of data science for database generation. Data scientists have recognized the need for data modeling in data analysis, as it is the foundation for gathering clean, interpretable data that businesses can use to make decisions. 

What Is Data Modeling and Why Do You Need It? 

Data modeling evaluates and measures how an organization manages the flow of data in and out of the database management system. Since it is responsible for creating the space needed for your data, data modeling is one of the most important parts of a Big Data project. Data modeling structures the space for your data, and looks after the factors related to the environment your data lives in. In short, data modeling is the management of data within an organization.

Data modeling also determines how the data should be treated, how the data neurons connect with each other and define how the data is generated and what story it will tell going into the future. 

Considering the impact it has on an organization, decisions regarding data modeling need to be made early on in the data-gathering process. It is up to the organization to decide what story each data set will narrate, and for data to tell the perfect story, it needs to be modeled to perfection. 

Numerous software applications make use of data modeling processes to give the most seamless customer experience. With the changing culture of the world, it is imperative that the data you hold should be altered in a way that best matches the needs of the end customer. Ensuring a perfect customer experience is something that many organizations are working on, and this experience can only be achieved through the use of perfect data modeling strategies. 

The Data Modeling Process 

Data modeling serves as a means to complement business modeling and to work towards generating a sufficient database. The process for designing a database includes the production of three major schemas: conceptual, logical and physical. A Data Definition Language is used to convert these schemas into an active database. A data model that is fully attributed and covers all major aspects includes detailed descriptions for every entity contained within it.

Although data models can be created through the use of numerous methods, there are two methodologies that produce the best model. These are known as the bottom-up and top-down data modeling processes. 

• Bottom-up Model: Bottom-up models, also known as integration models, are created through re-engineering efforts. This method usually starts with the existing structure forms for data and underlying reports. This model may not be feasible for data sharing, considering that they are built without specific reference to all other departments/parts of the organization. 
• Top-down Data Model: Top-down data models are created through an abstract methodology, by garnering information from people who have sufficient expertise in the subject area. The system for this data model may not implement in all entities, but the model does serve as a brilliant template or reference point. 

What Do You Need to Become a Data Modeler? 

Data modelers help organizations efficiently manage and organize data within a computer system. Data modelers are tasked with the responsibility of working with professionals to ensure that the data model is designed according to their specific needs. In all originality, data modelers are required to make complicated sets of data easy and less complicated. 

The career path for becoming a data modeler starts with specific education in the data science field and requires a lot of continuous training and certifications throughout one’s career. You’ll be required to continuously develop and train to stay in touch with the ever-changing norms of data modeling and data development. 

Education 

To enter the field as a data modeler, you should acquire a basic bachelor’s degree in information, applied mathematics, or computer science. You may find it advantageous to then look into pursuing your MBA or a post-graduate course in information systems management or business. 

You must exhibit the following skills before pursuing a career in data modeling:

1. Digital logic 
2. Computer architecture and organization
3. Data representation 
4. Memory architecture 
5. Familiarity with numerous modeling tools that are currently in place within organizations 
6. Directions in computing 
7. SQL language and its implementing 
8. Exemplary communication skills that will help you in making your way around organizations with an intricate hierarchy
9. Sufficient experience using Teradata or Oracle database systems

Job Expectations and Compensations 

Stepping into a career as a modeler, you’ll have to work with data analysts and architects to identify key dimensions and facts to support the system requirements of your client or company. You will be required to: 

1. Manage data 
2. Maintain the integrity and usefulness of data 
3. Restructure data 
4. Reduce redundancy 
5. Have necessary knowledge of the domains you will work on

Most data modelers start their journey as analysts and then move up the ladder of the hierarchy as they prove themselves and gain experience in the lower ranks. There is a lot of scope for learning, and data modelers can be assured of being greatly compensated. In fact, according to Glassdoor, the average salary in the U.S. is $79,000 and extends as far as $114,000 depending on the level of experience. 

Importance of Certifications 

Certifications are crucial when it comes to data modeling in the formal setting. Companies agree it’s important for their data modelers to obtain reputable certifications that prove their expertise and also enhances their skills. These certifications include Big Data and Data Science courses, Big Data Architect Master’s Programs, Big Data Hadoop Training, and Data Science with R, among others.

We hope you found this article informative. In case you have any queries, list them out in the comment section below.