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 be achieved only through the use of perfect data modeling strategies.

Post Graduate Program in Data Analytics

In partnership with Purdue UniversityView Course
Post Graduate Program in Data Analytics

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?

The skills required for data modeling are quite different than the skills required for programming and systems administration. While programmers and administrators are required to have sufficient expertise on the technical front, data modelers are required to be more apt at the logical side of things. The skills required for data modeling include the following:

  • Conceptual Design
  • Abstract Thinking
  • User communication
  • Internal communication

Free Course: Python Libraries for Data Science

Learn the Basics of Python LibrariesEnroll Now
Free Course: Python Libraries for Data Science

Based on these requirements, a person who does not have the required software and system knowledge, but has the proven ability to think conceptually and abstractly, will be considered perfect as a data modeler.

Communication skills are essential for all data modelers. Organizations look for strong communication skills in data modelers because modelers are required to translate and balance all user requirements. Moreover, they are also required to document the final results in a perspective that is easy to understand for all users.


Many recruiters looking for data modelers want candidates with a bachelor’s degree, preferably, in information science, applied mathematics, or computer studies. These degrees are deemed perfect for a data modeler, and the candidate is considered suitable in most cases. However, some employers may also want to look out for data modelers with multiple courses in information systems management or business management. Data Modelers should also be skilled in database administration and should know how to look over a database and to think of plausible outcomes for different data complications.

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

  1. Digital logic: Digital logic is also known as boolean logic, and it is the basis for all modern computer systems and programming languages. It is a system that simplifies complicated problems into “yes/no”, “true/false” or “1/0” values that are placed into equations to produce input and output operations. As the fundamental concept behind coding, it is important to possess this skill in order to clean and organize an unstructured set of data.
  2. Computer architecture and organization: This skill builds on the first listed skill of digital logic. Logic, architecture, and organization are all interrelated, and a firm grasp is needed of all of these in order to optimize performance. Computer architecture is the logical set of rules that allow a programmer to interface between the hardware and software, and how it internally functions and is implemented. The organization of the computer is an expression of its architecture, and how the system itself is structured. A solid understanding of computer architecture and organization will enable you to maximize efficiency when working with data.
  3. Data representation: Data representation involves breaking down complicated information into simpler bits, such as being coded into numbers. This allows for easier gathering, manipulation, and analysis of data, which can save valuable time and money.
  4. Memory architecture: After you understand how to best represent and code the data, it is important to be able to store it for future retrieval. Memory architecture concerns how binary digits come to be stored in a computer’s cells, as well as the storage of more complicated data in spreadsheet and database programs. The most important part of memory architecture is being able to find the method that best combines speed, durability, reliability, and cost-effectiveness while not compromising the integrity of the data.
  5. Familiarity with numerous modeling tools that are currently in place within organizations: The list of tools that exist to aid in data modeling is extensive, however, some of the top tools include PowerDesigner, Enterprise Architect, and Erwin. Organizations utilize these tools in order to structure and define data for optimal results. Already being familiar with these tools can help save valuable training time on the job and enable you to more efficiently be able to analyze your data sets.
  6. FREE Course: Introduction to Data Analytics

    Learn Data Analytics Concepts, Tools & SkillsStart Learning
    FREE Course: Introduction to Data Analytics
  7. Adapt to new modeling methods: Data modeling will continue to evolve. The differences in infrastructure, data sources, and models will likely become more complicated in the coming years. The ability to quickly learn and adapt modeling methods from case studies or other proven approaches is a crucial skill for a data modeler to stay up to date.
  8. SQL language and its implementation: SQL stands for “structured query language” and holds primary importance when becoming a data modeler, as it is the standard programming language for manipulating, managing, and accessing data stored in relational databases. Its ease of development and portability helped to make it the nearly universal language for querying databases. In short, without a foundation in SQL, it is not possible to be a data modeler.
  9. Sufficient experience using database systems: Relational Database Management Systems (RDBMS) that possess big data handling capabilities, such as the ability to quickly store and fetch data. Experience with these is absolutely necessary for managing a complex data environment.
  10. Exemplary communication skills that will help you in making your way around organizations with an intricate hierarchy: Data modeling isn’t just about possessing technical skills. You also need to be able to communicate your knowledge of complicated technical data in such a way that those in any non-technical data roles will also be able to understand. Data modelers need to be able to communicate with all levels of a business in order to best help implement well-informed changes and promote growth. This can be quite challenging, but it is important to be able to relate to and inform everyone while understanding the nuances of business politics.

How Do You Advance as a Data Modeler?

As soon as a novice modeler starts their training period, they are assigned to an experienced mentor. The experienced mentor should preferably be someone who has years of experience in data modeling behind them and has partaken in many training programs, both as a learner and as a trainer. The mentor should be well versed with the techniques used for data modeling within the industry and should know of all the systems in place with the specific organization. The experience of the mentor and the training methodology used by them, usually defines how well the data modeler is able to apply his or her skills within the organization.

There are numerous advancement opportunities for data modelers in the workplace. A data modeler’s career can grow over time, and they can soon head their own department or even become a manager of an IT firm that works in data marketing or data modeling.

Career Outlook

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 manage and keep the integrity and quality of the data. It’s essential to have domain knowledge to be able to interpret the results.

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 market for data modelers is projected to $78,601 on average. Data Modelers also get paid well, which is why there is no shortage of adequate monetary and career opportunities.

Get broad exposure to key technologies and skills used in data analytics and data science, including statistics with the PG Program in Data Analytics.

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 Engineer Master’s Programs, Big Data Hadoop Training, and Data Science with R, among others.

If you're interested in becoming a Big Data expert then we have just the right guide for you. The Big Data Career Guide will give you insights into the most trending technologies, the top companies that are hiring, the skills required to jumpstart your career in the thriving field of Big Data, and offers you a personalized roadmap to becoming a successful Big Data expert.

About the Author

Ronald Van LoonRonald Van Loon

Named by Onalytica as the world's #1 influencer in Data and Analytics, Automation, and the Future Economy (Tech), Ronald is the CEO of Intelligent World and one of the top thought leaders in Data Science and Digital Transformation.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.