What Is Data Modeling?

Businesses today increasingly rely on data to drive decision-making. However, data comes in many forms, which must be sorted and have rules assigned to it before it can be analyzed. This is where data modeling comes in. IBM defines data modeling as the creation of a visual representation of a data set in its entirety, or specific parts of it, to illustrate how data points and structures connect. For example, if an organization assigns clients a customer ID, those ID numbers become more useful when they’re assigned to correspond with the products customers buy and the intervals at which they buy them.

Students who are interested in learning more about what data modeling is, and who want to help companies make smart data-driven decisions, should start by developing the knowledge and skills to be successful. Completing an advanced education, such as an online Master’s in Data Science, can prepare graduates to pursue this career path.

How Data Modeling Helps Businesses

Data modeling provides businesses with a visual representation of their data in a form that non-technical staff can understand. According to international data management firm CloverDX, data modeling is a tool that organizations can use to “accelerate their application development and unlock the power of their data,” adding that “data modeling helps organizations create a visual description of their business by analyzing, understanding, and clarifying the business’ data requirements.”

Although data modeling can be complex, when executed correctly, it can help businesses improve their processes, facilitate collaboration between technical and non-technical staff, and enhance analytics capabilities in ways that help the organization improve efficiency and reduce cost.

Types of Data Modeling

Individuals wondering, “What is data modeling?” should know its most common types. These include conceptual data modeling, logical data modeling, and physical data modeling, each having its own specific purpose.

  • Conceptual data modeling: This type of data modeling provides organizations with a big-picture view of the data that’s being used, how it’s being organized, and the relationship between various data points. For example, if a company wants to implement a new purchasing system, its conceptual data model may include data points such as client ID numbers, purchase order numbers, inventory data, accounts receivable information, and shipment dates.
  • Logical data modeling: Logical data models are not specific to a database. IBM defines this model as one “that describes things about which an organization wants to collect data, and the relationships among these things.” Logical data models are often described as being the blueprint that users can draw from when creating other types of data models.
  • Physical data modeling: Where logical data models serve as blueprints, physical data models are used to design and create databases. With physical data modeling, database designers draw information from conceptual and logical data models, and use that information to visualize how columns, graphs, tables, and other useful visual depictions will be represented to users.

Work Settings for Data Modeling Professionals

The U.S. Bureau of Labor Statistics (BLS) classifies data modeling professionals under the blanket category of computer systems analysts. The BLS further notes that professionals in this field may work full time, directly with an organization, or as an independent contractor. Most data modelers work 40 hours per week, although overtime hours may be needed to complete time-sensitive projects.

Data Modeling Job Growth Projections

Job growth in this field is projected to be favorable. BLS data notes that employment of operations research analysts, including data modeling professionals, is expected to grow by 25% between 2019 and 2029, which is much faster than the average growth rate projected for all other occupations (4%). This projection indicates that approximately 26,100 new analyst jobs will become available during that same reporting period.

Average Data Modeling Salary

The compensation website PayScale reports that as of July 2021, the average base salary for data modeling professionals was $82,000 per year. However, salary ranges can vary based on a variety of factors, including the geographical region in which the position is located, whether an individual is employed full time with a company or if they’re working as an independent contractor, and an applicant’s experience level, among others.

Learn More About What Data Modeling Is and How This Skill Can Help You Advance Your Career

Businesses across the globe understand the importance of harnessing and structuring their data in ways that can help drive operational decision-making. Additionally, the exponential growth of big data has created numerous career paths for skilled data scientists. Students interested in learning more about what data modeling is, and the skills they’ll need to pursue jobs in this field, are likely to find that completing an advanced education — such as an online Master of Science in Data Science from Maryville University — can prepare them to be successful.

Are you ready to take your data science career to the next level? Discover how completing an online Master’s in Data Science from Maryville University can prepare you for the job you want.

Recommended Reading

Predictive Analytics in Insurance: Types, Tools, and the Future

Master’s in Statistics vs. Data Science

Research Analyst vs. Data Scientist


CloverDX, What is Data Modeling (And Why Is It Essential)?

DS Stream, “Why is Data Modeling Important for Your Business”

IBM, Data Modeling

IBM, Logical Data Models

IDERA, “Top Six Benefits of Data Modeling – Why You Should Build Data Models”

Indeed, “What is Data Modeling?”

PayScale, Data Modeling

Sinense, Data Modeling

U.S. Bureau of Labor Statistics, Operations Research Analyst

Vertabelo, “5 Examples of Conceptual Data Models”

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