What Is a Data Modeller and What Do They Do?

Indeed Editorial Team

Updated 22 August 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Many organisations maintain databases that are central to their functioning and business operations. Data modelling is a useful step to take before coding a database, as it allows you to make necessary changes before developers begin to create the database. Understanding how data modelling works can be beneficial when you want to do database modelling for your own projects. In this article, we discuss what a data modeller is, what skills you require to become a data modeller and the different types of data modelling.

What Is A Data Modeller?

Data modellers are computer systems engineers who are experts in data modelling. Data modelling is the process of documenting a software system design as an easy-to-understand diagram. It allows you to conceptually represent the data and the association between data objects and rules. It is beneficial because it allows you to identify errors or make necessary changes before the programmers have written any code. Data modelling can also be used to reverse-engineer the efforts of programmers and extract models from existing systems.

The primary goal for data modelling is to:

  • Ensure the data is accurately represented, as any data that is omitted from the database can result in incorrect results and inaccurate reporting

  • Help to define the database and the conceptual, logical and physical levels involved

  • Help to define the primary and foreign keys, relational tables and stored procedures

  • Obtain a clear picture for the base data that developers can use when creating the actual database

  • Identify any redundant or missing data

  • Make maintenance and upgrades on the IT infrastructure to make it faster and more affordable

Related: What Does a Data Scientist Do? And How To Become One

What Does Data Modeller Do?

A data modeller's service is crucial to the optimal working of most organisations that maintain large data volumes. They are responsible for identifying an organisation's business needs and managing their data to improve its function. They evaluate existing data systems to design and build databases that can be useful for a variety of purposes. A data modeller also identifies and defines the specific requirements of a system's users. Along with that, they may also suggest and implement innovative data solutions.

Data modellers work alongside database engineers to create data models and maintain data maps. They identify the best tools, standards and related development techniques for the organisation. They may have to work with other teams to create data models and data flows. Based on the analysis they make, they provide recommendations for the optimisation and standardisation of various information systems.

Skills For Data Modellers

Here are some skills that a data modeller may require on the job:

Communication skills

Data modellers may have to constantly communicate with other teams. An ability to communicate ideas and concepts clearly to others can be highly desirable for a data modeller. They may have to communicate these ideas to both technical and non-technical professionals. They benefit from having an ability to articulate complex ideas in ways that can be understood by a wide audience.

Related: How To Improve Communication Skills

Interpersonal skills

Data modellers mostly work as part of a team, or may have to coordinate with other teams to ensure smooth functioning of their operations. They may need strong interpersonal skills to seamlessly coordinate a variety of processes. In their professional life, they may benefit from being able to maintain a large number of professional relationships and collaborative engagements.

Read more: Interpersonal Skills: Definitions and Examples

Technical knowledge

Data modellers require sound technical knowledge regarding their craft, the equipment and tools they use and the complex workflows involved in their work. They may have good knowledge of mathematical and statistical operations like time series and cluster analysis. Since data modellers perform data analysis, they may require a working knowledge of various data analysis programs and software.

Familiarity with modelling tools

Data modellers constantly work with various tools to analyse and derive insights from a given volume of data. They benefit from being familiar with different data analysis and modelling tools and they may also have a good understanding of data modelling principles and useful methods like conceptual, logical and physical data analysis. Data modellers who are experts in a wide variety of tools are highly employable, as companies may have different operational requirements.


Data modellers may need good conceptualisation skills to create and visualise complex models with different types of components and interactions. They may have to determine the nature of relationships between entities to represent it logically and visually, for others to understand. They may also need to identify and solve conceptual problems as part of their work.


Data modellers can benefit greatly from learning and adapting quickly to changes in the world of business. Businesses periodically undergo change and with rapidly developing technologies, data modellers may need to constantly change and adapt their skills and capabilities to sustain in new environments. They may have to periodically learn new modelling methods from case studies, documents and external sources.

Problem-solving skills

Another skill that data modellers may require is the ability to solve problems efficiently. Their clients may present them with problem statements that require employable solutions within strict timelines. Modellers quickly identify issues and solve them using feasible methods. They can also save a client's time and money by identifying and eliminating potential errors before they occur.

Types Of Data Models

Depending on the type of data and the nature of the information they may be working with, modellers may pursue different approaches to data modelling. Some popular types of data models include:


The conceptual data model is a high-level, structured business view of the data that is required to track performance metrics, support crucial processes and record business events. This model is focused on identifying the type of data a business uses, but not its physical characteristics or processing flow. The general business scope of the model is to act as a point for discussion among stakeholders. Important classes in the data are selected and relationships are described based on real-world instances that the classes represent.


With hierarchical data models, the data is organised into a tree-like structure. Data is stored as separate records that are connected through links. This type of data modelling requires that each child record only have one parent, although a parent record can have multiple child records.


This approach to data modelling represents the database as a collection of relations, where every relation is a table of values. Every row in the table represents a collection of related data values. The central idea of a relational model is to describe a database as a collection of predicates over a set of predicate values. This enables users to describe the constraints on a value or combinations of values.


Closely related to relational models, this type of data model uses diagrams to depict the elements in a database and makes it easier to understand. The entity-relationship model (ER model) describes interrelated things that are of interest in a specific domain of knowledge. An entity is a piece of data, the object or the concept about which data is stored. The relationship is how the data is shared between entities.


This type of data model describes the data in as much detail as possible, without regard for the physical implementation or the database management system technology that is used to store the data. A logical data model includes all the information about entities and the relationships between them, within a database.


Graph data modelling is the process of describing an arbitrary domain as a connected graph of nodes and relationships with properties and labels. It is used to solve technical and business problems by organising a data structure for the graph database.


The physical model represents data as it is implemented in the database management system. Using this model, you can describe individual database tables, component columns and the details of relationships and keys. This model can also take into consideration, factors like performance, resource allocation and access. Using this approach, you can reach the highest level of granularity before actual deployment.

Advantages Of Data Modelling

There are several advantages to data modelling:

  • It ensures that the objects are accurately represented.

  • It allows you to define the relationship between tables, stored procedures and primary and foreign keys.

  • It helps businesses to communicate within and across organisations.

  • It helps to recognise the accurate sources of data to populate the model.

  • It allows a business to document data mappings in ETL (Extract, Transform, Load) process.

Disadvantages Of Data Modelling

There are some disadvantages to data modelling:

  • You may require the physical data's stored characteristics in order to develop a data model.

  • A navigational system uses complex application development and management, which requires advanced skills.

  • Small changes in structure require modification of an entire application.

  • There is no set manipulation language in database management systems.

How Much Do Data Modellers Make?

The job role of data modeller is becoming increasingly relevant. Successful data modellers can earn high salaries depending on their experience and expertise. The average base salary of a data modeller is ₹10,17,774 per year.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.


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