Get in Touch
With Our Experts
+49 711 / 75886-600
Please Type A Message
Let us know where we can contact you
Please click to start verification
Send message
Thank You For Your Message!
We will contact you as soon as possible.
Close Window

Three elements influence the effectiveness of an enterprise application: The quality of the code, the user experience (UX) and the data model. This last element is not only the responsibility of the IT department but must be co-designed by relevant stakeholders to achieve an optimal result.

We explain what is behind a data model, what significance it plays for internal processes and which types of models companies should use in which cases.

Definition: What Is a Data Model?

In the literature, there are various definitions of the term data model, some of which are very detailed. In our view, a simpler, pragmatic description that ensures a common basis of understanding is sufficient for everyday business.

A data model, also database schema or data schema, describes current or planned data structures for a use case in the company. Data and their relationships to one another are visualized, whereby the visualization can take place from a technical or business perspective and include IT systems, employees and workflows.

What Is the Benefit of a Data Model?

Improved Decision Quality

Data modeling, i.e. the creation of a data model, is an essential step in software development and a prerequisite for optimal utilization of data in companies. After all, the quality of decisions based on analysis and BI tools is ultimately also based on the quality of the data models used.

Faster Time-To-Value

Data models provide a static overview of the data and data relationships in an area of use. Particularly in complex use cases, it is helpful to obtain these before the actual coding. If IT and business departments discuss data models in advance, corrections in the development process can be minimized. Data modeling can thus reduce programming costs by 60 to 75 percent.

Cost Reduction

Data models make it easier to network the data sources in a company in such a way that technical capacities are used efficiently. This reduces the costs for hardware and storage space. At the same time, the IT infrastructure becomes clearer, so that maintenance can be carried out faster and more cost-effectively.

Effective Communication

A semantic data model creates a basis for communication for all stakeholders in the project. Creating it, forces employees to structure their ideas clearly and comprehensibly. Stakeholders use a common vocabulary when referring to the data model for requirements and change requests.

High Data Quality

Data models form a reference point for quality assurance, so that an as-is/target comparison between planning and the current implementation status can be made at any time. Since they provide an overview of the data structures in the company, they help to plan effective data integration processes.

Data Model Types

Data model is a generic term. Depending on the project phase and department, different methods are used for data modeling.

Conceptual data model / semantic data model

This type of data model is usually created first and by business stakeholders to develop business processes. Using text and graphics, the model describes real-world objects and their properties and relates these business objects to each other. For example, a product such as a hydraulic pump would be listed as an object, and the product category, weight, and color would be listed as properties.

Logical data model

The logical data model is created from a technical perspective. It extends the conceptual data model with information, for example on data formats, attributes and key properties. Thus, the data model can be transferred to a vendor-independent database management system (DBMS).

Physical data model

For use in operations, the logical data model must be extended and formatted in a vendor-specific manner, depending on the DBMS being used. In the relational data model, for example, the data must be assigned to tables and columns and authorizations, triggers and views must be created. The transfer and adaptation of the logical data model to the syntax of the respective DBMS can be partially automated.

Create a Data Model: How Should Companies Proceed?

Which methods and technologies companies use to create a data model depends on the application purpose and the existing IT infrastructure. However, some basic principles have always proven themselves.

Top down

A top-down approach is always recommended. The technology is subordinated to the application scenario. In terms of data modeling, it means that business decision-makers first create a semantic data model, followed by the IT department developing the logical data model. This is coordinated, released and translated into a physical data model.


Ideally, a data model is created incrementally, in successive iterations, whereby technical and business stakeholders should be in close contact with each other. Mistakes made in data modeling result in considerable costs.


Companies should ensure that their data modeling tool can interface with their existing systems as easily as possible. Future company developments and possibly increasing requirements for data modeling should also be taken into account. Ideally, solutions should be easily scalable or modularly expandable.


The use of versioning is also recommended. Many data modeling tools offer a history as standard, so that users can track changes and their effects down to the business object level. Database versioning not only helps with troubleshooting, but also speeds up problem solving many times over and contributes to transparency in data management.


Since data models lay the foundation for databases, the quality of the data model determines the value companies can derive from their data. For this reason, and because the lifespan of data models extends far beyond that of individual applications, companies should devote sufficient time and resources to their design and implementation.

Frequently Asked Questions About Data Model

Data success is plannable

Leverage the value that lies in your data assets. With a holistic data strategy.
Ihr Webbrowser ist veraltet

Aktualisieren Sie Ihren Browser damit diese Webseite richtig dargestellt werden kann.

Zur Infoseite