„In short, we were impressed by the modularity and scalability with which you can work in the system. "
Siegfried SchneiderCMO, Red Dot Design
product data
12 min
What is a “Data Model” in the context of software and what significance does it have for the system architecture, system stability, flexibility and scalability of an application?
A data model describes how data is structured, stored and linked together. It plays a central role in the world of software development, as it forms the basis for efficient data management and smooth processes. With a well-thought-out data model, you can ensure that data is logically organized and technically feasible.
Especially for enterprise software such as ERP, DAM, PIM or MDM, a flexible data model is essential for the long-term adaptability to the needs of the users, as well as the scalability of the solution. Only in this way can an application allow its users to optimally map their own data management (e.g. for product information or master data).
The importance of a data model goes far beyond its technical implementation. It is an indispensable tool for visualizing complex data flows and creating a common language between specialist departments and developers. In this article, you will find out what a data model is, what types there are and why it is so important for companies. We also show you specific examples and tips for implementation.
Translated with www.DeepL.com/Translator (free version)
A data model defines how information is presented and processed in a system. It ensures that data is organized in a uniform structure so that it can be easily retrieved, stored and analyzed.
Put simply, a data model depicts the relationships between different data types. It visualizes how data objects interact and how they can be implemented technically.
Example: In an e-commerce system, a data model could map the relationship between products, categories and customers. This makes it possible to track which products are contained in a category and which customers have purchased them.
There are three main aspects to a data model:
A good data model is essential to ensure data integrity and consistency. It reduces redundancies and facilitates collaboration between teams involved in the development or use of databases.
The logical data model is the theoretical representation of data. It describes how data is organized and what relationships exist between the data elements. This model is particularly useful for business analysts, as it makes the requirements of the database understandable without taking technical details into account.
The physical data model, on the other hand, focuses on the technical implementation. It describes how data is stored in a database, including details such as storage locations, indexing and data types. The physical data model is about implementing the logical structures in such a way that the database works efficiently without taking technical details into account.
Example: In the logical model, a customer could be represented by a table with attributes such as name, email and address. In the physical model, this table is defined with concrete data types (e.g. VARCHAR for names) and indexes can be added to speed up data access.
These models help to organize data optimally for different use cases. For example, the relational model is ideal for applications with structured database queries, while the object-oriented model is preferred in software development projects.
Data modeling is a systematic process that involves the creation and maintenance of data models. This process is iterative and begins with the definition of requirements. The business objectives and the required data types are determined.
In the conceptual phase, a rough overview is created to identify the most important data objects. In the next step, logical modeling, the details of the relationships and dependencies between the data are defined. Finally, physical modeling follows, in which the technical implementation takes place in a database.
Data structures are at the heart of every data model. They define how data is organized and stored. In relational data models, data is often structured in tables, with each row representing an entity and each column an attribute.
Diagrams and visual representations are essential for developing a data model. They help teams to understand complex structures and ensure that no details are overlooked.
A clearly defined data model offers numerous advantages for companies:
Example: A company that maps its product portfolio in a clearly structured data model can add new products more easily while keeping existing data consistent
Data models are used in various areas:
Creating a data model can be complex. A common problem is the lack of coordination between specialist departments and developers. This is where workshops help to clarify requirements and avoid misunderstandings. Choosing the right tools is also crucial. Specialized software such as ER diagram tools make modelling easier and ensure consistent results.
Data models are the backbone of modern software and business systems. They create a solid foundation for managing, analyzing and using data. A well thought-out data model saves time, costs and resources. Companies that invest in data modeling secure long-term competitive advantages.