What is data modeling?

At a glance

• Creates a clear framework for data structures and relationships – as a blueprint for databases and information systems
• Three model levels: conceptual, logical, and physical
• A step-by-step process from defining objectives through structuring to optimization
• Essential for databases, BI systems, and consistent corporate management

Data modeling definition

Data modeling refers to the process of schematically representing data within a system in order to understand its relationships, attributes, and flows. Data modeling thus creates a clear and uniform view of a company's data. A data model defines the relationships between the various data records and optimizes the structuring, storage, and analysis of the data. Conceptual, logical, and physical data models are created, which serve as a blueprint for developing databases or information systems. By using a well-thought-out data model and standardized methods, data modeling ensures consistent data organization and enables efficient data management across all systems.

This creates the basis for reliable reports and advanced analyses, such as those required in modern business intelligence environments like DeltaMaster. DeltaMaster uses the structures created by data modeling to analyze and visualize data consistently.

What is a data model?

A data model describes which data a company captures, how this data is structured, and how it is interrelated. It defines the relevant objects (for example customer, article, or order), describes their characteristics, and defines the relationships between them. The data model thus serves as a binding blueprint for databases, data warehouses, and analysis applications. Data modeling is the process in which this blueprint is created. The data model is its result.

For analyses and reporting, multidimensional data models are mainly used, often as a star schema. At its center is a fact table with key figures such as revenue or sales volume. Around it are dimension tables such as time, customer, product, or region. Hierarchies make it possible to break down a value step by step, for example from total revenue through the sales region down to the individual branch.

A good data model ensures that everyone involved works with the same definitions. Without this foundation, typical problems arise: sales and controlling report different revenue figures because credit notes or intercompany charges are handled differently, and reports contradict each other.

What is the goal of data modeling and why is data modeling important?

The goal of data modeling is to organize and visualize complex data structures in such a way that they can be managed and analyzed efficiently. A well-thought-out data model:

• improves data quality and minimizes redundancies,
• optimizes storage requirements and speeds up data queries,
• serves as a central reference point for developers and analysts,
• ensures consistency in database design and reduces development errors,
• creates a solid data basis for business process optimization and well-founded decisions,
• supports compliance with legal regulations and compliance requirements.

Data modeling is particularly important in times of growing data volumes and increasing demands for fast, precise analyses.

What types of data models are there? – Types of data modeling

There are various types of data models and data modeling. They can be distinguished by their properties and their structure. Each model thus offers different approaches to organizing and managing data efficiently.

What properties do various types of data models have?

• A conceptual data model serves to define the fundamental data structures and relationships at an abstract level. It helps to simplify the understanding of data requirements.
• A logical data model is structured so that it defines the concrete structure of the data and the business rules that specify how data is to be organized and processed in a system.
• A physical data model is about the concrete implementation of data in a database. It specifies how data is physically stored and retrieved.

What structures do various types of data modeling have?

• A relational data model is based on tables in which data is organized in rows and columns. It is especially widespread and enables flexible queries.
• Dimensional data models are specifically designed for analytical purposes and organize data into facts and dimensions. This model makes it easier to analyze large volumes of data quickly and is frequently used in data warehouses for BI.
• In a hierarchical data model, data is arranged in a tree-like structure in which every database unit has a parent-child relationship. It is well suited for data with a clear hierarchy.
• An object-oriented data model combines data and its behavior in the form of objects. It is often used in modern programming approaches and is useful for complex data types.
• Entity-relationship data models (ER models) graphically represent the relationships between data objects, called entities, in a database. Through this visualization, they provide a clear basis for designing relational databases.

How does data modeling work? – Phases of data modeling

Data modeling takes place in several phases that ensure data is structured, consistent, and usable for analysis:

• 1. Define objectives and scope: At the outset, the purpose of the data model is determined, along with the specific business requirements and the types of data to be represented.
• 2. Identify entities and attributes: Next, the essential entities are identified. Each entity is described by specific attributes that represent its characteristics. For example, customer and product could be defined as entities, with their attributes comprising customer number, customer name and product name, price.
• 3. Define relationships between entities: The relationships between the entities are then established to clarify how they interact. For instance, the relationship between customer and product could be represented by a sales relationship.
• 4. Conceptual data modeling: Based on entities and their relationships, a graphical, clear model is created that reflects the business logic.
• 5. Logical data modeling: The conceptual model is converted into tables and relationships, establishing the logical structure for the later database.
• 6. Physical data modeling: Concrete database structures are defined with data types, indexes, and storage formats designed for performance.
• 7. Normalization and optimization: The data structure is simplified, redundancies are minimized, and data integrity is ensured in order to optimize storage space and speed up queries.
• 8. Validation and feedback: The model is tested and adjusted if necessary to ensure that it meets real-world requirements. Feedback from stakeholders and end users is also gathered in the process.

This systematic and iterative approach to data modeling ensures that companies have a high-quality, consistent data foundation that delivers reliable analysis results and thus enables well-founded decisions.

Application examples: Where is data modeling used?

Data modeling creates the foundation for consistent analyses in almost all areas of a company. Typical fields of application range from controlling to logistics.

Summary

Data modeling is the structural foundation of every high-performance BI and database project: it defines how data is organized, stored, and interconnected – and thereby determines the quality of all downstream analyses and reports. With DeltaMaster and its consulting expertise, Bissantz turns data modeling into a strategic lever for consistent data flows, reliable KPI logic, and well-founded business decisions.