Data modeling – an indispensable basis for precise data analysis

What is data modeling?

Data modeling is creating a structured data model that defines the logical relationships and attributes of the data in a system. It is used to organize and structure data to be stored, retrieved and analyzed efficiently. Good data modeling is therefore crucial for integrating data from various sources into a BI system and preparing it for analyses and reports.

Types of data models

The design of databases begins at a high level of abstraction and then becomes increasingly concrete. The data model is developed from a conceptual model to a logical and then physical model.

• Conceptual data model: A conceptual data model has a high level of abstraction that describes the most important entities and their relationships without worrying about technical details. It is used to understand and document business requirements.
• Logical data model: A logical data model further details the conceptual model by defining specific attributes as well as primary and foreign keys. It is still technology-independent but provides a more accurate representation of the data structure.
• Physical data model: A physical data model, on the other hand, describes the concrete implementation of the logical model in a specific database management system (DBMS). It takes into account technical aspects such as data types, indices, partitioning, and performance optimizations.

The process of data modeling

To model data as optimally as possible, an iterative process is often followed, the workflow of which usually looks like this:

1. Identify entities: First of all, data is assigned to specific business objects, known as entities. An entity therefore represents an identifiable object or concept in a data model that stores relevant information. This could be customers, products, or sales, for example.
2. Identify attributes: Each entity can be distinguished from others because it has one or more unique attributes. These are properties and characteristics, such as name, customer number, address or date. These attributes are assigned to the entities.
3. Define relationships: Next, primary and foreign keys are used to define how the individual entities and attributes relate to each other, e.g. which sales were made by which customers. These relationships can take many different forms, such as one-to-one, one-to-many, or many-to-many relationships.
   An attribute or a combination of attributes that uniquely identifies an entity is called a primary key. The value of the primary key must be unique. For example, a customer number can be the primary key of the entity “customer”. A foreign key, on the other hand, is an attribute that establishes a relationship between two entities by referring to the primary key of another entity. For example, a foreign key of the entity “order” could refer to the customer number to indicate which customer placed the order.

Data modeling techniques and methods

Data modeling techniques define the logical structure of data and determine how it is stored, organized, and retrieved. The three most important types are the relational, the dimensional and the entity-relationship data model. Other less frequently used models are the hierarchical, object-oriented, network, and multivalue model.

1. Relational data model

The relational data model is the oldest of the three data modeling techniques but is still widely used. It stores data in data records with fixed formats and tables with rows and columns. This data model has two elements:

• Key figures: Numerical values such as quantities and yields are used for mathematical calculations, such as totals or average.
• Dimensions: In contrast, dimensions are text or numeric values that contain descriptions or locations and are not used for calculations.
  These elements are linked or related to each other with keys.

2. Dimensional data model

Dimensional models are more flexible and focused on contextual data. They are therefore ideal for online queries and data warehousing, as is often used for BI systems. Here are the most important elements facts and dimensions:

• Facts: Facts are important data elements such as transaction quantities.
• Dimensions: Facts are linked to reference information such as product ID or transaction date. This reference information is called dimensions.

In dimensional models, the fact tables are a primary table. This structure enables fast queries as the data for a specific activity is stored together. However, missing relationship links can make it difficult to use the data and the data structure is tied to the business function that generates and uses the data. This can make it difficult to combine data from different systems.
Two particularly frequently used dimensional modeling schemas are the star schema and the snowflake schema. In a star schema, a central fact table is directly linked to several dimension tables. This structure is simple and efficient for queries, as all dimensions have only one connection to the fact table. This makes the schema easy to understand and suitable for simple analyses and reports. The snowflake schema is an extension of the star schema in which dimensions are further normalized, i.e. redundancies are minimized. To do this, dimension tables are divided into several linked tables, which makes the structure more complex. This can improve data integrity, but also increase query complexity.

3. Entity-Relationship Model (ER Model)

The ER model visualizes business data structures graphically. It uses symbols for entities, activities, and functions as well as lines for relationships, connections, and dependencies. An ER model serves as the basis for the structure of relational databases, with each line containing an entity and the fields containing attributes. The individual tables are in turn linked by keys.

Conclusion

Data modeling forms the basis for all BI activities. Well-designed data modeling enables data to be consistent, accurate, and easily accessible. It improves data quality and integrity and ensures that the data meets business requirements. The efficiently organized data can be easily retrieved and analyzed so that it can be optimally used for data-based business decisions. So, despite the challenges and complexity associated with data modeling, it is essential to reap the full benefits of BI initiatives.
However, we at Parm AG are aware that the shortage of specialists and the high complexity of data modeling are a major challenge for many companies. We are therefore happy to support you in this task. From the creation of a concept to data modeling and subsequent report design, our team of experts will take care of the data modeling for you so that you can concentrate on your core business while we create the basis for your BI success. Our OLAP-supported solutions ensure that fast, complex queries, different ways of presenting the data, and individual and trend analyses are also possible.

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