The Importance of Data Modelling in Business Intelligence

Have you ever wondered how businesses make sense of all the data they collect? The answer is data modelling!

It is the process of creating a conceptual representation of data, which helps organizations understand and analyze their data in a more meaningful way.

Data modelling involves organizing data into logical structures, such as tables and relationships, to make it easier to understand and work with.

It’s a crucial step in the data management process and helps businesses make informed decisions based on accurate and relevant information. In this article, we will explore the basics of data modelling, its importance and best practices that you can apply.

What is Data Modeling?

Data modeling is the art of creating a visual representation of data systems or their components to show the connections and relationships between data points and structures.

The ultimate goal is to depict the types of data used, stored, and the way it can be organized, grouped, and formatted.

Data models are built to cater to specific business needs, and the rules and requirements are established through input from stakeholders.

The process starts by gathering information about the business and translating it into data structures for a concrete database design.

Data models can be compared to blueprints, roadmaps, or any diagram that simplifies understanding the design.

Data modeling uses standard techniques and schemas that provide a consistent, common, and predictable way of defining and managing data resources throughout an organization and beyond.

Data models are living documents that adapt to changing business needs and play a vital role in supporting business processes, IT architecture, and strategy.

Types of Data Modelling

Designing a database or information system is similar to any other design process, it starts with a general idea and becomes more specific as the design progresses.

Data models can be broadly categorized into three types, each with its level of abstraction. The design process begins with a Conceptual Model, then moves on to a Logical Model, and finally concludes with a Physical Model.

Each type of data model will be discussed in greater detail below:

1. Conceptual Data Models

Conceptual models, also known as domain models, provide a broad overview of the system’s components, organization, and business rules. They’re typically developed during the early stages of gathering project requirements.

These models usually include entity classes, which define the important business entities, their characteristics, constraints, and relationships, as well as security and data integrity requirements.

The notation used in these models is usually straightforward.

2. Logical Data Models

Logical models offer a less abstract representation and provide more detailed information about concepts and relationships within the domain.

These models use formal data modeling notation systems to indicate data attributes, such as data types and lengths, and depict the relationships between entities.

Logical data models do not include any technical system requirements, and this stage is often skipped in agile or DevOps practices.

Logical models are useful in highly procedural implementation environments or projects that are data-driven, such as data warehouse design or reporting system development.

3. Physical Data Models

Physical models are the least abstract and provide a concrete schema for how the data will be stored within a database.

They present a final design that can be implemented as a relational database, complete with associative tables that show relationships between entities and the primary and foreign keys that will be used to maintain those relationships.

Physical data models may also include properties specific to the database management system, such as performance tuning.

Data Modelling Techniques

There are three main data modeling techniques:

1. The Entity-Relationship Diagram (ERD) method, which is used for modeling and designing relational or traditional databases.

2. The Unified Modeling Language (UML) Class Diagrams, a standardized set of notations for modeling and designing information systems.

3. The Data Dictionary technique, which involves creating a tabular definition or representation of data assets.

Data Modelling Process

Data modeling is a detailed process that involves stakeholders evaluating data processing and storage.

Different techniques have their own conventions for representing data, model layout, and conveying business requirements.

All methods provide a formalized workflow that includes a series of tasks to be performed iteratively. These workflows typically include:

1. Identify the entities

Identifying data entities is an important step in data modeling. Data entities are the objects or concepts that are important to the business and need to be represented in the data model.

They are the building blocks of the data model and are used to represent the real-world objects that the system will be working with.

Examples of data entities include customers, orders, products, and employees.

2. Identify key properties of each entity

Identifying key properties of each entity is an important step in data modeling. These properties are the characteristics or attributes that describe the entity and provide information about it.

Examples of key properties include a customer’s name, address, and phone number. Identifying key properties of each entity helps to create a complete and accurate representation of the data.

It also helps in understanding the relationships between the entities and is important for maintaining data integrity and consistency.

3. Identify relationships among entities

Identifying relationships among entities is an essential step in data modeling. Relationships are the connections that exist between different entities and help to describe how they are related to one another.

Examples of relationships include “customer places order,” “employee works for department,” etc.

Identifying relationships among entities helps to create a clear and accurate representation of the data and aids in understanding the relationships between the entities.

4. Map attributes to entities completely

Mapping attributes to entities is a key step in data modeling. It involves assigning the appropriate attributes to the relevant entities.

This helps to create a clear and accurate representation of the data and ensures that the attributes are accurately associated with the correct entities.

This step is important for maintaining data integrity, consistency and for making informed business decisions.

It also helps in designing the physical database by creating tables, columns and keys.

5. Normalization 

Normalization is a technique for organizing data models where numerical keys are assigned to groups of data to represent relationships between them without repeating data. This helps in reducing storage space but can slow down query performance.

6. Finalize and validate the data model

Data modeling is an iterative process that should be repeated and refined as business needs change.

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