Business and Technical Data Model Views

A Comprehensive Guide for Your Data Journey

Data modeling is a crucial aspect of modern businesses, as it helps organizations understand, structure, and manage the information they use daily. In this blog post, we will explore two critical types of data model views – business data model views and technical data model views. We will also discuss the steps to connect these two views and the roles or subject matter experts needed to develop them. Let’s dive into the world of data modeling and enhance your data journey!

Business and Technical Data Model Views

Business and Technical Data Model Views

Business Data Model Views – Building a Common Understanding

A business data model view is an abstract, high-level representation of the data used within a specific business domain. Its purpose is to provide a collective understanding of the information used within an organization, facilitating communication and collaboration among different stakeholders, such as business users, IT professionals, and data analysts.

Business data model views typically include entities (objects), attributes (properties or characteristics), and relationships between entities, describing the structure, meaning, and context of the data relevant to a specific business domain or process. The goal is to improve the accuracy, consistency, and completeness of the data within an organization, reduce redundancy and inconsistency, and enhance the overall data quality.

Technical Data Model Views – Supporting System Implementation

A technical data model view, on the other hand, is a detailed representation of the data and information used within a specific software application or technical system. It is a low-level, technical view of the data, designed to support the system’s implementation and provide guidance for database design, programming, and other technical tasks.

Technical data model views typically consist of tables (or other data structures), columns, data types, relationships between tables, and constraints. They describe the physical storage of the data, the algorithms used to manipulate it, and the rules governing its use. Creating a technical data model view aims to provide a precise and unambiguous description of the data used within a software application or technical system, ensuring efficient organization, optimized performance, and data integrity maintenance.

Connecting Business and Technical Data Model Views

To connect a business data model view to a technical data model view, a process called mapping is required. Mapping involves translating high-level business concepts and relationships into a specific technical implementation. The steps to connect these two views include identifying the entities, attributes, and relationships in the business data model view, mapping them to corresponding elements in the technical data model view, and documenting the mapping between the two models. By connecting these views, you can ensure that the software application or technical system accurately reflects the business domain requirements, and that the data is organized efficiently and maintained with integrity.

Roles and Subject Matter Experts in Developing Data Model Views

Developing both business and technical data model views requires input from various roles or subject matter experts. For a business data model view, key roles include business analysts, subject matter experts (SMEs), data architects, IT professionals, and data stewards. These experts ensure that the business data model view accurately reflects the needs and requirements of the business domain.

For a technical data model view, the roles involved include database architects, database administrators, developers, quality assurance professionals, and security professionals. These experts ensure that the technical data model view accurately reflects the software application or technical system’s needs and requirements, resulting in an optimized database design for performance, scalability, and security.


Understanding and effectively utilizing business and technical data model views can significantly improve your organization’s data management and decision-making processes. By following the steps outlined in this blog post and engaging the necessary subject matter experts, you can create data model views that accurately reflect your business domain requirements and support your software applications or technical systems. So, embark on your data journey with confidence, knowing that you have the knowledge and tools needed to create robust data models that drive your organization’s success.

As you continue to refine and maintain your data model views, remember to keep communication and collaboration at the forefront of your efforts. Engaging stakeholders from various departments and backgrounds can provide valuable insights and help ensure that your data models remain accurate, relevant, and useful over time.

Here at datatunnel, we’re committed to empowering you on your data journey, providing insights and guidance on best practices in data modeling and management. Stay tuned for more blog posts on various aspects of data modeling, analytics, and strategy to help you unlock your organization’s full potential. Happy data modeling!


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