How do you choose between Kimball and Inmon for your data warehouse design?

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Data warehousing is a crucial component of any data-driven organization, as it enables the integration, analysis, and reporting of data from various sources. However, designing a data warehouse is not a trivial task, and there are different approaches and methodologies to consider. Two of the most popular and influential ones are the Kimball and Inmon methods, which have different philosophies, architectures, and implications for data modeling. In this article, you will learn the main differences between Kimball and Inmon, the pros and cons of each approach, and how to choose the best one for your data warehouse project.

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1 Kimball method

The Kimball method, also known as the dimensional approach, is based on the idea of creating a single, centralized data warehouse that consists of fact tables and dimension tables. Fact tables store the quantitative data, such as sales, transactions, or metrics, while dimension tables store the descriptive data, such as customers, products, or dates. The fact and dimension tables are related by foreign keys, forming a star or snowflake schema. The Kimball method focuses on delivering fast and user-friendly data analysis, by organizing the data into subject areas or business processes that reflect the needs and questions of the end-users. The Kimball method is suitable for organizations that have stable and well-defined business requirements, that need to perform ad-hoc queries and reports, and that have limited resources and time for data warehouse development.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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While the Kimball method emphasizes speed and user-friendliness, consider how well it aligns with your organization's data governance and security requirements. In scenarios where data privacy and access control are critical, the decentralized nature of Kimball's data marts might require additional layers of security management. Additionally, if your organization anticipates rapid growth or expansion into new business areas, it’s essential to evaluate whether the Kimball approach will scale effectively or if it might require significant re-engineering later.

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2 Inmon method

The Inmon method, also known as the enterprise approach, is based on the idea of creating a normalized data warehouse that serves as a single source of truth for the entire organization. The normalized data warehouse consists of multiple tables that follow the relational database principles, such as avoiding data redundancy, ensuring data integrity, and enforcing referential integrity. The normalized data warehouse is then used to populate multiple data marts, which are subsets of the data warehouse that cater to specific business units or functions. The data marts can be denormalized or dimensional, depending on the analytical needs of the users. The Inmon method focuses on creating a comprehensive and consistent data architecture, by modeling the data according to the business entities and relationships that exist in the real world. The Inmon method is suitable for organizations that have complex and evolving business requirements, that need to perform cross-functional and strategic analysis, and that have sufficient resources and expertise for data warehouse development.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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The Inmon method's focus on creating a centralized, normalized data warehouse offers a strong foundation for organizations with complex data environments. However, it's important to assess how this approach integrates with modern data architecture trends, such as data lakes or cloud-based solutions. Inmon's method may require adaptation to leverage the flexibility and scalability of cloud services, ensuring that your data warehouse can accommodate evolving data sources, including unstructured or semi-structured data.

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3 Pros and cons of Kimball

The Kimball method has several advantages and disadvantages, depending on the context and goals of the data warehouse project. For example, it is easier and faster to implement, more user-friendly and intuitive, and more efficient and performant. However, it can be less flexible and scalable, less comprehensive and holistic, which may lead to data quality and consistency issues or fail to support cross-functional and strategic analysis.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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When considering the pros and cons of the Kimball method, it’s also worth exploring how this approach can be enhanced with modern tools like ETL automation, data virtualization, and real-time data processing. These advancements can mitigate some of the traditional limitations of the Kimball method, such as its scalability issues, by enabling more dynamic data integration and reducing the need for extensive data warehousing resources.

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4 Pros and cons of Inmon

The Inmon method has various advantages and disadvantages, depending on the context and goals of the data warehouse project. Its benefits include being more flexible, comprehensive, and reliable. It is also capable of adapting to new or changing business requirements, capturing the full complexity and diversity of the data sources, and ensuring data quality and consistency. On the other hand, it is more difficult and time-consuming to implement, less user-friendly and intuitive, and less efficient and performant. It requires more data modeling and transformation, presents the data in a complex way, increases the data storage and processing costs, and may degrade query response time.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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For the Inmon method, consider how emerging data management techniques, such as data mesh or data fabric, can complement its strengths. These techniques can provide additional layers of abstraction and governance, helping to manage the complexity of a large, enterprise-wide data warehouse. Additionally, the Inmon approach can be augmented with machine learning models to automate data quality checks and anomaly detection, further enhancing the reliability and consistency of the data warehouse.

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5 How to choose

Choosing between Kimball and Inmon for your data warehouse design is not always a simple task, as it depends on various factors like the scope, purpose, audience, budget, timeline, and quality of your project. There is no one-size-fits-all solution, so you may need to consider a hybrid or customized approach. However, some general guidelines can help you make an informed choice. If your goal is to deliver fast and user-friendly data analysis for specific subject areas or business processes, the Kimball method may be best. If you’re seeking to create a comprehensive and consistent data architecture for the entire organization, then the Inmon method may be more suitable. If your data warehouse project is somewhere in between, you should weigh the trade-offs and benefits of each method to find a balance that meets your needs and expectations.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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In my opinion; Inmon method (normalized data model) want to generate an integrated data in organizational level to consume for several processes; while the kimball method (dimensional data model) want to generate an integrated data in a specific process scope to consume. based in this fact; the design phrase is different for both methods: inmon approach require more integrated and wide scope in the subject aspect to implement it; while kimball approach require facts tables based to implement the process based in the anaytics Another related point; the normalized data model require to use the normal forms to improve it; while the dimensional data model could "evolve" the model based the needs must: star schema to snowflake/galaxy schema.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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In making your decision between Kimball and Inmon, consider hybrid approaches that combine the best of both worlds. For example, you could implement an Inmon-style centralized data warehouse with a core focus on data governance and integrity, while using Kimball-style data marts for specific business units that require quick, flexible access to data. This approach allows for a balance between strategic, cross-functional analysis and agile, department-level insights, providing a more tailored solution that meets diverse business needs.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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I think the data vault data model could be a good option to implement when your data warehouse needs the development agility and integration data fast on the time.

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How do you choose between Kimball and Inmon for your data warehouse design?

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1 Kimball method

2 Inmon method

3 Pros and cons of Kimball

4 Pros and cons of Inmon

5 How to choose

6 Here’s what else to consider

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