What Is the Kimball Methodology?
The Kimball methodology is a widely adopted approach to designing data warehouses, developed by Ralph Kimball and his colleagues. Its central idea is dimensional modeling: organizing data into fact tables that hold measurable events and dimension tables that hold descriptive context, arranged in a star schema. The goal is to make data easy for business users to understand and fast to query, rather than optimizing purely for storage efficiency. Decades after it was introduced, dimensional modeling remains the foundation of most analytics data models, including the semantic models in tools like Power BI.
The methodology is sometimes contrasted with the Inmon approach, named for Bill Inmon, which favors a more normalized, enterprise-wide data warehouse built first, with dimensional marts derived from it. Kimball’s approach builds dimensional models directly, organized around business processes, and connects them through shared dimensions. In practice, most modern analytics leans heavily on Kimball’s dimensional modeling, whatever the broader architecture.
Core Ideas of the Kimball Methodology
Dimensional modeling. Data is organized into facts (the measurable events, such as sales or transactions) and dimensions (the descriptive context, such as customer, product, and date). This separation is what makes the model intuitive: facts hold the numbers, dimensions hold the things you slice them by.
The star schema. A fact table sits at the center, surrounded by its dimension tables, forming a star shape. This structure is simple to understand and efficient to query, which is why it became the standard for analytics models.
Conformed dimensions. Shared dimensions used consistently across multiple fact tables, so different business processes can be analyzed together. A conformed customer dimension lets sales and support data be combined along the same customers.
Business-process orientation. Models are built around the business processes that generate data, such as orders, shipments, or payments, which keeps them grounded in how the business actually works.
Why the Kimball Methodology Matters
The lasting influence of the Kimball methodology comes from its focus on the business user. By organizing data the way people think about it, into events and the things that describe them, dimensional models are far easier to navigate than the normalized structures of transactional systems. This is what makes self-service analytics possible: a business user can understand a star schema and build a report from it without deep technical knowledge.
The approach also performs well. Star schemas are efficient for the kinds of queries analytics requires, aggregating measures across dimensions, which is why analytics engines, including the in-memory model behind Power BI, are optimized for them. Following dimensional modeling principles tends to produce models that are both understandable and fast.
The Kimball Methodology in ERP Environments
Applying dimensional modeling to ERP data is where much of the real work of analytics happens. ERP systems store data in highly normalized structures built for transaction processing, the opposite of a star schema. Turning that into clean fact and dimension tables, deciding the grain of each fact, building conformed dimensions for customer, product, and account, is the core of modeling ERP data for analytics.
This is detailed work, and it follows Kimball’s principles whether or not it is named as such. For organizations running ERPs like JD Edwards, NetSuite, or Vista, building these dimensional models from scratch is substantial, which is why pre-built models that already apply dimensional modeling to each ERP’s structure save so much effort.
Common Challenges and Best Practices
- Define the grain first. Kimball’s own guidance: decide what one row of a fact table represents before anything else. The grain drives every other modeling decision.
- Use conformed dimensions. Shared dimensions across fact tables are what let different processes be analyzed together. Build them deliberately.
- Model around business processes. Organize fact tables around the processes that generate data, which keeps the model aligned with how the business works.
- Favor the star schema. Resist over-normalizing analytics models. The simplicity of the star schema is a feature, not a compromise.
- Apply it to ERP data. Dimensional modeling is the proven way to turn normalized ERP data into analytics-ready structures. Pre-built models apply it for each ERP.
Frequently Asked Questions
What is the difference between the Kimball and Inmon methodologies?
Kimball builds dimensional models (star schemas) directly, organized around business processes and connected by conformed dimensions. Inmon builds a normalized, enterprise-wide data warehouse first, then derives dimensional marts from it. Kimball is bottom-up and business-process oriented; Inmon is top-down and enterprise oriented. Most modern analytics relies heavily on Kimball’s dimensional modeling.
Is the Kimball methodology still relevant?
Yes. Dimensional modeling remains the foundation of most analytics data models, including the semantic models in tools like Power BI. The technology has evolved, but the principles of facts, dimensions, and star schemas are as widely used as ever.
What is a star schema in the Kimball methodology?
A star schema places a fact table at the center, surrounded by the dimension tables that describe its events. It is the core structure of dimensional modeling, simple for business users to understand and efficient for analytical queries.
The Kimball Methodology and QuickLaunch’s Approach
QuickLaunch Analytics applies dimensional modeling to ERP data in its pre-built semantic models, turning the normalized structures of JD Edwards, NetSuite, Vista, and other systems into clean fact and dimension tables with conformed dimensions. Following these proven principles produces models that are both easy for business users to navigate and fast to query, on a foundation refined across 250+ enterprise implementations.
