Factless fact table are“fact tables that have no facts but captures the many-to-many relationship between dimension keys.” We’ve previously discussed factless fact tables to represent events or coverage information. An event-based factless fact table is student attendance information; the grain of the fact table is one row per student each day. A typical coverage factless fact […]

Accumulating snapshots are one of the three fundamental types of fact tables. We often state that accumulating snapshot fact tables are appropriate for predictable workflows with well-established milestones. They typically have five to ten key milestone dates representing the workflow/pipeline start, completion, and the key event dates in between. Our students and clients sometimes ask […]

Design Tip #43 Dealing with Nulls in the Dimensional Model describes two cases where null values should be avoided in a dimensional model; in these situations, we recommend using default values rather than nulls. This Design Tip provides guidance for selecting meaningful, verbose defaults. Handling Null Foreign Keys in Fact Tables The first scenario where […]

If you’re a long time reader of the Kimball articles, Design Tips and books, you know we feel strongly about the importance of understanding the business’s data and analytic requirements. Suffice it to say that we expect more than merely inventorying the existing reports and data files; we encourage you to immerse yourself in the business community […]

The Kimball Group has frequently written about the importance of focusing on business requirements as the foundation for a successful DW/BI implementation. Design Tip #110 provides a crisp set of dos and don’ts for gathering requirements. However, some organizations find it difficult to land on the right level of detail when documenting the requirements, and […]

Over the years, we’ve described common dimensional modeling mistakes, such as our October ’03 “Fistful of Flaws” article in Intelligent Enterprise magazine. And we’ve recommended dimensional modeling best practices countless times; our May ’09 “Kimball’s Ten Rules of Dimensional Modeling” article has been widely read. While we’ve identified frequently-observed errors and suggested patterns, we haven’t […]

Certain industries need the ability to look at a backlog of work, and project that backlog into the future for planning purposes. The classic example is a large services organization with multi-month or multiyear contracts representing a large sum of future dollars to be earned and/or hours to be worked. Construction companies, law firms and other organizations with […]

A student in a recent Data Warehouse Lifecycle in Depth class asked me for an overview of the Kimball Lifecycle approach to share with their manager. Confident that we’d published an executive summary, I was happy to oblige. Much to my surprise, our only published Lifecycle overview was a chapter in a Toolkit book, so this Design Tip […]

Successful data warehouse and business intelligence solutions provide value by helping the business identify opportunities or address challenges. Obviously, it’s risky business for the DW/BI team to attempt delivering on this promise without understanding the business and its requirements. This Design Tip covers basic guidelines for effectively determining the business’s wants and needs. First, start by properly preparing […]

There is a tendency for data warehouse project teams to jump immediately into implementation tasks as the dimensional data model design is finalized. But we’d like to remind you that you’re not quite done when you think you might be. The last major design activity that needs to be completed is a review and validation of the dimensional […]