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 […]

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 […]

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 […]

Whether you are developing a new dimensional data warehouse or replacing an existing environment, the ETL (extract, transform, load) implementation effort is inevitably on the critical path. Difficult data sources, unclear requirements, data quality problems, changing scope, and other unforeseen problems often conspire to put the squeeze on the ETL development team. It simply may […]

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 […]

This article describes six key decisions that must be made while crafting the ETL architecture for a dimensional data warehouse. These decisions have significant impacts on the upfront and ongoing cost and complexity of the ETL solution and, ultimately, on the success of the overall BI/DW solution. Read on for Kimball Group’s advice on making […]

It’s no secret that the US and global economies are facing difficult times. If the economic pundits are correct, we are now working through the most challenging economic decline of most of our lifetimes. Many of your organizations have already made significant reductions in staffing and spending. The data warehouse/ business intelligence (DW/BI) sector seems […]

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 […]

It’s surprising the number of DW/BI teams that confine the responsibility for designing dimensional models to a single data modeler or perhaps a small team of dedicated data modelers. This is clearly shortsighted. The best dimensional models result from a collaborative team effort. No single individual is likely to have the detailed knowledge of the business requirements and the […]

Delivering consistent data is like reaching the top of Mount Everest for most data warehouse initiatives, and data stewards are the climbers who fearlessly strive toward that goal. Achieving data consistency is a critical objective for most DW/BI programs. Establishing responsibility for data quality and integrity can be extremely difficult in many organizations. Most operational systems effectively capture key […]