The Kimball methodology of building a data warehouse is often called a bottom-up approach. This label, along with its associated connotations, is misleading, and misunderstandings about our approach are proliferating. It’s time to set the record straight: Although our iterative development and deployment techniques may superficially suggest a bottom-up methodology, a closer look reveals a […]

Intelligent Enterprise published an article entitled “The Bottom-Up Misnomer” in their latest September 17th issue. I wrote this article with Ralph several months ago. While it’s been working through the publishing pipeline, an industry newsletter has sparked more bottom-up versus top-down discussion. Everyone seems to feel qualified to explain the Kimball approach. Unfortunately, they sometimes spread misunderstandings and continue […]

As data warehouse designers, you know how important a business executive sponsor is to your initiative. After focusing on data warehousing for the past two decades, I’m convinced that strong business sponsorship is the leading make-or-break indicator of data warehouse success. Having the so-called right sponsor can overcome a multitude of shortcomings, such as difficulties […]

When developing a dimensional model, we often encounter miscellaneous indicators and flags that don’t logically belong to the core dimension tables. These unattached attributes are usually too valuable to ignore or exclude. Designers sometimes want to treat them as facts (supposed textual facts) or clutter the design with numerous small dimensional tables. A third, less obvious but preferable, solution […]

As the data warehouse market matures, the cause of data warehouse “pain” (otherwise known as vendor growth opportunity) within the IT organization is bound to evolve. Vendors promote centralization as a miracle elixir to treat data warehouse ailments. They claim it spins independent, disparate data marts into gold by reducing administrative costs and improving performance. […]

Many of you are already familiar with the data warehouse bus architecture and matrix given their central role in building architected data marts. The corresponding bus matrix identifies the key business processes of an organization, along with their associated dimensions. Business processes (typically corresponding to major source systems) are listed as matrix rows, while dimensions appear as matrix […]

Contrary to William Shakespeare and some data warehouse industry pundits, that’s NOT the question. In this article, we discuss an issue faced by maturing data mart/warehouse environments. While some organizations are newcomers to the data warehouse party, others have been at this for quite a while. As the market matures, the cause of data warehouse “pain” within the […]

The acronym, SCD, is a keyword in a dimensional modeler’s vernacular. As most of you know, SCD is short-hand for slowly changing dimensions. There are several well-documented techniques for dealing with slowly changing dimension attributes. Briefly, with SCD Type 1, the attribute value is overwritten with the new value, obliterating the historical attribute values. For example, when the […]

By Margy Ross and Bob Becker During the past year, we’ve repeatedly observed a pattern with maturing data warehouses. Despite significant effort and investment, some data warehouses have fallen off course. Project teams (or their user communities) are dissatisfied with the warehouse deliverables – the data’s too confusing, it’s not consistent, queries are too slow, etc. Teams have […]

One of the most prevalent fallacies in our industry is that data marts are defined by business department. We’ve seen countless data warehouse architecture diagrams with boxes labeled “Marketing Data Mart,” “Sales Data Mart,” and “Finance Data Mart.” After reviewing business requirements from these departments, you’d inevitably learn that all three organizations want the same core information, such as […]