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Fables and Facts

Do you know the difference between dimensional modeling truth and fiction?

According to Merriam-Webster, fables are fictitious statements. Unfortunately, fables about dimensional modeling circulate throughout our industry. These false claims and assertions are a distraction, especially if you're trying to align a team. In this column, we'll describe the root misunderstandings that perpetuate these myths so you understand why they're as unfounded as fairy tales about two-headed creatures.

Not all Dimensional Models are Created Equal

We review a lot of dimensional models in our work. They often illustrate best practice design principles from our Toolkits and columns. However, not all supposed dimensional models are designed appropriately. Some blatantly violate core dimensional modeling tenets. Given the abysmal sample star schemas found in seemingly authoritative books and training presentations, this comes as no surprise. However, you shouldn't lump all dimensional models into a "bad" category based on misguided imposters.

Most of the fabled assertions are rooted in several basic mistakes regarding dimensional modeling best practices. Dimensional modeling can't be blamed if its fundamental concepts aren't embraced. Likewise, criticisms lobbed by individuals who don't understand its key premises need to be taken with a grain of salt. Once we clarify these misunderstandings, you'll be prepared to personally distinguish fables from facts.

Focus on Measurement Processes, not Departmental Reports

We advocate a four-step approach for designing dimensional models. The first step is to identify the business process, followed by declaring the grain, then selecting the dimensions and facts. Nowhere do we recommend specifying the business's top 10 report layouts or queries.

If requirements are gathered by focusing exclusively on report or query templates, you're susceptible to modeling data to produce a specific report, rather than capturing the key metrics and related dimensions for analysis. Obviously, it's important to consider business usage when designing dimensional models. The dimension attributes must support the BI environment's filtering and labeling requirements. Robust dimension attributes translate into nearly endless analytic slicing-and-dicing combinations. However, don't blindly focus on a top-10 list in isolation because priorities and "hot" reports will inevitably evolve.

Instead of concentrating on specific reports or departmental needs in a vacuum, we suggest focusing the dimensional design on the most critical performance measurement process. In doing so, you can put the following fables to rest.

FABLE: Data marts are built to address a specific business report or application. When the business needs a new report, another star schema is built.

FACT: Data marts with dimensional models should be built around physical measurement processes or events. A fact table row is created when a measurement occurs. The associated dimension attributes reflect contextual characteristics and hierarchies. If the business identifies a new report based on the same measurement process, there's no need to build a new mart, model, or schema. Measurement processes are relatively stable in most organizations; the analytics performed against these metrics are more fluid.

FABLE: Dimensional models are departmental solutions. When a different department needs access to the data, a new star schema is built and labeled with the department's vocabulary. Data marts require multiple extracts from the same source data repeatedly.

FACT: Dimensional models shouldn't be departmentally bound. A fact table representing a fundamental measurement process need only have one physical instance that's shared across business functions or departments. There's no reason to create multiple extracts from the same source. Metrics resulting from the invoicing process, for example, are made available in a single dimensional model for access across the enterprise; there's no reason to replicate invoice performance metrics in separate departmental solutions for finance, marketing, and sales. Even if these departmental solutions were sourced from the same repository, they likely use similar, but slightly different naming conventions, definitions, and business rules, defeating the promise of a single version of the truth. The departmental approach is highly vulnerable to inconsistent, nonintegrated point solutions. We've never advocated this approach.

FABLE: You can't incorporate new data sources without rebuilding the original star schema or creating separate fact tables or data marts.

FACT: If the new data source is another capture system for an existing measurement process in the BI environment, then the new data can be gracefully combined with the original data without altering any existing reporting applications, presuming the granularity is the same. If the new data source is at a different grain representing a new measurement process, then a new fact table must be created. This has nothing to do with dimensional modeling. Any data representation would create a new entity when a new table with different keys is introduced.

FABLE: With dimensional modeling, the fact table is forced to a single grain that is inflexible.

FACT: Having the discipline to create fact tables with a single level of detail assures that measurements aren't inappropriately double counted. A table with mixed-grain facts can only be queried by a custom application knowledgeable about the varying levels of detail, effectively ruling out ad hoc exploration. If measurements naturally exist at different grains, then the most foolproof design establishes a fact table for each level. Far from being inflexible, this approach protects existing applications from breaking or recoding as changes occur.

Begin with Atomic Details, Not Summarized Data

Some claim that data marts with dimensional models are intended for managerial, strategic analysis and, therefore, should be populated with summarized data, not operational details. We strongly disagree. Dimensional models should be populated with atomic data so business users can ask very precise questions. Even if users don't care about the details of a single transaction, their "question of the moment" involves summarizing the details in unpredictable ways. Database administrators may presummarize some information, either physically or via materialized views, to avoid on-the-fly summarization with every query. However, these aggregates are performance-tuning complements to the atomic level, not replacements. If you create dimensional models with atomic details, the following fables are nonissues.

FABLE: Star schemas and dimensional models presuppose the business question. When the requirements change, the model must be modified.

FACT: When you presummarize information, you've presupposed the business question. However, dimensional models with atomic data are independent of the business question as users can roll up or drill down ad infinitum. They answer new, previously unspecified questions without database changes. Obviously, business requirements are key input to any DW/BI initiative.

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