The dimensional model plays four key roles in Business Intelligence:
- The dimensional model is the ideal way define requirements, because it describes how the business is measured
- The dimensional model is ideal for managing scope because it communicates to business people (functionality) and technical people (complexity)
- The dimensional model is ideal as a basis for data mart design because it provides ease of use and high performance
- The dimensional model is ideal as a semantic layer because it communicates in business terms
The dimensional model is best understood as an information model, rather than data model. It describes business activities the same way people do: as a system of measurement. This makes it the ideal form to express information needs, regardless of how information will be stored.
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This representation is valuable because business questions are constantly changing. If you simply state them, you produce a model with limited shelf life. If you model answers to the question of today, you've provided perishable goods.
A dimensional model establishes information requirements that endure, even as questions change. It provides a strong foundation for multiple facets of BI:
- Performance management, including dashboards and scorecards
- Analytic processing, including OLAP and ad hoc analysis
- Reporting, including both enterprise and operational reports
- Advanced analytics, including business analytics, data mining and predictive analytics
All these disciplines center on business metrics. It should be no surprise that when Howard Dresner coined the term Business Intelligence, his definition referenced "facts and fact based systems." It's all about measurement.
Program Roadmap and Project Scope
A dimensional model can be used to describe scope because it communicates to two important audiences.
- Business people: functionality The dimensional model describes the measurement of a business process, reflecting how the process is evaluated by participants and observers. It communicates business capability.
- Technical personnel: level of effort A dimensional model has technical implications: it determines the data sources that must be integrated, how information must be integrated and cleansed, and how queries or reports can be built. In this respect, it communicates level of effort.
These dual perspectives make the dimensional design an ideal centerpiece for managing the roadmap for your BI program. Fully documented and mapped to data sources, a dimensional model can be divided into projects and prioritized. It is a blueprint that can be understood by all interested parties. A simple conformance matrix communicates both intended functionality and technical level of effort for each project.
At the project level, a dimensional design can be used as the basis for progress reporting. It can also serve as a nonambiguous arbiter of change requests. Changes that add data sources or impact grain, for example, are considered out of scope. This is particularly useful for organizations that employ iterative methodologies, but its simplicity makes it easy to reconcile with any development methodology.
The dimensional model is best known as the basis for database design. The term "star schema" is far more widely recognized than "dimensional model" (a fact that influenced the name of my most recent book).
In fact, the dimensional model is the de facto standard for data mart design, and many organizations use it to shape the entire data warehouse. It has an important place in the W.H. Inmon's Corporate Information Factory, Ralph Kimball Dimensional Bus architecture, and even in one-off data marts that lack an enterprise focus.
Implemented in a relational database, the dimensional model becomes known as a star schema or snowflake. Implemented in a multidimensional database, it is known as a cube. These implementations offer numerous benefits. They are:
- Easily understandable by business people
- Extraordinarily flexible from a reporting and analysis perspective
- Adaptable to change
- Capable of very high performance
Presentation and the Semantic Layer
A dimensional representation is the ideal way to present information to business people, regardless of how it is actually stored. It reflects how people think about the business, so it is used to organize the catalog of items they can call on for analysis.
Many business intelligence tools are architected around this concept, allowing a semantic layer to sit between the user and database tables. The elements with which people can frame questions are categorized as facts and dimensions. One need not know what physical data structures lay beneath.
Even the earliest incarnations of the semantic layer leveraged this notion. Many organizations used these tools to impose a dimensional view directly on top of operational data. Today, semantic layers are commonly linked to dimensional data marts.
A dimensional representation of business activity is the starting point for a variety of BI activities:
- Building enterprise reports
- Defining performance dashboards
- Performing ad hoc analysis
- Preparing data for an analytic model
The concept of dimensional presentation is receiving renewed attention as federated solutions promise the construction of virtual solutions rather than physical ones.
I briefly covered these four roles in an interview last year:
- Big Data and Dimensional Modeling (4/20/2012)
Many of these themes have been discussed previously:
- Three Data Warehouse Architectures that Use Star Schema (3/26/2007)
- Drive Warehouse Strategy with a Dimensional Model (7/6/2007)
- What is a Dimensional Model? (4/27/2010)
- The Conformance Matrix (6/5/2012)
Although I've touched on these topics before, I wanted to bring them together in a single article. In the coming months, I will refer back to these concepts as I address common questions about big data, agile BI and federation.
In the mean time, please help support this blog by picking up a copy of my latest book.