"Big data" is pervasive in the headlines nowadays. The entire world is enthralled by the implications of this enormous, constant flow of variably-structured information as well as the great and terrible things that can be and are being done with its power. There are mountains of data in every industry, from manufacturing to finance, retail to security, and the possibilities for what we can do with this information are endless. Big data may be all the buzz, but we must not let this excitement distract us from the reason we want data in the first place: to make better decisions.

Dimensional modeling -- the process of organizing an enterprise's information into facts (the things that we measure) and dimensions (the things that describe the measurements) -- isn't new anymore and it isn't generating big headlines. Great visionaries of data management laid out the approach two decades ago and although it has been refined and expanded in the time since, the basic idea has remained the same. Yet this fairly simple approach to building data marts and data warehouses has continuously proven its incredible value over time.

The concept of a dimensional model is beautiful in its simplicity: organize the data the way the business people imagine it. When a business decision maker asks a question of her data, she usually says, "I want to see such-and-such a measurement by dimension x, dimension y, and dimension z." For example, "I want to see the average sales transaction amount by customer demographic group, by store, and by month." When we build the database along these lines we make it easy for the user to understand and quick for the database engine to answer the question.

There are still major investments being made in dimensional data marts and data warehouses. They haven't lost their relevance and popularity but rather their mindshare. Big data is the sexy new concept and everyone is pushing their money that way, but sometimes your best investment isn't in the next big thing, it's in the proven thing. While companies and executives are focusing on big data, they're often underinvesting in dimensional data warehouses.

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