Business intelligence (BI) is not physics, but there is at least one similarity in the disciplines. BI practitioners look for the stories in the data by studying the visible tracks left in the muddle of source systems.
Browsing the Web recently, I was struck by the opening lines of an article on Freeman Dyson, the frog prince of physics and its import on business intelligence.
Im not against the first group, but they take an exalted view of science. Frogs typically enjoy exploring things locally and developing skills, said Dyson in an 1999 Salon.com article by Kristi Coale.
With remarkable clarity, I saw immediately what most data warehouse projects lacked brogsamalgams of birds and frogs.
Best of Both Worlds
Obviously, I am talking about people here. A brog lives in two worlds and segues seamlessly between them: the birds view of business objectives, and the frogs mud-plowing efforts to make those objectives a reality.
A data warehouse project staffed by a small team of brogs, helping with, or taking on multiple roles within the data warehouse lifecycle, has a better chance of succeeding than a project staffed with disparate people, each playing a singular bird- or frog-like role of project manager, business analyst, data modeler, DBA, ETL designer, ETL developer, end user application designer, and developer.
The ability to flit between business needs and data minutiae is finely honed. It comes from a broad and deep understanding of both transaction processing and the full data warehouse lifecycle. Although the methodology for designing and developing a data warehouse is quite different from the one used in a transactional application, it is the accumulated memory of both worlds that forge a brog.
An experienced trekker of business transactions understands the trip down the business lifecycle and its many legs. She can recognize the questions the business wants answered, and where the answers can be found.
Likewise, the experience of acquiring, cleaning, and moving the right data into a data warehouse within ever-narrowing timeframes creates a keen awareness of the need for a sensibly scoped undertaking, when and how to deal with dirty data, and the value of navigable and responsive query applications.
It is this accumulated memory that gives a brog the peculiar ability to see things not readily visible to others. She can visualize the patterns of user queries, the dimensional models needed to facilitate those queries and the constraints imposed by the realities of the data.