Financial analysts crunch numbers. They sift through reams of detailed figures, combining and recombining them looking for the underlying meaning, searching for a simple presentation that brings out what is really important—an understanding that can be the basis of a financial decision.
Effective domain modelers are knowledge crunchers. They take a torrent of information and probe for the relevant trickle. They try one organizing idea after another, searching for the simple view that makes sense of the mass. Many models are tried and rejected or transformed. Success comes in an emerging set of abstract concepts that makes sense of all the detail. This distillation is a rigorous expression of the particular knowledge that has been found most relevant.
Knowledge crunching is not a solitary activity. A team of developers and domain experts collaborate, typically led by developers. Together they draw in information and crunch it into a useful form. The raw material comes from the minds of domain experts, from users of existing systems, from the prior experience of the technical team with a related legacy system or another project in the same domain. It comes in the form of documents written for the project or used in the business, and lots and lots of talk. Early versions or prototypes feed experience back into the team and change interpretations.
In the old waterfall method, the business experts talk to the analysts, and analysts digest and abstract and pass the result along to the programmers, who code the software. This approach fails because it completely lacks feedback. The analysts have full responsibility for creating the model, based only on input from the business experts. They have no opportunity to learn from the programmers or gain experience with early versions of software. Knowledge trickles in one direction, but does not accumulate.
Other projects use an iterative process, but they fail to build up knowledge because they don't abstract. Developers get the experts to describe a desired feature and then they go build it. They show the experts the result and ask what to do next. If the programmers practice refactoring, they can keep the software clean enough to continue extending it, but if programmers are not interested in the domain, they learn only what the application should do, not the principles behind it. Useful software can be built that way, but the project will never arrive at a point where powerful new features unfold as corollaries to older features.
Good programmers will naturally start to abstract and develop a model that can do more work. But when this happens only in a technical setting, without collaboration with domain experts, the concepts are naive. That shallowness of knowledge produces software that does a basic job but lacks a deep connection to the domain expert's way of thinking.
The interaction between team members changes as all members crunch the model together. The constant refinement of the domain model forces the developers to learn the important principles of the business they are assisting, rather than to produce functions mechanically. The domain experts often refine their own understanding by being forced to distill what they know to essentials, and they come to understand the conceptual rigor that software projects require.
All this makes the team members more competent knowledge crunchers. They winnow out the extraneous. They recast the model into an ever more useful form. Because analysts and programmers are feeding into it, it is cleanly organized and abstracted, so it can provide leverage for the implementation. Because the domain experts are feeding into it, the model reflects deep knowledge of the business. The abstractions are true business principles.
As the model improves, it becomes a tool for organizing the information that continues to flow through the project. The model focuses requirements analysis. It intimately interacts with programming and design. And in a virtuous cycle, it deepens team members' in-sight into the domain, letting them see more clearly and leading to further refinement of the model. These models are never perfect; they evolve. They must be practical and useful in making sense of the domain. They must be rigorous enough to make the application simple to implement and understand.