Agile Data

The Agile Data (AD) method defines a collection of strategies that IT professionals can apply in a wide variety of situations to work together effectively on the data aspects of software systems. This isn’t to say that AD is a “one size fits all” method. Instead, consider AD as a collection of techniques and philosophies that enable IT professionals within your organization to work together effectively when it comes to the data aspects of software-based systems. Practices for evolutionary/agile database development are

• Database refactoring. A database refactoring is a small change to your database schema that improves its design without changing its semantics (e.g., you don’t add anything nor do you break anything). The process of database refactoring is the evolutionary improvement of your database schema without changing its functionality so as to improve your ability to support the new needs of your customers.

• Agile data modeling. With an evolutionary approach to data modeling you model the data aspects of a system iteratively and incrementally. With an agile approach you do so in a highly collaborative manner. You envision the initial requirements and architecture early in the project at a high-level and then model storm the details on a just-in-time (JIT) basis when and if they’re needed.

• Database regression testing. You should ensure that your database schema actually meets the requirements for it, and the best way to do that is via testing. Minimally this should be done via regression testing, but better yet via test-driven development (TDD). Agile testing provides the concrete feedback that you need to ensure data quality.

• Configuration management. Your data models, database tests, test data, and so on are important project artifacts that should be configuration managed just like any other artifact.

• Developer sandboxes. Developers need their own working environments, called sandboxes, where they can modify the portion of the system they are building and get it working before they integrate their work with that of their teammates.

• Development guidelines. Having a common, usable set of development standards that are easy to understand and to comply with can greatly improve the quality of the systems that you develop. These guidelines may include, but not be limited to, programming guidelines, modeling style guidelines, data naming conventions, and user interface conventions (including report design conventions).