| Data centric applications, an important effort of software development in large organizations, have been mostly adopting a software methodology, such as a waterfall or Rational Unified Process, as the framework for its development. These methodologies could work on structural, procedural, or object oriented based applications, but fails to capture the dynamics and dimensions of data centric applications. These methodologies were proven to be unsuccessful because they did not take into consideration all of the aspects of the data components and structures. This raises the question of what method should be used in developing those types of applications.;The purpose of this study is to address the essential elements for a successful agile methodology that can effectively address the structure, behavior, and dimensions of a data centric development effort. An example of these dimensions could be the phases that the project goes though, the principles or best practices that are used, or the disciplines that are applied in the project lifecycle.;My assumption is that there is a number of common essential elements used in developing and managing data centric applications and could be combined in a formal Systems Development Lifecycle of data centric application. |
