Empirical System Development Methodologies

As with defined system development methodologies, empirical systems are used for similar purposes, although the way in which they operate differs. They remain to be the way in which logical constructs in scrum methodology can be computed to form computer data, and create effective systems, but input is the key component, which varies between the two methods.

Empirical System Development Methodologies are essentially raw systems, which require the input of measurements, observation and defining controls. Through this data we are able to construct a process model strictly from information concerning the project under development. Unlike the defined system model, the key difference is the flexibility in the use of the system, as the empirical method requires extensive data collection because it relies entirely on experimentation and information for the model development, as opposed to the utilization of preexisting laws.

The two also differ in terms of the information that is provided to the user during the scrum process model. While theoretical (defined) system development provides information about the internal state of the process as it occurs, the empirical method of modeling processes provides only the information about the portion of the process that can be influenced by control action. This also influences the difference in understanding required for the use of both models.
While the theoretical model encourages fundamental understanding of the internal workings of the process, thanks to the use of fundamental laws based around the method, the empirical model is oblivious to anything related to the internal function of the system, and it treats the process like a “black box”, meaning it is a system where the mechanism is mysterious to the user. This also means that the method does not require detailed knowledge in the way in which it functions, apart from the fact that output data is obtainable in response to input changes. The empirical method also tends to be the only alternative for modeling the behaviour of poorly understood and/or complex processes, unlike the theoretical modeling method which is quite inflexible to various forms of data due to the use of preexisting laws in the system.

The downsides of the empirical model when compared to the theoretical model are the time required to create a properly functioning system and the way in which it requires special methods to create non-linear models. Empirical modeling requires the collection of data and measurements that are later computed into the system. The theoretical model on the other hand relies on predetermined material and energy balances and fundamental laws to determine the model.

As with the defined system methodology, it is not possible to depend on empirical modeling as the only way in which solutions and results can be delivered during a project. Although the logical constructs are useful as a tool in aiding the process, they are unable of delivering critical solutions, as well as creativity and innovative problem solving. Using the computerized systems contributes to the overall process and is a useful form of additional intellectual infrastructure that focuses on efficiency and project management, but is not capable of replacing the analytical thinking of a human being.