Project managers working on large healthcare data warehousing projects have multiple methodology options at their disposal: agile, iterative, waterfall, hybrid. The challenge often becomes determining which methodology is best suited for the specific type of project.
This is where using the Cynefin framework can provide guidance for healthcare data projects. The Cynefin framework is used to help project managers, policy makers, and others reach decisions on how to execute based upon how well you know your end result. The framework consists of five decision making contexts of domains – simple, complicated, complex, chaotic and disorder – all of which provide guidance and direction for managers to identify how they will need to proceed with execution.
In “Simple” projects the requirements never change and the scope and execution paths are very well defined. Simple projects represent the “known knowns.” There are best practices and the relationship between cause and effect is clear. Change is not an option.
In “Complicated” projects the scope and requirements are well known however change is possible. The execution path is also well defined but flexibility may be required. The relationship between cause and effect requires analysis or expertise; there is a range of right answers. You need to assess the facts, analyze, and apply the appropriate good operating practice.
In “Complex” projects the scope and requirements are flexible and experimentation is required to determine what the end result needs to be. The execution also needs to be flexible as well to accommodate the changing requirements. In Complex projects the cause and effect can only be deduced in retrospect.
In “Chaotic” projects the scope and requirements are non-existent or ambiguous to the point of being non-existent. Execution will consist of build “something” and obtain feedback. In Chaotic projects cause and effect are unclear. Any action is the first and only way to respond appropriately.
The Cynefin framework quadrant you are in determines the proper project methodology to use to best implement your healthcare data project. The picture below highlights the suggested methodology:
In future blog posts, I will explore what and why each methodology is best suited for each type of project defined by the Cynefin framework.
