Common Mistakes and Misunderstandings

or, How to Fail with Evo

Error: Adoption mistakes Lack of management support. Lack of training in concepts and methods. Lack of clear quantified management objectives as the basis for evolving towards Evo methods. Lack of clarity about the management objectives of using the method and how to measure these improvements in practice. Lack of a good successful pilot project to prove it works in your environment. Lack of dramatic motivation to change from older methods.

Error: Lack of focus on results Self-explanatory.

Error: Giving up or not believing short iterations are possible Giving up too easily when managers or engineers claim they cannot find small early steps (they need training, motivation and help). Giving up too early and falling back on old habits.

Error: Lack of management encouragement When a team starts delivering something of value every short iteration, that's often a revolutionary event. Management needs to praise and encourage this result, rather than take it for granted.

Error: Failing to use value/cost priority Not choosing solutions based on highest value-to-cost.

Error: Customers not involved Evo is customer and results-driven; they need to participate in providing feedback on the results of evolutionary deliveries, and in steering the next iteration.

Error: No measurements It is a mistake to avoid regular measurement of the impact of delivered solutions. Frequent numeric measurement is a significant shift for many managers, but central to Evo.

Error: Iterations too long Evo frontroom iterations should be 2 5% of total project time, with a lower bound of one or two weeks.

Error: Each iteration does not end in a delivery Evo is about evolutionary delivery on a "weekly" basis to real stakeholders for useful results, even when backroom development may take months.

Error: Predictive planning It is a misunderstanding to create, at the start of the project, a believable plan laying out exactly how many iterations there will be for a long project, their lengths, and what will occur in each. This is contrasted with Evo or adaptive planning. The Evo team and customer plans the next iteration, and then planning adapts iteration by iteration, based on measurement and feedback.