Why Scrum-like teams are more suitable for solving mysteries than puzzles

February 21, 2011 at 1:58 pm | Posted in Blogroll, Scrum and agile, Software Engineering | Leave a comment

Scrum is one way of developing software which is becoming more and more popular. Scrum teams are characterized by being cross-functional and self-organizing. Because of its increasing popularity, sometimes the pendulum tends to swing excessively in its favor and scrum-like teams are being used in situations where they may not be suitable, both within software development and team formations in general. It may be useful to understand why and when scrum-like teams work and what the challenges are so that we can make best use of them.

Teams are formed for a variety of reasons. In some cases the problems to be solved are straightforward; there are ready solutions which are known to the team or with little effort can be known based on others’ experience. In other cases, the problems are complex and do not have a ready solution. Past experience may or may not work for the current situation. Stephen Denning in his book “Radical management” brings this out nicely by relating this to puzzles and mysteries. In his words “A puzzle may be difficult to solve, but if you have enough analytical skill and enough information, you can find the right answer. By contrast, a mystery doesn’t have a right answer. Mysteries require judgments and the assessment of uncertainty. With a mystery you could go on endlessly analyzing the problem, searching for a better solution, and never be sure that we had arrived at the right answer until after we had seen what actually happened. A mystery is essentially unique. It doesn’t repeat.” and suggests that mysteries like “discovering ways to delight clients, is best solved by a cognitively diverse group of people that is given responsibility for solving problems, self-organizes and works together to solve it.”

He explains further by an example, “In business, increasing efficiency and productivity requires information which can be gathered. It requires analysis of vast amount of data which can be done using mathematical optimization techniques. It requires good systems which can be setup. So it essentially involves puzzles which can be solved. On the other hand, we don’t even understand what will or will not delight clients until after we have seen how they react. It is a mystery. Asking people what they want in interviews or focus groups has turned out to be of limited help. That’s because the people themselves often don’t know what will delight them until they have the actual experience.”

Solving puzzles may need a group of people with different abilities to tackle different aspects of the puzzle. But solving mystery requires people with diverse perspectives so that they can see the problem from different angles. This is fundamental difference. In the former case, a team leader can direct each member knowing his or her abilities; self-organization is not necessary. While in the latter case, self-organization is essential because it uses different perceptions rather than different abilities and frequent sharing of different viewpoints builds up to help the group come up with a much better answer than what the experts would have come up with. So to be most effective, scrum-like team members need to provide not only their different abilities but also be aware of each other’s perspective and see the problem in totality.

Scrum-like teams have a great potential but there are real challenges too. Management needs to give up power to allow the team greater autonomy in the interest of better overall output for the org. Secondly; it is not always easy for even the team members to gel well. This is compounded by the fact that the success or failure is collective and the team members have to go beyond their narrow interests. Related aspect is the way compensation is managed. It is difficult to separate each member’s contribution because the success or failure is collective. If these factors are understood and accepted, scrum-like teams provide a good mechanism to come up with better results in complex situations.

I would really appreciate any thoughts / experiences from you that either support or challenge what is said above.


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