A second and critical coping mechanism is to look at the relative sizes of the work pieces rather than the time required. To illustrate, in our classes we try the following experiment. we ask for a volunteer to guess the height in inches of a certain fellow student. Invariably, the estimates are all over the waterfront, varying by as much as 2 feet or, in percentages, of the order of 33 percent. We then ask another student to stand next to the first student. we ask the same erring estimators which of the two students is taller. Invariably, the answers are 100 percent correct.
The success in this case illustrates the soundness of the correct use of the story point. In some cases, estimators never understand that estimates are supposed to be made in a relative fashion. That is, some tasks are estimated based on initial guesses. Other tasks are estimated based on the perceived effort required, but relative to the initial guesses. Over time, this particular team in this particular environment in this particular project learns to compensate for reality converging on still wrong but increasingly right guesses. Experience with this approach results in increasingly accurate estimates, even though they are still just that—;estimates.
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Two common problems occur too often in the application of this very effective approach. In some cases, there is a lack of understanding of the importance of the relative concept in the beginning and the approach is never properly applied. In other cases, that concept is initially agreed to but falls by the wayside as the team drifts back to its old, very human habit of trying to produce absolute estimates.
The folly of absolute time estimation is illustrated by the driving analogy. In a perfect setting, such as 4 a.m. on a Sunday morning, driving to work takes, say, 15 minutes. However, an estimate of 10 miles is more useful because it can be placed in the context of the particular time of day and day of the week in which the driving actually winds up occurring. Relative, continuing estimation therefore places the estimate closer to the context in which the work is actually being done.
Because we must adapt to lots of changing circumstances when doing knowledge work, we need to anchor the estimate in something more stable.
Relative Estimation Versus Absolute Estimation
Early absolute estimation is not only folly but also wasteful because the result is based on incomplete information. Relative estimation involves simpler categorization, with evolving estimates continually improved collectively as experience accumulates.
Agile methodologies offer approaches to facilitate the relative-estimation process. For example, consider affinity estimating. On a larger scale, affinity estimating is especially useful when larger numbers of estimates must be made at the same time, but this approach works perfectly well in smaller samples. Here, typically on a whiteboard with sticky notes, items are place into columns according to estimated effort required, effectively removing from the process attempts at precise estimates. Because items of similar size are grouped together, this method spurs healthy discussion about items that are misplaced.
Beyond the basic practice of relative estimation, various agile techniques are employed to direct thinking further away from absolute terms. Matt Barcomb takes the common estimation categories of S, M, L, XL, and XXL a step further by using woodland creatures of varying sizes, with the creature size representing the degree of challenge (Barcomb, 2011). Creatures increase in size as follows:
Such a classification removes the temptation to perform mathematical operations, abstracts the concept of accuracy, and provides much-needed levity. The final dragon category is reserved for those items that are not well understood, are especially challenging, and require special consideration.
A more common technique uses a Fibonacci sequence (1, 2, 3, 5, 8, 13, 21…) or perhaps a modified Fibonacci sequence (0, 0.5, 1, 2, 3, 5, 8…). These numbers make it more difficult to think in terms of one item being twice the size of another, for example, a degree of precision almost certainly not possible at the initial estimation.
A particularly good extension of these techniques is to blend them with something akin to the Delphi method called planning poker (Wikipedia, 2014). In the Delphi method, participants decide on their own estimates completely independent of each other, removing the effect of particularly dominating group members. In planning poker, members of a group, preferably in the same room, individually decide on a category for each work element. After all have decided, each presents his or her result. Lively discussion is likely to ensue, leading to a better consensus estimate and better team understanding of the group effort ahead. This approach differs from the Delphi method in that group discussion usually occurs first, followed by secret ballots to be discussed shortly thereafter.
For Those Who Simply MUST Think in Time
There are teams that simply refuse to use relative estimation. In these instances, some form of project prophylaxis is essential, such as the inch-pebbles (or miniature milestones) approach (Rothman, 1999). This solution involves much more upfront work, breaking down epic tasks into much smaller chunks that can each be executed in a couple of days. In this case, it must be realized that each “couple of days” task may take one or three days instead. Rothman provides a very good presentation of the technique. If this all sounds familiar, it’s because this is the way user stories are normally used. Unfortunately, much extra early work is done breaking down larger elements (perhaps sub-optimally) when the pieces are not yet well understood.
But Is it Really That Simple?
As with all things in life, classification becomes more complicated than one would initially imagine. Upon close examination, it isn’t surprising that a given project or effort includes both knowledge work and task work. For instance, in the driving example, in a perfect world with no traffic the effort is almost completely task work. However, the inevitable changes in factors, such as traffic, weather, accidents, mandatory detours, and more, place the effort partially into the realm of knowledge work, where probabilistic decisions can add immense value.
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For this reason, even within a single project, each work element must be considered according to its most suitable category as task- or knowledge-oriented or perhaps a blend of the two.
We as humans perform poorly when asked to make absolute estimates, but we excel at relative estimates. Relative estimation is even more valuable when there are more unknowns, triggering discussion and negotiation among the team and the customer. The outcome is more complete understanding and more robust group buy-in of the resulting estimates. The challenge is to avoid slipping back into the abyss of old, familiar, and comfortable tendencies to work in absolute terms.
Regardless of the team’s discipline in retaining the relative view, management must understand that the estimates are indeed relative. While past experience can be carried forward into the estimation process, management must be acclimated to the fact that estimates will evolve and should improve over time. Traditional estimating methods simply ignore this reality. Project sponsors should appreciate the fact that the continuous re-estimation that is inherent here is a very positive phenomenon that pays future dividends. Knowledge-work estimates, regardless of industry, are indeed educated guesses that naturally improve with the experience of the guessers within a given project context!
Continued vigilance in the team’s application of the three tenets described here can only be assured if the topic is on the agenda at each retrospective, and the rigor of its application is evaluated each time.
As agility becomes ever more pervasive in the efforts of modern endeavor, these techniques, when properly applied, can have an unimaginable impact on the fiscal efficiency of future work and hence societal success. Such an adaptation is long overdue.