How does Agile help with long term planning?

I’m often involved in discussions about Agile that question its efficacy in one way or another. This is, in my view, a very good thing and I highly encourage this line of inquiry. It’s important to challenge the assumptions behind Agile so as to counteract any complacency or expectation that it is a panacea to project management ills. Even so, with apologies to Winston Churchill, Agile is the worst form of project management…except for all the others.

Challenges like this also serve to instill a strong understanding of what an Agile mindset is, how it’s distinct from Agile frameworks, tools, and practices, and where it can best be applied. I would be the first to admit that there are projects for which a traditional waterfall approach is best. (For example, maintenance projects for nuclear power reactors. From experience, I can say traditional waterfall project management is clearly the superior approach in this context.)

A frequent challenge the idea that with Agile it is difficult to do any long-term planning.

Consider the notion of vanity vs actionable metrics. In many respects, large or long-term plans represent a vanity leading metric. The more detail added to a plan, the more people tend to believe and behave as if such plans are an accurate reflection of what will actually happen. “Surprised” doesn’t adequately describe the reaction when reality informs managers and leaders of the hard truth. I worked a multi-million dollar project many years ago for a Fortune 500 company that ended up being canceled. Years of very hard work by hundreds of people down the drain because projected revenues based on a software product design over seven years old were never going to materialize. Customers no longer wanted or needed what the product was offering. Our “solution” no longer had a problem to solve.

Agile – particularly more recent thinking around the values and principles in the Manifesto – acknowledges the cognitive biases in play with long-term plans and attempts to put practices in place that compensate for the risks they introduce into project management. One such bias is reflected in the planning fallacy – the further out the planning window extends into the future, the less accurate the plan. An iterative approach to solving problems (some of which just happen to use software) challenges development teams on up through managers and company leaders to reassess their direction and make much smaller course corrections to accommodate what’s being learned. As you can well imagine, we may have worked out how to do this in the highly controlled and somewhat predictable domain of software development, however, the critical areas for growth and Agile applicability are at the management and leadership levels of the business.

Another important aspect the Agile mindset is reflected in the Cone of Uncertainty. It is a deliberate, intentional recognition of the role of uncertainty in project management. Yes, the goal is to squeeze out as much uncertainty (and therefore risk) as possible, but there are limits. With a traditional project management plan, it may look like everything has been accounted for, but the rest of the world isn’t obligated to follow the plan laid out by a team or a company. In essence, an Agile mindset says, “Lift your gaze up off of the plan (the map) and look around for better, newer, more accurate information (the territory.) Then, update the plan and adjust course accordingly.” In Agile-speak, this is what is behind phrases like “delivery dates emerge.”

Final thought: You’ll probably hear me say many times that nothing in the Agile Manifesto can be taken in isolation. It’s a working system and some parts if it are more relevant than others depending on the project and the timing. So consider what I’ve presented here in concert with the Agile practices of developing good product visions and sprint goals. Product vision and sprint goals keep the project moving in the desired direction without holding it on an iron-rails-track that cannot be changed without a great deal of effort, if at all.

So, to answer the question in the post title, Agile helps with long term planning by first recognizing the the risks inherent in such plans and implementing process changes that mitigate or eliminate those risks. Unpacking that sentences would consist of listing all the risks inherent with long-term planning and the mechanics behind and reasons why scrum, XP, SAFe, LeSS, etc., etc., etc. have been developed.


Image by Lorri Lang from Pixabay

The Practice of Sizing Spikes with Story Points

Every once and a while it’s good to take a tool out of it’s box and find out if it’s still fit for purpose. Maybe even find if it can be used in a new way. I recently did this with the practice of sizing spikes with story points. I’ve experienced a lot of different projects since last revisiting my thinking on this topic. So after doing a little research on current thinking, I updated an old set of slides and presented my position to a group of scrum masters to set the stage for a conversation. My position: Estimating spikes with story points is a vanity metric and teams are better served with spikes that are time-boxed but unsized.

While several colleagues came with an abundance of material to support their particular position, no one addressed the points I raised. So it was a wash. My position hasn’t changed appreciably. But I did gain from hearing several arguments for how spikes could be used more effectively if they were to be sized with story points. And perhaps the feedback from this article will further evolve my thinking on the subject.

To begin, I’ll answer the question of “What is a spike?” by accepting the definition from agiledictionary.com:

Spike

A task aimed at answering a question or gathering information, rather than at producing shippable product. Sometimes a user story is generated that cannot be well estimated until the development team does some actual work to resolve a technical question or a design problem. The solution is to create a “spike,” which is some work whose purpose is to provide the answer or solution.

The phrase “cannot be well estimated” is suggestive. If the work cannot be well estimated than what is the value of estimating it in the first place? Any number placed on the spike is likely to be for the most part arbitrary. Any number greater than zero will therefore arbitrarily inflate the sprint velocity and make it less representative of the business value being delivered. It may make the team feel better about their performance, but it tells the stakeholders less about the work remaining. No where can I find a stated purpose of Agile or scrum to be making the team “feel better.” In practice, by masking the amount of business value being delivered, the opposite is probably true. The scrum framework ruthlessly exposes all the unhelpful and counterproductive practices and behaviors an unproductive team may be unconsciously perpetuating.

Forty points of genuine business value delivered at the end of a sprint is 100% of rubber on the road. Forty points delivered of which 10 are points assigned to one or more spikes is 75% of rubber on the road. The spike points are slippage. If they are left unpointed then it is clear what is happening. A spike here and there isn’t likely to have a significant impact on the velocity trend over 8 or 10 sprints, for example. One or more spikes per sprint is another matter. In this case, numerous spikes in successive sprints will likely cause the velocity to sink. If this happens, a number of corrective actions can be taken – actions that may be missed if the velocity is falsely kept at a certain desired or expected value. In other words, pointing spikes hides important information that could very well impact the success of the project. Bad news can inspire better decisions and corrective action. Falsely positive news most often leads to failures of the epic variety.

Consider the following two scenarios.

Team A has decided to add story points to their spikes. Immediately they run into several significant challenges related to the design and the technology choices made. So they create a number of spikes to find the answers and make some informed decision. The design and technology struggles continue for the next 10 sprints. Even with the challenges they faced, the team appears to have quickly established a stable velocity.

The burndown, however, looks like this:

If the scrum master were to use just the velocity numbers it would appear Team A is going to finish their work in about 14 sprints. This might be true if Team A were to have no more spikes in the remaining sprints. The trend, however, strongly suggests that’s not likely to happen. If a team has been struggling with design and technical issues for 10 sprints, it is unlikely those struggles will suddenly stop at sprint 11 or beyond unless there have been deliberate efforts to mitigate that potential. By pointing spikes and generating a nice-looking velocity chart it is more probable that Team A is unaware of the extent to which they may be underestimating the amount of time to complete items in the backlog.

 

 

Team B finds themselves in exactly the same situation as Team A. They immediately run into several significant challenges related to the design and the technology choices made and create a number of spikes to find the answers and make some informed decision. However, they decided not to add story points to their spikes. The design and technology struggles continue for the next 10 sprints. The data show that Team B is clearly struggling to establish a stable velocity.

 

And the burndown looks like this, same as Team A after 10 sprints:

However, it looks like it’s going to take Team B 21 more sprints to complete the work. That they’re struggling isn’t good. That it’s clear they struggling is very good. This isn’t apparent with Team A’s velocity chart. Since it’s clear they are struggling it is much easier to start asking questions, find the source of the agony, and make changes that will have a positive impact. It is also much more probably that the changes will be effective because they will have been based on solid information as to what the issues are. Less guess work involved with Team B than with Team A.

However, any scrum master worth their salt is going to notice that the product backlog burndown doesn’t align with the velocity chart. It isn’t burning down as fast as the velocity chart suggests it should be. So the savvy Team A scrum master starts tracking the burndown of value-add points vs spike points. Doing so might look like the following burndown:

Using the average from the parsed burndown, it is much more likely that Team A will need 21 additional sprints to complete the work. And for Team B?

The picture of the future based on the backlog burndown is a close match to the picture from the velocity data, about 22 sprints to complete the work.

If you were a product owner, responsible for keeping the customer informed of progress, which set of numbers would you want to base your report on? Would you rather surprise the customer with a “sudden” and extended delay or would you rather communicate openly and accurately?

Summary

Leaving spikes unpointed…

  • Increases the probability that performance metrics will reveal problems sooner and thus allow for corrective actions to be taken earlier in a project.
  • The team’s velocity and backlog burndown is a more accurate reflection of value actually being created for the customer and therefore allows for greater confidence of any predictions based on the metrics.

I’m interested in hearing your position on whether or not spikes should be estimated with story points (or some other measure.) I’m particularly interested in hearing where my thinking described in this article is in need of updating.

[An earlier version of this article originally appeared on the Agile Alliance blog.]

Center Point: The Product Backlog

 

If a rock wall is what is needed, but the only material available is a large boulder, how can you go about transforming the latter into the former?

Short answer: Work.

Longer answer: Lots of work.

Whether the task is to break apart a physical boulder into pieces suitable for building a wall or breaking up an idea into actionable tasks, there is a lot of work involved. Especially if a team is inexperienced or lacks the skilled needed to successfully complete such a process.

Large ideas are difficult to work with. They are difficult to translate into action until they are broken down into more manageable pieces. That is, descriptions of work that can be organized into manageable work streams.

We choose to go to the Moon in this decade and do the other things, not because they are easy, but because they are hard; because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one we intend to win, and the others, too.President John Kennedy

That’s a pretty big boulder. In fact, it’s so big it served quite well as a “product vision” for the effort that eventually got men safely to and from the moon in 1969. Even so, Kennedy’s speech called out the effort that it would take to realize the vision. Hard work. Exceptional energy and skill. What followed in the years after Kennedy’s speech in 1962 was a whole lot of boulder-busting activity

 

A user story is a brief, simple statement from the perspective of the product’s end user. It’s an invitation for a conversation about the what’s needed so that the story can meet all the user’s expectations.

In it’s simplest form, this is all a product backlog is. A collection of doable user stories derived from a larger vision for the product and ordered in a way that allows for a realistic path to completion to be defined. While this is simple, creating and maintaining such a thing is difficult.

Experience has taught me that the single biggest impediment to improving a team’s performance is the lack of a well-defined and maintained product backlog. Sprint velocities remain volatile if design changes or priorities are continually clobbering sprint scope. Team morale suffers if they don’t know what they’re going to be working on sprint-to-sprint or, even worse, if the work they have completed will have to be reworked or thrown out. The list of negative ripple effects from a poor quality product backlog is a long one.


Photo by James Lee on Unsplash

Systems Thinking, Project Management, and Agile – Part 8: Design Changes and Scope

[For this series, it will help to have read “System Dynamics and Causal Loop Diagrams 101.”]

For the conclusion of this series, I’ll look at several other levers that management can control when working to keep their teams in good health: Design and scope changes.

Changes in design can either be tightly or loosely coupled to changes in scope. In general, you can’t change one without changing the other. This is how I think of design and scope. Others think of them differently.

Few people intentionally change the scope of a project. Design changes, however, are usually intentional and frequent. They are also usually small relative to the overall project design so their effect on scope and progress can go unnoticed.

Nonetheless, small design changes are additive. Accumulate enough of them and it becomes apparent that scope has been affected. Few people recognize what has happened until it’s too late. A successive string of “little UI tweaks,” a “simple” addition to handle another file format that turned out to be not-so-simple to implement, a feature request slipped in by a senior executive to please a super important client – changes like this incrementally and adversely impact the delivery team’s performance.

Scope changes primarily impact the amount of Work to Do (Figure 1). Of course, Scope changes impact other parts of the system, too. The extent depends on the size of the Scope change and how management responds to the change in Scope. Do they push out the Deadline? Do they Hire Talent?

Figure 1

The effect of Design Changes on the system are more immediate and significant. Progress slows down while the system works to understand and respond to the Design Changes. As with Scope, the effect will depend on the extent of the Design Changes introduced into the system. The amount of Work to Do will increase. The development team will need to switch focus to study the changes (Task Switching. ) If other teams are dependent on completion of prior work or are waiting for the new changes, Overlap and Concurrence will increase. To incorporate the changes mid-project, there will likely be Technical Debt incurred in order to keep the project on schedule. And if the design impacts work already completed or in progress, there will be an increase in the amount of Rework to Do for the areas impacted by the Design Changes.

Perhaps the most important secondary consequence of uncontrolled design changes is the effect on morale. Development teams love a good challenge and solving problems. But this only has a positive effect on morale if the goal posts don’t change much. If the end is perpetually just over the next hill, morale begins to suffer. This hit to morale usually happens much quicker than most managers realize.

It is better to push off non-critical design changes to a future release. This very act often serves as a clear demonstration to development teams that management is actively working to control scope and can have a positive effect on the team’s morale, even if they are under a heavy workload.


Photo by Ariel Pilotto on Unsplash

Story Points and Fuzzy Bunnies

The scrum framework is forever tied to the language of sports in general and rugby in particular. We organize our project work around goals, sprints, points, and daily scrums. An unfortunate consequence of organizing projects around a sports metaphor is that the language of gaming ends up driving behavior. For example, people have a natural inclination to associate the idea of story points to a measure of success rather than an indicator of the effort required to complete the story. The more points you have, the more successful you are. This is reflected in an actual quote from a retrospective on things a team did well:

We completed the highest number of points in this sprint than in any other sprint so far.

This was a team that lost sight of the fact they were the only team on the field. They were certain to be the winning team. They were also destine to be he losing team. They were focused on story point acceleration rather than a constant, predictable velocity.

More and more I’m finding less and less value in using story points as an indicator for level of effort estimation. If Atlassian made it easy to change the label on JIRA’s story point field, I’d change it to “Fuzzy Bunnies” just to drive this idea home. You don’t want more and more fuzzy bunnies, you want no more than the number you can commit to taking care of in a certain span of time typically referred to as a “sprint.” A team that decides to take on the care and feeding of 50 fuzzy bunnies over the next two weeks but has demonstrated – sprint after sprint – they can only keep 25 alive is going to lose a lot of fuzzy bunnies over the course of the project.

It is difficult for people new to scrum or Agile to grasp the purpose behind an abstract idea like story points. Consequently, they are unskilled in how to use them as a measure of performance and improvement. Developing this skill can take considerable time and effort. The care and feeding of fuzzy bunnies, however, they get. Particularly with teams that include non-technical domains of expertise, such as content development or learning strategy.

A note here for scrum masters. Unless you want to exchange your scrum master stripes for a saddle and spurs, be wary of your team turning story pointing into an animal farm. Sizing story cards to match the exact size and temperament from all manner of animals would be just as cumbersome as the sporting method of story points. So, watch where you throw your rope, Agile cowboys and cowgirls.


Image by Simona Robová from Pixabay

Agile Metrics – Time (Part 3 of 3)

In Part 1 of this series, we set the frame for how to use time as a metric for assessing Agile team and project health. In Part 2, we looked at shifts in the cross-over point between burn-down and burn-up charts. In Part 3, we’ll look at other asymmetries and anomalies that can appear in time burn-down/burn-up charts and explore the issues the teams may be struggling with under these circumstances.

Figure 1 shows a burn-up that by the end of the sprint significantly exceeded the starting value for the original estimate.

Figure 1

There isn’t much mystery around a chart like this. The time needed to complete the work was significantly underestimated. The mystery is in the why and what that led to this situation.

  • Where there unexpected technical challenges?
  • Were the stories poorly defined?
  • Were the acceptance criteria unclear?
  • Were the sprint goals, objectives, or minimum viable product definition unclear?

Depending on the tools used to capture team metrics, it can be helpful to look at individual performances. What’s the differential between story points and estimated time vs actual time for each team member? Hardly every useful as a disciplinary tool, this type of analysis can be invaluable for knowing who needs professional development and in what areas.

In this case, there were several technical challenges related to new elements of the underlying architecture and the team put in extra hours to resolve them. Even so, they were unable to complete all the work they committed to in the sprint. The the scrum master and product owner need to monitor this so it isn’t a recurrent event or they risk team burnout and morale erosion if left unchecked. There are likely some unstated dependencies or skill deficiencies that need to be put on the table for discussion during the retrospective.

Figure 2 shows, among other things, unexpected jumps in the burn-down chart. There is clearly a significant amount of thrashing evident in the burn-down (which stubbornly refuses to actually burn down.)

Figure 2

Questions to explore:

  • Are cards being brought into the sprint after the sprint has started and why?
  • Are original time estimates being changed on cards after the sprint has started?
  • Is there a stakeholder in the grass, meddling with the team’s commitment?
  • Was a team member added to the team and cards brought into the sprint to accommodate the increased bandwidth?
  • Whatever is causing the thrashing, is the team (delivery team members, scrum master, and product owner) aware of the changes?

Scope change during a sprint is a very undesirable practice. Not just because it goes against the scrum framework, but more so because it almost always has an adverse effect on team morale and focus. If there is an addition to the team, better to set that person to work helping teammates complete the work already defined in the sprint and assign them cards in the next sprint.

If team members are adjusting original time estimates for “accuracy” or whatever reason they may provide, this is little more than gaming the system. It does more harm than good, assuming management is Agile savvy and not intent on using Agile metrics for punitive purposes. On occasion I’ve had to hide the original time estimate entry field from the view of delivery team members and place it under the control of the product owner – out of sight, out of mind. It’s less a concern to me that time estimates are “wrong,” particularly if time estimate accuracy is showing improvement over time or the delta is a somewhat consistent value. I can work with an delivery team member’s time estimates that are 30% off if they are consistently 30% off.

In the case of Figure 2 it was the team’s second sprint and at the retrospective the elephant was called out from hiding: The design was far from stable. The decision was made to set aside scrum in favor of using Kanban until the numerous design issues could be resolved.

Figure 3 shows a burn-down chart that doesn’t go to zero by the end of the sprint.

Figure 3

The team missed their commit and quite a few cards rolled to the next sprint. Since the issue emerged late in the sprint there was little corrective action that could be taken. The answers were left to discovery during the retrospective. In this case, one of the factors was the failure to move development efforts into QA until late in the sprint. This is an all too common issue in cases where the sprint commitments were not fully satisfied. For this team the QA issue was exacerbated by the team simply taking on more than they thought they could commit to completing. The solution was to reduce the amount of work the team committed to in subsequent sprints until a stable sprint velocity emerged.

Conclusion

For a two week sprint on a project that is 5-6 sprints in, I usually don’t bother looking at time burn-down/burn-up charts for the first 3-4 days. Early trends can be misleading, but by the time a third of the sprint has been completed this metric will usually start to show trends that suggest any emergent problems. For new projects or for newly formed teams I typically don’t look at intra-sprint time metrics until much later in the project life cycle as there are usually plenty of other obvious and more pressing issues to work through.

I’ll conclude by reiterating my caution that these metrics are yard sticks, not micrometers. It is tempting to read too much into pretty graphs that have precise scales. Rather, the expert Agilest will let the metrics, whatever they are, speak for themselves and work to limit the impact of any personal cognitive biases.

In this series we’ve explored several ways to interpret the signals available to us in estimated time burn-down and actual time burn-up charts. There are numerous others scenarios that can reveal important information from such burn-down/burn-up charts and I would be very interested in hearing about your experiences with using this particular metric in Agile environments.

Agile Metrics – Time (Part 2 of 3)

In Part 1 of this series, we set the frame for how to use time as a metric for assessing Agile team and project health. In Part 2, we’ll look at shifts in the cross-over point between burn-down and burn-up charts and explore what issues may be in play for the teams under these circumstances.

Figure 1 shows a cross-over point occurring early in the sprint.

Figure 1

 

This suggests the following questions:

  • Is the team working longer hours than needed? If so, what is driving this effort? Are any of the team members struggling with personal problems that have them working longer hours? Are they worried they may have committed to more work than they can complete in the sprint and are therefore trying to stay ahead of the work load? Has someone from outside the team requested additional work outside the awareness of the product owner or scrum master?
  • Has the team over estimated the level of effort needed to complete the cards committed to the sprint? If so, this suggests an opportunity to coach the team on ways to improve their estimating or the quality of the story cards.
  • Has the team focused on the easy story cards early in the sprint and work on the more difficult story cards is pending? This isn’t necessarily a bad thing, just something to know and be aware of after confirming this with the team. If accurate, it also points out the importance of using this type of metric for intra-sprint monitoring only and not extrapolate what it shows to a project-level metric.

The answer to these questions may not become apparent until later in the sprint and the point isn’t to try and “correct” the work flow based on relatively little information. In the case of Figure 1, the “easy” cards had been sized as being more difficult than they actually were. The more difficult cards were sized too small and a number of key dependencies were not identified prior to the sprint planning session. This is reflected in the burn-up line that significantly exceeds the initial estimate for the sprint, the jumps in the burn-down line, and subsequent failure to complete a significant portion of the cards in the sprint backlog. All good fodder for the retrospective.

Figure 2 shows a cross-over point occurring late in the sprint.

Figure 2

On the face of it there are two significant stretches of inactivity. Unless you’re dealing with a blatantly apathetic team, there is undoubtedly some sort of activity going on. It’s just not being reflected in the work records. The task is to find out what that activity is and how to mitigate it.

The following questions will help expose the cause for the extended periods of apparent inactivity:

  • Are one or more members not feeling well or are there other personal issues impacting an individual’s ability to focus?
  • Have they been poached by another project to work on some pressing issue?
  • Are they waiting for feedback from stakeholders,  clients, or other team members?
  • Are the story cards unclear? As the saying goes, story cards are an invitation to a conversation. If a story card is confusing, contradictory, or unclear than the team needs to talk about that. What’s unclear? Where’s the contradiction? As my college calculus professor used to ask when teaching us how to solve math problems, “Where’s the source of the agony?”

The actual reasons behind Figure 2 were two fold. There was a significant technical challenge the developers had to resolve that wasn’t sufficiently described by any of the cards in the sprint and later in the sprint several key resources were pulled off the project to deal with issues on a separate project.

Figure 3 shows a similar case of a late sprint cross-over in the burn-down/burn-up chart. The reasons for this occurrence were quite different than those shown in Figure 2.

Figure 3

 

This was an early sprint and a combination of design and technical challenges were not as well understood as originally thought at the sprint planning session. As these issues emerged, additional cards were created in the product backlog to be address in future sprints. Nonetheless, the current sprint commitment was missed by a significant margin.

In Part 3, we’ll look at other asymmetries and anomalies that can appear in time burn-down/burn-up charts and explore the issues may be in play for the teams under these circumstances.

Agile Metrics – Time (Part 1 of 3)

Some teams choose to use card level estimated and actual time as one of the level of effort or performance markers for project progress and health. For others it’s a requirement of the work environment due to management or business constraints. If your situation resembles one of these cases then you will need to know how to use time metrics responsibly and effectively. This series of articles will establish several common practices you can use to develop your skills for evaluating and leveraging time-based metrics in an Agile environment.

It’s important to keep in mind that time estimates are just one of the level of effort or performance markers that can be used to track team and project health. There can, and probably should be other markers in the overall mix of how team and project performance is evaluated. Story points, business value, quality of information and conversation from stand-up meetings, various product backlog characteristics, cycle time, and cumulative flow are all examples of additional views into the health and progress of a project.

In addition to using multiple views, it’s important to be deeply aware of the strengths and limits presented by each of them. The limits are many while the strengths are few.  Their value comes in evaluating them in concert with one another, not in isolation.  One view may suggest something that can be confirmed or negated by another view into team performance. We’ll visit and review each of these and other metrics after this series of posts on time.

The examples presented in this series are never as cut and dried as presented. Just as I previously described multiple views based on different metrics, each metric can offer multiple views. My caution is that these views shouldn’t be read like an electrocardiogram, with the expectation of a rigidly repeatable pattern from which a slight deviation could signal a catastrophic event. The examples are extracted from hundreds of sprints and dozens of projects over the course of many years and are more like seismology graphs – they reveal patterns over time that are very much context dependent.

Estimated and actual time metrics allow teams to monitor sprint progress by comparing time remaining to time spent. Respectively, this will be a burn-down and a burn-up chart in reference to the direction of the data plotted on the chart. In Figure 1, the red line represents the estimated time remaining (burn-down) while the green line represents the amount of time logged against the story cards (burn-up) over the course of a two week sprint. (The gray line is a hypothetical ideal for burn-down.)

Figure 1

The principle value of a burn-down/burn-up chart for time is the view it gives to intra-sprint performance. I usually look at this chart just prior to a teams’ daily stand-up to get a sense if there are any questions I need to be asking about emerging trends. In this series of posts we’ll explore several of the things to look for when preparing for a stand-up. At the end of the sprint, the burn-down/burn-up chart can be a good reference to use during the retrospective when looking for ways to improve.

The sprint shown in Figure 1 is about as ideal a picture as one can expect. It shows all the points I look for that tell me, insofar as time is concerned, the sprint performance is in good health.

  • There is a cross-over point roughly in the middle of the sprint.
  • At the cross-over point about half of the estimated time has been burned down.
  • The burn-down time is a close match to the burn-up at both the cross-over point and the end of the sprint.
  • The burn-down and burn-up lines show daily movement in their respective directions.

In Part 2, we’ll look at several cases where the cross-over point shifts and explore the issues the teams under these circumstances might be struggling with.

Agile Team Composition: Generalists versus Specialists

Estimating levels of effort for a set of tasks by a group of individuals well qualified to complete those tasks can efficiently and reliable be determined with a collaborative estimation process like planning poker. Such teams have a good measure of skill overlap. In the context of the problem set, each of the team members are generalist in the sense  it’s possible for any one team member to work on a variety of cross functional tasks during a sprint. Differences in preferred coding language among team members, for example, is less an issue when everyone understands advanced coding practices and the underlying architecture for the solution.

With a set of complimentary technical skills it’s is easier agree on work estimates. There are other benefits that flow from well-matched teams. A stable sprint velocity emerges much sooner. There is greater cross functional participation. And re-balancing the work load when “disruptors” occur – like vacations, illness, uncommon feature requests, etc. – is easier to coordinate.

Once the set of tasks starts to include items that fall outside the expertise of the group and the group begins to include cross functional team members, a process like planning poker becomes increasingly less reliable. The issue is the mismatch between relative scales of expertise. A content editor is likely to have very little insight into the effort required to modify a production database schema. Their estimation may be little more than a guess based on what they think it “should” be. Similarly for a coder faced with estimating the effort needed to translate 5,000 words of text from English to Latvian. Unless, of course, you have an English speaking coder on your team who speaks fluent Latvian.

These distinctions are easy to spot in project work. When knowledge and solution domains have a great deal of overlap, generalization allows for a lot of high quality collaboration. However, when an Agile team is formed to solve problems that do not have a purely technical solution, specialization rather than generalization has a greater influence on overall success. The risk is that with very little overlap specialized team expertise can result in either shallow solutions or wasteful speculation – waste that isn’t discovered until much later. Moreover, re-balancing the team becomes problematic and most often results in delays and missed commitments due to the limited ability for cross functional participation among team mates.

The challenge for teams where knowledge and solution domains have minimal overlap is to manage the specialized expertise domains in a way that is optimally useful, That is, reliable, predictable, and actionable. Success becomes increasingly dependent on how good an organization is at estimating levels of effort when the team is composed of specialists.

One approach I experimented with was to add a second dimension to the estimation: a weight factor to the estimator’s level of expertise relative to the nature of the card being considered. The idea is that with a weighted expertise factor calibrated to the problem and solution contexts, a more reliable velocity emerges over time. In practice, was difficult to implement. Teams spent valuable time challenging what the weighted factor should be and less experienced team members felt their opinion had been, quite literally, discounted.

The approach I’ve had the most success with on teams with diverse expertise is to have story cards sized by the individual assigned to complete the work. This still happens in a collaborative refinement or planning session so that other team members can contribute information that is often outside the perspective of the work assignee. Dependencies, past experience with similar work on other projects, missing acceptance criteria, or a refinement to the story card’s minimum viable product (MVP) definition are all examples of the kind of information team members have contributed. This invariably results in an adjustment to the overall level of effort estimate on the story card. It also has made details about the story card more explicit to the team in a way that a conversation focused on story point values doesn’t seem to achieve. The conversation shifts from “What are the points?” to “What’s the work needed to complete this story card?”

I’ve also observed that by focusing ownership of the estimate on the work assignee, accountability and transparency tend to increase. Potential blockers are surfaced sooner and team members communicate issues and dependencies more freely with each other. Of course, this isn’t always the case and in a future post I’ll explore aspects of team composition and dynamics that facilitate or prevent quality collaboration.


Image by Gerd Altmann from Pixabay

Improving The Odds of Success For Any Goal With A Definition of Done

In 1626 the King of Sweden, Gustavus Adolphus, began work on what he envisioned would be the most powerful battleship ever to set sail – the Vasa. By all accounts, Gustavus was a brilliant military commander. Over the next two years the King repeatedly alter specifications such that massive amounts of rework were required. The mid-project inclusion of non-essential work – such as adding close to 500 elaborately decorated sculptures – added to the delay. The array of canon on the ship grew both in size and number. The result was an untested design that proved unstable when the ship was launched with great fanfare from the shipyard in Stockholm. Before King and country, the Vasa hadn’t made it out of the harbor before a strong breeze tipped the ship so far that water began entering the canon portals and sank the ship.

The lessons from this historical event about meddling managers embedded in a hierarchical system of status and nobility are obvious. This practice is still very much endemic in legacy corporations and MBA programs continue to crank out a plethora of future executives equipped to carry on the tradition. Thousands of executive and Agile coaches make a well deserved living working to remediate the problem.

A lesser but more actionable lesson has to do with Gustavus’ approach to project management. As brilliant as he was on the battlefield, this skill did not translate to the material production field where events move toward completion over months and years rather than hours and days.

There is a reason Agile project management leverages frameworks rather than highly structured protocols for getting work done. It recognizes that the world can be a messy place. Particularly when it requires human beings to complete work. With so many variables in play – emotions, physical health, competing priorities around family, pandemics, etc. – it’s amazing we get as much done as we do. Frameworks give us the flexibility to adjust and adapt to the situation.

There is a paradox embedded within Agile frameworks. Flexibility and adaptability are important, but there are also elements of the frameworks that are important to get right. The most important is to have a healthy product backlog that is vigorously maintained and defended by the product owner. If this isn’t in place, everything else become a major battle to implement. Stories bounce across multiple sprints, errors and rework grow exponentially and stakeholders readily jump to uncomfortable conclusions about progress.

Another important element is what’s typically called the “definition of done.” If the product owner or Agile team member can’t clearly and concisely describe what “done” looks like, you end up with some version of this conversation.

Product Owner: “What do you mean you’re still working on that story. I closed it last sprint because you said it was ‘done!'”

Agile Team Member: “Well, uh, yeah. It was done. But it wasn’t done done. There were still a couple of things I wanted to finish.”

If your definition of done is some version of “I’ll know it when I see it,” there is a good chance you’re about to attempt the launch your very own Vasa.

If you’d rather not do that, here are a few things to do instead:

  • If you are involved during the design phase of the project, repeatedly run a thought experiment where you begin with the end in mind. It’s that vision statement thingy.
  • Work to establish a clear understanding of what “good enough for now” means. And when you’ve done that, keep checking in with your team to evaluate if anything has changed to cloud that understanding.
  • Use minimum viable product definitions. Add to this the idea of minimum viable actions. As important as it is to know, as best you can, what done looks like, you need a sequence of actions that will get you there. What are those? In what order can they most effectively be sequenced? How jis what you’re learning along the way changing the path to “done?”
  • Finally, keep your product backlog healthy and strong. Without exception, continuously refine the backlog with stakeholders and the development team so that it is an accurate reflection of progress and future work.

Photo by Jamie Morrison on Unsplash