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
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
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
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.


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)

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
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
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
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
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.

(This article cross-posted on LinkedIn.)

How to Know You Have a Well Defined Minimum Viable Product

Conceptually, the idea of a minimum viable product (MVP) is easy to grasp. Early in a project, it’s a deliverable that reflects some semblance to the final product such that it’s barely able to stand on it’s own without lots of hand-holding and explanation for the customer’s benefit. In short, it’s terrible, buggy, and unstable. By design, MVPs lack features that may eventually prove to be essential to the final product. And we deliberately show the MVP to the customer!

We do this because the MVP is the engine that turns the build-measure-learn feedback loop. The key here is the “learn” phase. The essential features to the final product are often unclear or even unknown early in a project. Furthermore, they are largely undefinable or unknowable without multiple iterations through the build-measure-learn feedback cycle with the customer early in the process.

So early MVPs aren’t very good. They’re also not very expensive. This, too, is by design because an MVP’s very raison d’être is to test the assumptions we make early on in a project. They are low budget experiments that follow from a simple strategy:

  1. State the good faith assumptions about what the customer wants and needs.
  2. Describe the tests the MVP will satisfy that are capable of measuring the MVP’s impact on the stated assumptions.
  3. Build an MVP that tests the assumptions.
  4. Evaluate the results.

If the assumptions are not stated and the tests are vague, the MVP will fail to achieve it’s purpose and will likely result in wasted effort.

The “product” in “minimum viable product” can be almost anything: a partial or early design flow, a wireframe, a collection of simulated email exchanges, the outline to a user guide, a static screen mock-up, a shell of screen panels with placeholder text that can nonetheless be navigated – anything that can be placed in front of a customer for feedback qualifies as an MVP. In other words, a sprint can contain multiple MVPs depending on the functional groups involved with the sprint and the maturity of the project. As the project progresses, the individual functional group MVPs will begin to integrate and converge on larger and more refined MVPs, each gaining in stability and quality.

MVPs are not an end unto themselves. They are tangible evidence of the development process in action. The practice of iteratively developing MVPs helps develop to skill of rapid evaluation and learning among product owners and agile delivery team members. A buggy, unstable, ugly, bloated, or poorly worded MVP is only a problem if it’s put forward as the final product. The driving goal behind iterative MVPs is not perfection, rather it is to support the process of learning what needs to be developed for the optimal solution that solves the customer’s problems.

“Unlike a prototype or concept test, an MVP is designed not just to answer product design or technical questions. Its goal is to test fundamental business hypotheses.” – Eric Ries, The Lean Startup

So how might product owners and Agile teams begin to get a handle on defining an MVP? There are several questions the product owner and team can ask of themselves, in light of the product backlog, that may help guide their focus and decisions. (Use of the following term “stakeholders” can mean company executives or external customers.)

  • Identify the likely set of stakeholders who will be attending the sprint review. What will these stakeholders need to see so that they can offer valuable feedback? What does the team need to show in order to spark the most valuable feedback from the stakeholders?
  • What expectations have been set for the stakeholders?
  • Is the distinction clear between what the stakeholders want vs what they need?
  • Is the distinction clear between high and low value? Is the design cart before the value horse?
  • What are the top two features or functions the stakeholders  will be expecting to see? What value – to the stakeholders – will these features or functions deliver?
  • Will the identified features or functions provide long term value or do they risk generating significant rework down the road?
  • Are the identified features or functions leveraging code, content, or UI/UX reuse?

Recognizing an MVP – Less is More

Since an MVP can be almost anything,  it is perhaps easier to begin any conversation about MVPs by touching on the elements missing from an MVP.

An MVP is not a quality product. Using any generally accepted definition of “quality” in the marketplace, an MVP will fail on all accounts. Well, on most accounts. The key is to consider relative quality. At the beginning of a sprint, the standards of quality for an MVP are framed by the sprint goals and objectives. If it meets those goals, the team has successfully created a quality MVP. If measured against the external marketplace or the quality expectations of the customer, the MVP will almost assuredly fail inspection.

Your MVPs will probably be ugly, especially at first. They will be missing features. They will be unstable. Build them anyway. Put them in front of the customer for feedback. Learn. And move on to the next MVP. Progressively, they will begin to converge on the final product that is of high quality in the eyes of the customer. MVPs are the stepping stones that get you across the development stream and to the other side where all is sunny, beautiful, and stable. (For more information on avoiding the trap of presupposing what a customer means by quality and value, see “The Value of ‘Good Enough’“)

An MVP is not permanent. Agile teams should expect to throw away several, maybe even many, MVPs on their way to the final product. If they aren’t, then it is probable they are not learning what they need to about what the customer actually wants. In this respect, waste can be a good, even important thing. The driving purpose of the MVP is to rapidly develop the team’s understanding of what the customer needs, the problems they are expecting to have solved, and the level of quality necessary to satisfy each of these goals.

MVPs are not the truth. They are experiments meant to get the team to the truth. By virtue of their low-quality, low-cost nature, MVPs quickly shake out the attributes to the solution the customer cares about and wants. The solid empirical foundation they provide is orders of magnitude more valuable to the Agile team than any amount of speculative strategy planning or theoretical posturing.

(This article cross-posted on LinkedIn.)

How to know when Agile is working

On a recent flight into Houston, my plane was diverted to Austin due to weather. Before we could land at Austin, we were re-diverted back to Houston. I’ve no idea why the gears aligned this way, but this meant we were out-of-sequence with the baggage handling system and our luggage didn’t arrive at the claim carousel for close to an hour and a half. Leading up to the luggage arrival was an unfortunate display from an increasingly agitated young couple. They were loudly communicating their frustration to an airport employee with unknown authority. Their frustration was understandable in light of the fact that flights were undoubtedly going to be missed.

At one point, the woman exclaimed, “This isn’t how this is supposed to work!”

I matched this with a similar comment from one of the developers on one of my project teams. Stressed with the workload he had committed to, he declared there are too many meetings and therefore “the agile process is not working!” When explored, it turned out some version of this sentiment was common among the software development staff.

In both cases, the process was working. It just wasn’t working as desired or expected based on past experience. In both cases, present events were immune to expectations. The fact that our luggage almost always shows up on time and that agile frequently goes smoothly belies how susceptible the two processes actually are to unknown variables that can disrupt the usual flow of events.

There is a difference with agile, however. When practiced well, it adapts to the vagaries of human experience. We expect the unexpected, even if we don’t know what form that may take.

There is an assumption being made by the developers in that “working agile” makes work easy and stress free. I’ve never found that. And I don’t know anyone who has. It stresses teams differently than waterfall. I’ve experienced high stress developing code under both agile and waterfall. With agile, however, teams have a better shot at deciding for themselves the stress they want to take on. But there will be stress. Unstressed coders deliver code of questionable value and quality, if they deliver at all.

The more accurate assessment to make here is that the developers aren’t practicing agile as well as they could. That’s fundamentally different from “agile isn’t working.” In particular, the developers didn’t understand what they had committed to. Every single sprint planning session I’ve run (and the way I coach them to be run) begins with challenging the team members to think about things that may impact the work they will commit to in the next sprint – vacations, family obligations, doctor visits, other projects, stubbed toes, alien abductions – anything that may limit the effort they can commit to. What occurred with the developers was a failure to take responsibility for their actions and decisions, a measure of dishonesty (albeit unintended) to themselves and their team mates by saying “yes” to work and later wishing “no.”

Underlying this insight into developer workload may be something much more unsettling. If anyone on your team has committed to more than they can complete and has done so for a number of sprints, your project may be at risk. The safe assumption would be that the project has a hidden fragility that will surprise you when it breaks. Project time lines, deliverables, and quality will suffer not solely because there are too many meetings, but because the team does not have a good understanding of what they need to complete and what they can commit to. What is the potential impact on other projects (internal and client) knowing that one or more of the team members is over committing? What delays, quality issues, or major pivots are looming out there ready to cause significant disruptions?

The resolution to this issue requires time and the following actions:

  • Coaching for creating and refining story cards
  • Coaching for understanding how to estimate work efforts (points and/or time)
  • Develop skills in the development staff for recognizing card dependencies
  • Develop skills for time management
  • Find ways to modify the work environment such that it is easier for developers to focus on work for extended periods of time
  • Evaluate the meeting load to determine if there are extraneous meetings
  • Based on metrics, specifically limit each developer’s work commitment for several sprints such that it falls within their ability to complete

Image credit: Prawny

Parkinson’s Law of Triviality and Story Sizing

From  Infogalactic: Parkinson’s law of triviality

Parkinson observed and illustrated that a committee whose job was to approve plans for a nuclear power plant spent the majority of its time on discussions about relatively trivial and unimportant but easy-to-grasp issues, such as what materials to use for the staff bike-shed, while neglecting the non-trivial proposed design of the nuclear power plant itself, which is far more important but also a far more difficult and complex task to criticise constructively.

I see this phenomenon in play during team story sizing exercises in the following scenarios.

  1. In the context of the story being sized, the relative expertise of each of the team members is close to equal in terms of experience and depth of knowledge. The assumption is that if everyone on the team is equally qualified to estimate the effort and complexity of a particular story then the estimation process should move along quickly. With a skilled team, this does, indeed, occur. If it is a newly formed team or if the team is new to agile principles and practices, Parkinson’s Law of Triviality can come into play as the effort quickly gets lost in the weeds.
  2. In the context of the story being sized, the relative expertise of the team members is not near parity and yet each of the individual team members has a great deal of expertise in the context of their respective functional areas. What I’ve observed happening is that the team members least qualified to evaluate the particular story feel the need to assert their expertise and express an opinion. I recall an instance where a software developer estimated it would take 8 hours of coding work to place a “Print This” button on a particular screen. The credentialed learning strategist (who asked for the print button and has no coding experience) seemed incredulous that such an effort would require so much time. A lengthy and unproductive argument ensued.

To prevent this I focus my coaching efforts primarily on the product owner as they will be interacting with the team on this effort during product backlog refinement session more frequently than I. They need to watch for:

  1. Strong emotional response by team members when a size or time estimate is proposed.
  2. Conversations that drop further and further into design details.
  3. Conversations that begin to explore multiple “what if” scenarios.

The point isn’t to prevent each of these behaviors from occurring. Rather manage them. If there is a strong emotional response, quickly get to the “why” behind that response. Does the team member have a legitimate objection or does their response lack foundation?

Every team meeting is an opportunity to clarify the bigger picture for the team so a little bit of conversation around design and risks is a good thing. It’s important to time box those conversations and agree to take the conversation off-line from the backlog refinement session.

When coaching the team, I focus primarily on the skills needed to effectively size an effort. Within this context I can also address the issue of relative expertise and how to leverage and value the opinions expressed by team member who may not entirely understand the skill needed to complete a particular story.

(John Cook cites another interesting example of Parkinson’s Law of Triviality (a.k.a. the bike shed principle) from Michael Beirut’s book How to involving the design of several logos.)

Best Practices or Common Practices

I’m using the phrase “best practices” less and less when working to establish good agile practices. In fact, I’ve stopped using it at all. The primary reason is that it implies there is a set of practices that apply to all circumstances. And in the case of “industry best practices,” they are externally established criteria – they are the best practices and all others have been fully vetted and found wanting. I have found that to be untrue. I’ve also found that people have a hard time letting go of things that are classified as “best.” When your practices are the “best,” there’s little incentive to change even when the evidence strongly suggests there are better alternatives. Moreover, peer pressure works against the introduction of innovative practices. Deviating from a “best” practice risks harsh judgment, retribution, and the dreaded “unprofessional” label.

If an organization is exploring a new area of business or bringing in-house a set of expertise that was previously outsourced, adopting “best” practices may be the smart way to go until some measure of stability has been established. But to keep the initial set of practices and change only as the external definition of “best” changes ends up dis-empowering the organization’s employees. It sends the message, “You aren’t smart enough to figure this out and improve on your own.” When denied the opportunity to excel and improve, employees that need that quality in their work will move one. Over time, the organization is left with just the sort of people who indeed are not inclined to improve – the type of individuals who need well defined job responsibilities and actively resist change of any sort.

There is a dangerous inertia to “best” practices that often goes unnoticed. When one group reaches a level of success by implementing a particular practice, it is touted as one of the keys to its success. And so other groups or organizations adopt the practice. Since everyone wants success, these practices are faithfully implemented according to tradition and change little even as the world around them changes dramatically. In his Harvard Business Review article “Which Best Practice Is Ruining Your Business?”, Freek Vermeulen observes that “when managers don’t see [a] practice as the root cause of their eroding competitive position, the practice persists — and may even spread further to other organizations in the same line of business.” Consequently, business leaders “never connect the problems of today with [a] practice launched years ago.”

“Common” practices, on the other hand, suggest there is room for improvement. They are common because a collection of people have accepted them as generally valuable, not because they are presumed universally true or anointed as “best.” They are derived internally, not imposed externally. As a result, letting go of a “common” practice for a better practice is easier and carries less stigma. With enough adoption throughout the organization, the better practice often becomes the common practice. When we use practices that build upon the collected wisdom from an organization’s experiences we are more likely to take ownership of the process and adapt in ways that naturally lead to improvement.

There are long term benefits to framing prevailing practices as “common.” It reverses the “you are not smart enough” message and encourages practitioners to take more control and ownership in the quality of their practices. Cal Newport argues that “[g]iving people more control over what they do and how they do it increases their happiness, engagement, and sense of fulfillment.” This message is at the heart of Dan Pink’s book, “Drive,” in which he makes the case that more control leads to better grades, better sports performance, better productivity, and more happiness. Pink cites research from Cornell that followed over three hundred small businesses. Half of the businesses deliberately gave substantial control and autonomy to their employees. Over time, these businesses grew at four times the rate of their counterparts.

When you are considering the adoption or pursuit of any best practice, ask yourself, “best” according to whom? It may help avoid some unintended consequences down the line where someone else’s “best” practice yields the worst results for you, your team, or your organization.


Newport, C. (2012). So Good They Can’t Ignore You. New York, NY: Grand Central Publishing.

Pink, D.H. (2009). Drive: The Surprising Truth About What Motivates Us. New York, NY: Riverhead

Vermeulen, F. (2012). Which Best Practice Is Ruining Your Business? Retrieved from

That Isn’t What I Expected

Adverse surprises during a team driven project are about as welcome as whooping cough at a glassblowers convention. Minimizing the opportunity for surprises comes down to how well expectations are defined at the very beginning and how well they are managed during the course of the project. Unidentified expectations are like landmines in the project path. When they explode, it’s bad and the course of the project WILL change. Product owners can’t elucidate all the expectations a stakeholder may have, but with experience they can define the major ones. With practice and attention, experienced product owners can tease out all but the minor expectations that are often dependant on discovery within the project’s sprints.

Key to this skill is knowing the questions to ask at the beginning. In my experience, stakeholders rarely deliberately hold back their expectations. They just don’t know what they don’t know and it is the product owner’s responsibility to establish clarity around expectations. Intuitively obvious expectations rarely play out as such.

A few questions for stakeholders that I’ve found helpful:

  • What business problems do you intend to solve with this project?
  • What do you need to see to know the project is progressing?
  • What will you see when the project is done?
  • What is your availability commitment for the duration of the project?
  • How often to you expect to meet to review progress?
  • How long do YOU think it will take to complete the project?
  • To what extent are your functional groups integrated?
  • Describe your process from design to development to implementation?
  • Are there other stakeholders we need to know about and include?
  • What factors have helped and hurt success with past projects?

This is by no means an exhaustive list of questions. And they may even seem obvious. The answers, however, are almost never obvious.

I also find it effective to challenge stakeholders with scenarios.

  • What happens if we discover this project will take two months longer than expected?
  • What happens if we discover a desired solution is technically unfeasible?
  • How will you support us if we encounter significant delays from client deliverables?

Product owners need to keep pursuing clarity around expectations until they are satisfied they have a good understanding of how the people side of the project will unfold. This will go a long way to helping the development team handle the technical side of the project.

While stakeholders answer these questions, product owners need to pay attention not just the words stakeholders use, but how they answer as well. They need to be scanning for underlying assumptions that drive the answers. These often reflect relevant cultural drivers which can signal significant expectations seemingly unrelated to the project at hand.

For example, perhaps the product owner has established the expectation of a three business day turnaround for feedback from the stakeholder when asked to review periodic project deliverables. “We can complete our reviews within three business days and work to get them to you as fast as possible,” says the stakeholder somewhat hesitantly as he looks off into the distance. Where the pain begins is when the inattentive product owner discovers that, while the feedback may be ready, the client organization has a thick layer of compliance and the feedback is hung up in legal for an additional one to two weeks…every time. If the stakeholder’s responses reflect something less than 100% commitment, keep asking questions designed to surface underlying assumptions.

As each sprint concludes, and eventually the project as well, the savvy product owner knows their work with expectations isn’t complete. Retrospectives for each sprint, each release, and the project conclusion should make note of the expectations that were missed and consider questions that could have been asked that would have helped surface the surprise expectations sooner.

This is also an excellent time to consider if any of the existing expectations have changed or if it appears there may be new expectations emerging. Internal forces, such as changes in team composition, and external forces, such as shifting market demands, can significantly impact the set of expectations a product owner is tasked with managing.

If  you expected to read these kinds of things about surfacing stakeholder expectations, then you’re probably an experienced product owner.