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.

Achieving 10x

Crossed paths with an old but still relevant conversation thread on Slashdot asking “What practices impede developers’ productivity?” The conversation is in response to an excellent post by Steve McConnell addressing productivity variations among software developers and teams and the origin of “10x” – that is, the observation noted in the wild of “10-fold differences in productivity and quality between different programmers with the same levels of experience and also between different teams working within the same industries.”

The Slashdot conversation has two main themes, one focuses fundamentally on communication: “good” meetings, “bad” meetings, the time of day meetings are held, status reports by email – good, status reports by email – bad, interruptions for status reports, perceptions of productivity among non-technical coworkers and managers, unclear development goals, unclear development assignments, unclear deliverables, too much documentation, to little documentation, poor requirements.

A second theme in the conversation is reflected in what systems dynamics calls “shifting the burden”: individuals or departments that do not need to shoulder the financial burden of holding repetitively unproductive meetings involving developers, arrogant developers who believe they are beholding to none, the failure to run high quality meetings, code fast and leave thorough testing for QA, reliance on tools to track and enhance productivity (and then blaming them when they fail), and, again, poor requirements.

These are all legitimate problems. And considered as a whole, they defy strategic interventions to resolve. The better resolutions are more tactical in nature and rely on the quality of leadership experience in the management ranks. How good are they at 1) assessing the various levels of skill among their developers and 2) combining those skills to achieve a particular outcome? There is a strong tendency, particularly among managers with little or no development experience, to consider developers as a single complete package. That is, every developer should be able to write new code, maintain existing code (theirs and others), debug any code, test, and document. And as a consequence, developers should be interchangeable.

This is rarely the case. I can recall an instance where a developer, I’ll call him Dan, was transferred into a group for which I was the technical lead. The principle product for this group had reached maturity and as a consequence was beginning to become the dumping ground for developers who were not performing well on projects requiring new code solutions. Dan was one of these. He could barely write new code that ran consistently and reliably on his own development box. But what I discovered is that he had a tenacity and technical acuity for debugging existing code.

Dan excelled at this and thrived when this became the sole area of his involvement in the project. His confidence and respect among his peers grew as he developed a reputation for being able to ferret out particularly nasty bugs. Then management moved him back into code development where he began to slide backward. I don’t know what happened to him after that.

Most developers I’ve known have had the experience of working with a 10x developer, someone with a level of technical expertise and productivity that is undeniable, a complete package. I certainly have. I’ve also had the pleasure of managing several. Yet how many 10x specialists have gone underutilized because management was unable to correctly assess their skills and assign them tasks that match their skills?

Most of the communication issues and shifting the burden behaviors identified in the Slashdot conversation are symptomatic of management’s unrealistic expectations of relative skill levels among developers and their inability to assess and leverage the skills that exist within their teams.


Image by alan9187 from Pixabay

Cook’s Theory of Performance Evaluation

The ideas presented here evolved from a post titled “Evaluate people at their best or their worst?” on John Cook’s blog. In order to make this post a little tighter, I’ll refer to John’s ideas as “Cook’s Theory of Performance Evaluation” and describe it as follows.

John identifies three ways a person’s performance can be evaluated:

  1. How good are they at their worst?
  2. How good are they on average?
  3. How good are they at their best?

Cook observes that schools evaluate performance using the first two benchmarks and markets use the third benchmark. To illustrate, consider the following assignment grades for a student in a hypothetical course:

Assignment Score
1 100
2 90
3 92
4 87
5 100
6 45
7 90
8 100
9 95
10 100
Average: 89.9
Result: B+

Graphically, this looks like:

The student’s worst performance pulls the grade average down and results in a B+ for the course. Performance evaluation in markets, however, is only interested in how well you do, that is, your best. Consider the following sales volumes for a fictional author for each of ten books:

Book Copies Sold
1 1,000
2 2,500
3 900
4 1,100
5 3,400
6 1,000,000
7 42,000
8 6,500
9 2,750
10 3,100
Result: Bestseller!

Graphically, this looks like:

Number 6 must have been some story. But as they say, you can’t argue with success and this author will forever be known as a bestseller. Subsequent flops won’t change that.

So there you have Cook’s Theory of Performance Evaluation. The consequences of this theory when played out in real life are noted by Cook:

We all want others to see the best in us. There are ethical and economic reasons to look for the best in others. But years of education can incline us to look for the worst in others and in ourselves.

Another point that can be made is that in school, everyone starts out with a perfect score that for most students erodes as the class progresses. In markets, everyone starts out with essentially a zero score that for most entrepreneurs improves over time, commensurate with the individual’s effort. Money, of course, is another way to keep score in market-based performance evaluations.

If education has conditioned us to look for the worst in others and ourselves, it has also conditioned us to become demoralized when encountering even the slightest failure that diminishes our chances at succeeding. Once lost, the perfect score can never be regained, so we settle for less. The greater the failure, the less we must settle for.

Moreover, we are conditioned that we can never exceed the highest possible achievement as defined by academia. The best we can do is match it. Most come up short. This conditioning is difficult to shake and in my own experience took several years after obtaining my undergraduate degrees. Nothing like 100+ job rejection letters to cause one to reevaluate the nature and size of the door opened by a couple of college degrees.

There are other ways to evaluate an individual’s performance.

  1. How good are they compared to others (past and present)?
  2. How good are they compared to themselves in the past?
  3. How good are they compared to their personal criteria and expectations?
  4. How good are they compared to the criteria and expectations of others?

The answer to each of these questions can be radically different depending on the referential index of the questioner. “How good am I when compared to others?” is significantly different from “How good is he/she when compared to others?”

The answers to each of these questions can also be significantly influenced by various biases and prejudices. Confirmation bias, hindsight bias, self-serving bias, the Dunning–Kruger effect, the misinformation effect, self-handicapping, self-fulfilling prophecies, introspection illusion, groupthink, the affect heuristic – numerous ways an individual can skew the evaluation of their own performance.

When the performance evaluation comes from a third party, for example a university professor evaluating a student’s performance, there are a different combination of biases in play which can have an independent impact on the performance score. The fundamental attribution error, confirmation bias, the illusion of transparency, credentialism or argument from authority – more ways the individual’s eventual performance score can can be unconsciously influenced. The combination of unconscious incompetence and the Dunning–Kruger effect can have a particularly adverse effect on the student who asks questions that expose a professor’s incompetence.

Here again, the level playing field appears to be with market-based performance evaluations. An individual’s ability to understand and mitigate biases and prejudices affecting their success will have a direct impact on their performance in the market. Students, however, have less influence over these drivers when they are manifest in individuals working from a position of authority.


Image by StockSnap from Pixabay

What’s in YOUR manual?

 

You go to see a movie with a friend. You sit side-by-side and watch the same movie projected on the screen. Afterward, in discussing the movie, you both disagree on the motives of the lead character and even quibble over the sequence of events in the movie you just watched together.

How is it that two people having just watched the same movie could come to different conclusions and even disagree over the sequence of events that – objectively speaking – could have only happened in one way?

It’s what brains do. Memory is imperfect and every one of us has a unique set of filters and lenses through which we view the world. At best, we have a mostly useful but distorted model of the world around us. Not everyone understands this. Perhaps most people don’t understand this. It’s far more common for people – especially smart people – to believe and behave as if their model of the world is 1) accurate and 2) shared with everybody else on the planet.

Which gets me to the notion of the user manuals we all carry around in our heads about OTHER people.

Imagine a tall stack of books, some thin others very thick. On the spine of each book is the name of someone you know. The book with your partner’s name on it is particularly thick. The book with the name of your favorite barista on the spine is quite a bit thinner. Each of these books represents a manual that you have written on how the other person is supposed to behave. Your partner, for example, should know what they’re supposed to be doing to seamlessly match your model of the world. And when they don’t follow the manual, there can be hell to pay.

Same for your coworkers, other family members, even acquaintances. The manual is right there in plain sight in your head. How could they not know that they’re supposed to return your phone call within 30 minutes? It’s right there in the manual!

It seems cartoonish. But play with this point of view for a few days. Notice how many things – both positive and negative – you project onto others that are based on your version of how they should be behaving. What expectations do you have, based on the manual you wrote, for how they’re supposed to behave?

Now ask yourself, in that big stack of manuals you’ve authored for how others’ brains should work, where is your manual? If you want to improve all your relationships, toss out all of those manuals and keep only one. The one with your name on the spine. Now focus on improving that one manual.


Photo by Ying Ge on Unsplash

Agile Money

In a recent conversation with colleagues we were debating the merits of using story point velocity as a metric for team performance and, more specifically, how it relates to determining a team’s predictability. That is to say, how reliable the team is at completing the work they have promised to complete. At one point, the question of what is a story point came up and we hit on the idea of story points not being “points” at all. Rather, they are more like currency. This solved a number of issues for us.

First, it interrupts the all too common assumption that story points (and by extension, velocities) can be compared between teams. Experienced scrum practitioners know this isn’t true and that nothing good can come from normalizing story points and sprint velocities between teams. And yet this is something non-agile savvy management types are want to do. Thinking of a story’s effort in terms of currency carries with it the implicit assumption that one team’s “dollars” are not another team’s “rubles” or another teams “euros.” At the very least, an exchange evaluation would need to occur. Nonetheless, dollars, rubles, and euros convey an agreement of value, a store of value that serves as a reliable predictor of exchange. X number of story points will deliver Y value from the product backlog.

The second thing thinking about effort as currency accomplished was to clarify the consequences of populating the product backlog with a lot of busy work or non-value adding work tasks. By reducing the value of the story currency, the measure of the level of effort becomes inflated and the ability of the story currency to function as a store of value is diminished.

There are a host of other interesting economics derived thought experiments that can be played out with this frame around story effort. What’s the effect of supply and demand on available story currency (points)? What’s the state of the currency supply (resource availability)? Is there such a thing as counterfeit story currency? If so, what’s that look like? How might this mesh with the idea of technical or dark debt?

Try this out at your next backlog refinement session (or whenever it is you plan to size story efforts): Ask the team what you would have to pay them in order to complete the work. Choose whatever measure you wish – dollars, chickens, cookies – and use that as a basis for determining the effort needed to complete the story. You might also include in the conversation the consequences to the team – using the same measures – if they do not deliver on their promise.


Photo by Micheile Henderson on Unsplash

Innovation and Limits to Growth

In a basic growth model, some finite resource is consumed at a rate such that the resource is eventually depleted. When that happens the growth that was dependent on that resource stops and the system begins to collapse. If it happens that the resource is renewable eventually the rate of consumption matches the rate of renewal and the system enters into a state of equilibrium (no growth). This is illustrated by the black line in Figure 1. In this second scenario if the rate of consumption exceeds the rate of renewal the system will again collapse.

In the Solow model of growth (neoclassical growth model) a new element is introduced: the effect of technology or innovation on the growth curve. Without innovation, in systems where technology stays fixed, growth will eventually stop. The introduction of innovative solutions to resource problems, however, has the effect of raising the upper bound to growth limits. This is illustrated by the red line in Figure 1.

Figure 1 - Innovation Boost
Figure 1 – Innovation Boost

A prevailing assumption with innovation is that it is necessarily synonymous with invention. To be innovative is to create something that has not previously existed. This is an erroneous assumption. History is filled with accounts of dominant societies furthering their success by adopting innovative discoveries made by smaller societies. The adoption of Arabic numerals by countries that had previously used Roman numerals is a striking example of a dominant society integrating an innovation from a smaller society.

The challenge for an organization, then, isn’t so much how to be innovative, rather, how to better recognize and adopt innovations discovered elsewhere. More succinctly, how to better seek out and distinguish innovative solutions aligned with the organization’s strategy from those that simply rate high on the coolness scale.

False Barriers to Implementing Scrum – II

In a previous post, I described several barriers to implementing scrum. Recently, an additional example came to light similar to the mistake of elevating scrum or Agile to a philosophy.

In a conversation with a colleague, we were exploring ways on how we might drive interest for browsing the growing wealth of Agile related information being added to the company wiki.  It’s an impressive collection of experiences of how other teams have solved a wide array of interesting problems using Agile principles and practices. Knowing that we cannot personally attend to every need on every project team, we were talking through various ways to develop the capacity for exploration and self-education. I think I must have used the phrase “the information is out there and readily available” a couple of times to many because my colleague reacted to where I put the bar by comparing learning Agile to surgery.

Using the surgery metaphor, she pressed the comparison that all the information she needs about surgery is “out there and readily available” but even if she knew all that information she likely wouldn’t be a good surgeon. Fair point that experience and practice are important. And if that is the case, then everyone should be taking every opportunity they can to practice good agile rather than regressing to old habits.

More importantly, perhaps, is the misapplied metaphor. Practicing agile isn’t as complicated as surgery or rocket science or any other such endeavor that requires years of deep study and practice. Comparing it to something like that places the prospects of doing well in a short amount of time mentally beyond the reach of any potential practitioners.

Perhaps a better metaphor is the opening of a new rail line in the city. A good measure of effort needs to be expended to educate the public on the line’s availability, the schedules, how to purchase fares, where the connections are, what are the safety features, etc. Having done that, having “put the information out there where it is generally available,” it is a reasonable expectation that the public will make the effort to find it when they need it. It is unreasonable, and unscaleable, to build such a system and then expect that every passenger will be personally escorted from their front door to their seat on the train.

It is also interesting to consider what this does to the “empathy scale” when such an overextended metaphor is applied to efforts such as learning to practice Agile. If we frame learning Agile as similar to surgery then as people work to implement Agile are we more inclined to have an excessive amount of empathy for their struggles and be more accepting or accommodating of their short comings?

“Not to worry that you still don’t have a well formed product backlog. This is like surgery, after all.”

Are we as an organization and each of our employees better served by the application of a more appropriate metaphor, one that matches the skill and expectations of the task?

“We’ve provided instruction as to what a product backlog is and how to create one. We’ve guided you as you’ve practiced refining a product backlog. You know where to find suggestions for improving your skills for product backlog stewardship (wiki, books, colleagues, etc). Now role up your sleeves and do the work.”

Successful coaching for developing the ability in team members for actively seeking answers requires skillfully letting them struggle and fail in recoverable ways. It is possible to hold their hand too long. To use another metaphor, provide whatever guidance and instruction you need to so they know how to fish, then let them alone to practice casting their own line.


Photo credit: langll

Friends, Guides, Coaches, and Mentors

The “conscious competence” model for learning is fairly well known. If not explicitly, than at least implicitly. Most people can recognize when someone is operating at a level of unconscious incompetence even if they can’t quite put their finger on why it is such a person makes the decisions they do. Recognizing when we ourselves are at the level of unconscious incompetence is a bit more problematic.

A robust suite of cognitive biases that normally help us navigate an increasingly complex world seem to conspire against us and keep us in the dark about our own shortcomings and weaknesses. Confirmation bias, selective perception, the observer bias, the availability heuristic, the Ostrich effect, the spotlight effect and many others all help us zero in on the shiny objects that confirm and support our existing memories and beliefs. Each of these tissue-thin cognitive biases layer up to form a dense curtain, perhaps even an impenetrable wall, between the feedback the world is sending and our ability to receive the information.

There is a direct relationship between the density of the barrier and the amount of energy needed to drive the feedback through the barrier. People who are introspective as well as receptive to external feedback generally do quite well when seeking to improve their competencies. For those with a dense barrier it may require an intense experience to deliver the message that there are things about themselves that need to change. For some a poorly received business presentation may be enough to send them on their way to finding out how to do better next time. For others it may take being passed over for a promotion. Still others may not get the message until they’ve been fired from their job.

However it happens, if you’ve received the message that there are some changes you’d like to make in your life and it’s time to do the work, an important question to ask yourself is “Am I searching for something or am I lost?”

If you are searching for something, the answer may be found in a conversation over coffee with a friend or peer who has demonstrated they know what you want to know. It may be that what you’re looking for – improve your presentation skills, for example – requires a deeper dive into a set of skills and it makes sense to find a guide to help you. Perhaps this involves taking a class or hiring a tutor.

If you are lost you’ll want to find someone with a much deeper set of skills, experience, and wisdom. A first time promotion into a management position is a frequent event that either exposes someone’s unconscious incompetence (i.e. the Peter Principle) or challenges someone to double their efforts at acquiring the skills to successfully manage people. Finding a coach or a mentor is the better approach to developing the necessary competencies for success when the stakes are higher and the consequences when failing are greater.

A couple of examples may help.

When I was first learning to program PCs I read many programming books cover to cover. It was a new world for me and I had very little sense of the terrain or what I was really interested in doing. So I studied everything. Over time I became more selective of the books I bought or read. Eventually, I stopped buying books altogether because there was often just a single chapter of interest. By the time I concluded my software development career, it had been many years since I last picked up a software development book. This was a progression from being lost at the start – when I needed coaches and mentors in the form of books and experienced software developers – to needing simple guidance from articles and peers and eventually to needing little more than a hint or two for the majority of my software development career.

A more recent example is an emergent need to learn photography – something I don’t particular enjoy. Yet for pragmatic reasons, it’s become worth my time to learn how to take a particular kind of photograph. I needed a coach or a mentor because this was entirely new territory for me. So I hired a professional photographer with an established reputation for taking the type of photograph I’m interesting in. My photography coach is teaching me what I need to know. (He is teaching me how to fish, in other words, rather then me paying him for a fish every time I need one.)

Unlike the experience of learning how to program – where I really didn’t know what I wanted to do – my goal with photography is very specific. The difference had a significant influence on who I choose as guides and mentors. For software development, I sought out everyone and anyone who knew more than I. For photography, I sought a very specific set of skills. I didn’t want to sit through hours of classes learning how to take pictures of barn owls 1,000 meters away in the dark. I didn’t want to suffer through a droning lecture on the history of camera shutters. Except in a very roundabout way, none of this serves my goal for learning how to use a camera for a very specific purpose.

Depending on what type of learner you are, working with a mentor who really, really knows their craft about a specific subject you want to learn can be immensely more satisfying and enjoyable. Also, less expensive and time consuming. If it expands into something more, than great. With this approach you will have the opportunity to discover a greater interest without a lot of upfront investment in time and money.

Root Causes

The sage business guru Willie Sutton might answer the question “Why must we work so hard at digging to finding the causes to our problems?” by observing “Because that’s where the roots are.”
Digging to find root causes is hard work. They’re are rarely obvious and there’s never just one. Occasionally, you might get lucky and trip over an obvious root cause (obvious once you’ve tripped over it.) Most often, it’ll require some unknown amount of exploration and experimentation.

Even so, I’ve watch as people work very hard to avoid the hard work needed to find root causes or fail to acknowledge them even when they are wrapped around their ankles. It’s an odd form of bikeshedding whereby the seemingly obvious major issues are ignored in favor of issues that are much easier to identify, explain, or understand.

One thing is certain, you’ll know you’ve found a root cause when one of two things happen: You implement a change meant to correct the issue and a whole lot of other things get fixed as a result or there is noisy and aggressive resistance to change.

Poor morale, for example, is often a presenting symptom mistaken for a root cause. The inexperienced (or lazy) will throw fixes at poor morale like money, happy hours, or other trinkets. These work in the very short term and have their place in a manager’s toolbox, but eventually more money becomes the new low pay and more alcohol has it’s own very steep downside.

Morale is best understood as a signal for measuring the health of the underlying system. Poor morale is a signal that a whole lot of things are going wrong and that they’ve been going wrong for an extended period of time. By leveraging a system dynamics approach, it’s relatively easy to make some educated guesses about where the root causes may be. That’s the easy part.

The hard work lies with figuring out what interventions to implement and determining how to measure whether or not the changes are having the desired effect. A positive shift in morale would certainly be one of the indicators. But since it is a lagging indicator on the scale of months, it would be important to include several other measures that are more closely associated with the selected interventions.

There are other systemic symptoms that are relatively easy to identify and track. Workforce turnover, rework, and delays in delivery of high dependency work products are just a couple of examples. Each of these would suggest a different approach needed to resolve the underlying issues and restore balance to the system dynamics behind a team or organization’s performance.

How to know when Agile is working

On a flight into Houston several years ago, 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 connecting flight. Our luggage didn’t arrive at the claim carousel for an hour and a half after landing. 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.

At the airport and on my development teams 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 all the time. That was never the promise. Agile 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
  • 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

Photo by Jason Hafso on Unsplash