Relative Team Expertise and Story Sizing

In Parkinson’s Law of Triviality and Story Sizing, I touched on the issue of relative expertise among team members during collaborative efforts to size story cards. I’d like to expand on that idea by considering several types of team compositions.

Team 1 is a tight knit band of four software developers represented in Figure 1.

Figure 1 - Team 1
Figure 1 – Team 1

Their preferred domain and depth of experience is represented by the color and area of their respective circles. While they each have their own area of expertise, there is a significant overlap in common knowledge. All four of them understand the underlying architecture, common coding practices, and fundamental coding principles. Furthermore, there is a robust amount of inter-domain expertise. When needed, the HTML5/CSS developer can probably help out with JavaScript issues, for example. The probability of this team successfully working together to size the stories in the product backlog is high.

Team 1 represents a near-ideal team composition for a typical software related project. However, the real world isn’t so generous in it’s allocation of near-ideal, let alone ideal, teams. A typical team for a software related project is more likely to resemble Team 2, as represented in Figure 2.

Figure 2 - Team 2
Figure 2 – Team 2

In Team 2, the JavaScript developer is fresh out of college,  new to the company and new to the business. His real-world experience is limited so his circle of expertise is smaller relative to his teammates. The HTML5/CSS developer has been working for the company for 10 years and knows the business like the back of her hand. So she has a much wider view of how her work impacts the company and product development. As a team, there is much less overlap and options for helping each other through a sprint is diminished.  As for collaborative story sizing efforts, the HTML5/CSS and C# developers are likely to dominate the conversation while the JavaScript developer agrees with just about anything not JavaScript related.

As Agile practices become more ubiquitous in the business world, team composition beings to resemble Team 3, as shown in Figure 3.

Figure 3 - Team 3
Figure 3 – Team 3

The mix now includes non-technical people – content developers and editors, strategists, and designers. Even assuming an equal level of experience in their respective domains, the company, and the business environment, there is very little overlap. Arriving at a consensus during a story sizing exercise now becomes a significant challenge. But again, the real world isn’t even so kind as this. We are increasingly more likely to encounter teams that resemble Team 4 as shown in Figure 4.

Figure 4 - Team 4
Figure 4 – Team 4

As before, the relative circle of expertise among team members can vary quite a bit. When a team resembles the composition of Team 4, the software developers (HTML5/CSS and C#) will have trouble understanding what the Learning Strategist is asking for while the Learning Strategist may not understand why what he wants the software developers to deliver isn’t possible.

When I’ve attempted to facilitate story sizing sessions with teams that resemble Team 4 they either become quite contentious (and therefore time consuming) or team members that don’t have the expertise to understand a particular card simply accept the opinion of the stronger voices. Neither one of these situations is desirable.

To counteract these possibilities, I’ve found it much more effective to have the card assignee determine the card size (points and time estimate) and work to have the other team members ask questions about the work described on the card such that the assignee and the team better understand the context in which the card is positioned. The team members that lack domain expertise, it turns out, are in a good position to help craft good acceptance criteria.

  • Who will consume the work product that results from the card? (dependencies)
  • What cards need to be completed before a particular card can be worked on? (dependencies)
  • Is everything known about what a particular card needs before it can be completed? (dependencies, discovery, exploration)

At the end of a brief conversation where the entire team is working to evaluate the card for anything other than level of effort (time) and complexity (points), it is not uncommon for the assignee to reconsider their sizing, break the card into multiple cards, or determine the card shouldn’t be included in the sprint backlog. In short, it ends up being a much more productive conversation if teammates aren’t haggling over point distinctions or passively accepting what more experienced teammates are advocating. The benefit to the product owner is that they now have additional information that will undoubtedly influence the product backlog prioritization.

What does Agile documentation look like?

Reading through Dusty Phillips’ second edition of “Python 3 Object-oriented Programming,” this quote caught my attention:

Further, the most important person you will ever have to communicate with is yourself. We all think we can remember the design decisions we’ve made, but there will always be the Why did I do that? moments hiding in our future. If we keep the scraps of papers we did our initial diagramming on when we started a design, we’ll eventually find them a useful reference.

That strikes me as a good benchmark for acceptable documentation in Agile. Whether coder, UI/UX designer, data architect, or whatever, if you are keeping a good record of what you decided and why, you’ll probably be able to recreate the rationale for why things got to be the way they are for anybody who needs to know. Especially if that anybody is you. And there is a good chance that someone following in your footsteps will be able to pick up the same rationale even in your absence. All this without putting an unnecessary burden on project progress for the sake of detailed documentation.

Of course, what qualifies as “good” is the tricky part. A suggested threshold would be to specify only as much information as makes sense or for what is known given the current situation. Documentation should be subject to iterative practices just as much as code.

Responding to change over following a plan

Welcome changing requirements, even late in
development. Agile processes harness change for
the customer’s competitive advantage.

Agile Manifesto Principle #2

Following from the Agile Manifesto value that is the title of this post, Principle #2 may be the most mis-interpreted and misunderstood principle among the set of twelve. Teams frequently behave as if this principle was prefaced with the word “always.”

Constantly shifting requirements leads to a frustrating and unsatisfying environment in which to work. It feeds burn-out and loss of morale. The satisfaction of a job well done depends on the opportunity to actually finish the job, no matter how small. Consider the effects on a finish carpenter who has just spent several days installing and trimming a full set of kitchen cabinets when the homeowner declares they want to change the kitchen design such that all those new cabinets will need to be ripped out and work begun on a new design. Or a film editor who has just worked 21 days straight to pare down an hour’s worth of video to fit into 7 minutes only to learn the scene has to be re-shot from scratch in order to match a change in the storyline.

Of course, the second principle does not state we should “always welcome changing requirements.” Nor does anyone I know claim that it does. But that doesn’t stop people from behaving as if it did. The rationale offered for agreeing to change requests from the stakeholders may be “We’re an agile shop and agile welcomes changing requirements” when, in fact, the change was agreed to because the product owner didn’t challenge the value of the change or make clear the consequences to the stakeholders. Or the original design was, and remains, needlessly ambiguous. Or the stakeholders have changed without renegotiating the contract or working agreements. Or any number of reasons that are conveniently masked with “welcoming changing requirements.” At some point, welcoming changing requirements is about as attractive as welcoming a rabid dog into the house. This won’t end well.

So, what kind of change is the Agile Manifesto referring to? There are several key scenarios that embody the need for flexibility around requirements.

  • The change that results from periods of deliberate design, such as during design sprints.
  • The change that is driven by the lessons learned from exploration and prototyping. If it is understood that the work being “completed” is for the purposes of testing a hypothesis and the expectation is that the work will most likely be thrown away, there can still be a great deal of satisfaction derived from the effort as the actual deliverable wasn’t working software, but the lessons from the experiment (usually in the form of a wireframe or prototype.)

So what is it that locks out the option for additional change? It’s a simple event, really. A decision is made.

Each of these scenarios where adapting to lessons and discovery is essential nonetheless end in a decision, a leverage point from which progress can be made toward a final deliverable. Each of these decisions can themselves form the basis of a series of experiments which, depending on the eventual outcome, may change.  Often, a single decision point may look good but when several decisions are evaluated together they may suggest a new direction and therefore impact the requirements. If the cumulative insight from a series of decisions results in the need to change direction, that shift is usually more substantial and on the scale of a project plan pivot rather than a simple response to a single change in a single requirement. The need to pivot cannot reliably be revealed if the underlying decisions do not coalesce into some sort of stable understanding of the emerging design.

Changing requirements cannot go on indefinitely or a final product will never be delivered. Accepting change for the sake of change is what gets teams into trouble.

Much like the forces on evolution, there will always be some external force that seeks to change the project requirements so that the delivered product can be stronger, faster, better, taller, smarter, etc.  This must be countered by clear definitions of “minimum viable” and “good enough” relative to what the customer is expecting.

In addition, product owners would serve their teams well by vigorously challenging any proposed changes to the requirements.

  • What is the source of the change?
  • Is it random change or triggered by some agent that does not announce its arrival ahead of time?
  • Was the change in requirements a surprise? If so, why was it a surprise?
  • Will this (or something like it) happen again? With what frequency? At what probability?

See also: The Changeability Decision Matrix

Agile Interns

I had the privilege of presenting to a group of potential interns from the Colorado School of Mines interested in agile project management. Action shot…

The slide shows what we can offer them as interns: Failure, chaos, and confusion. I unpacked that as follows…


It’s important interns have the opportunity to learn how to fail in small, deliberate and safe increments along with the opportunity to learn how to extract every possible lesson from failures and how those failures lead to eventual success. Much of our business is driven by experimentation and hypothesis testing. Most of those experiments will fail, at least initially.


We strive to be anti-fragile. One way to accomplish this is to be good at working under chaotic and ambiguous conditions. When involved with highly evolutionary design sessions, shifting sands can seem like the most solid ground around.


One of the values for bringing interns into the organization is the fresh perspective they offer.  Why waste that on having them fetch coffee? However, interns can often be intimidated by working with people who have decades of experience under their belt. So it’s important they know they have the opportunity to work in an environment that expects questions and recognizes no one knows it all. They are in an environment that  seeks alternative points of view. In this organization, everyone gets their own coffee.