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This is the part I wrote first. All the other parts were written to justify this coldhearted analysis on what should be the role of management in Scrum. I was convinced that there had to be something more for management to do than “support the team and get out of the way.”

Over the years, managers of all stripes, engineering managers, product managers, project managers, manager managers have complained to me, usually as a stage-whispered aside, that “agile is dead” or “scrum is not agile.” Their frustration seemed to come from several places: the lack of promised accelerated productivity, the lack of visibility (other than the sphinxlike story point’s slow burndown), and complicated answers to simple Waterfall milestone status questions.

We managers, of all flavors, have layered on a whole superstructure of improvements on top of Scrum in our quest for certainty in an uncertain world. But let’s look ourselves in the selfie: Have these improvements worked? Have we improved Scrum? Have we delivered more certainty than what Scrum originally promised? No.

Working through the Computer Science foundations of Scrum, the data structures and algorithms, I realized that all these improvements to Scrum brought about by managers like me haven’t improved Scrum but obscured a scientific model of work under a fog of superstition, old husband tales, and best practices.

So, now, after all this, what really is the role of Management in Scrum?

Scrum is system and humans are its parts

First, a quick summary of parts 1, 2, 3, 4, and 5

• I read a book on Scrum by the inventor and co-creator of Scrum and his son
• I read this book because while I’ve been supporting Scrum for more than a decade, I kept hearing about how Agile is dead and Scrum is not Agile.
• I realized two insights from a close reading of the book: managers have no formal role in Scrum (autonomous teams don’t need managers) and there is a hardcore computational basis for the many of the processes that people follow in Scrum.
• I further realized that if you don’t treat these data structures and algorithms for what they are, you don’t get the productivity and team happiness benefits of Scrum.

I bet, as an experienced scrum master, you already knew all this. But most of the management folks I run with don’t think of Scrum as a computational system. We managers tend to see Scrum as a set of new best practices for project management. This is a little like seeing Astronomy as a new a better way to cast horoscopes for Astrology.

Scrum, at its heart, is a computational system that creates a human-based machine. Scrum uses this human-based machineto accelerate productivity by removing waste from the work process. The secret of Scrum is in the constraints it puts around inefficiencies but not around creativity. The beauty of Scrum is in its economy of design. This design enables Scrum to apply to a wide range of work problems (not just software development). A side effect of Scrum is that the human-machine manages itself and its moving parts (team members) are happier than they are in with a traditional manager managed process.

If Jeff Sutherland, like Jeff Bezos, had built a private platform out of Scrum instead of a public framework, he would be rocketing people to Mars and tooling around on his billion-dollar yacht.

Treat people like machines

OK, fellow managers, here is my advice (caveat emptor)

First, leave Scrum alone. Don’t fix it. Don’t do pre-work outside of the Sprint. Don’t tell the Sprint team or the Scrum master what to do or how to do it. Let the Scrum process fix itself over time.

Second, fix the problems outside of Scrum with formal computation systems (human machines) for those folks left out of the Scrum process. Translate your work into data structures and algorithms and eliminate waste. Don’t worry about whether the computation will be performed by silicon or carbon.

Scrum does an excellent job of work-as-computation at high efficiency. It does this by creating formal roles for the people who Sprint and ensuring that all work is filtered for priority and done with in a predictable, repeatable, time-boxed process.

BTW, this process of treating people like machines is nothing new!

The first computers were not made of silicon and software. They were people. For thousands of years people were doing the computing that enabled empires to trade, businesses to serve customers, and NASA to send rockets to the moon. Only within my lifetime have we delegated computation to non-humans.

I sense your eyebrows rising sharply! Managers who treat people like machines are inhumane.

And you are right. If we don’t follow Scrum’s model of how to compute well with people, then we managers are the living incarnation of Dilbert’s pointy-haired boss. We are micromanagers who make buzzwords out of useful tools like Agile, Scrum and DevOps. But if we don’t treat our people like machines what are we treating them like? Resources? Head counts? Soft capital?

So, if you think about it, as a manager, you pretty much treat your people like machines at some level. You give them tasks, expect them to ask relevant questions, and then to do the task to your specifications by the due date. You expect high-functioning employees to work well with vague input and all the rest to require SMART input. You don’t expect the employee’s feelings to impact the work. You are not a monster, but you have a business to run.

It is interesting to note that the people-treated-like-machines who follow a Scrum practice are far happier than their beleaguered and belabored non-Scrum counter parts Why is that?

Formal (systems) beats casual (anything)

I know we live in an age of the casual work environment. Dress codes are relaxed, hours are flexible, and hierarchies, while still in use, have been hidden away like ugly relics of a less enlightened age. But only the outside of the workplace is casual. On the inside our workplaces are just as formal as they have always been. I believe the patina of unscripted, casual interaction has made the workplace hard to navigate and an unhappier place.

Let’s contrast the formalism of Scrum with the casualism of the rest of the office:

We can easily see why the members of a Scrum are happier than the members of a Non-Scrum. Formalism brings clear boundaries so that employees know what they are doing, how well they are doing, and when they are finished. Non-Scrum team members might work all night on a project and find they failed because they didn’t work with the right info, or the right people, or the right priority. This kind of work-tragedy brings tears of frustration to the most experienced and valuable employees and leads to cynicism and other productivity busters that we managers are supposed to be managing out of the organization!

Because Scrum embraces and thrives on change the RACI is never out of date! Inside the sprint the priorities, the work to do, the due dates, the team members, and the estimated labor values do not change! Outside the sprint management brings everything the team has to do up to date. As a manager who prides himself on closing and finishing, I love the elegant efficiency of Scrum. I don’t know how other managers in other departments cope without Scrum.

We managers need not to fix Scrum but to fix ourselves. The dev team has become super effective. We, engineering management, product management, project management, and all the other managements need to catch up. We need formal systems of our own, similar to Scrum in the sense that they use data structures and algorithms to eliminate waste and accelerate work. 

Pavley.com presents, the penultimate episode of ITSAM! Starring the algorithms of Scrum. The computational thinking that makes it possible to do “twice the work in half the time.”

Last episode, part 4, starred the story point as a data structure of enumerated values and its function as a signal of complexity. Story points are expressed as Fibonacci numbers, ratios of intuitively accelerating magnitude. The humble but nuanced story point is like the pitch of the teeth in the gear that runs your sprint iteration: The finer the pitch (smaller the story point values) the faster your productivity flywheel turns.

In this episode we turn away from story points and take a step back to discuss four unambiguously defined recipes that precisely describe a sequence of operations that drive the Scrum process. Scrum is often visualized as a set of nested loops and we’re going to do the same. These loops take an input state, the backlog, and transform it by iterations, into an output state, working software.

Ah, but there is a catch! People are not machines. We tend to mess with the sequence and order of Scrum operations and derail the efficiency of its algorithms and then wonder why “Agile is dead.”

The algorithms of Scrum

What an algorithm is and is not is critical to understanding how to Scrum. Get it right and the Scrum fly wheel spins faster and faster. Get it wrong and the Scrum fly wheel wobbles and shakes, eventually flying off of its axis.

At the surface, almost any well-defined and repeatable process is an algorithm. Counting on your fingers, singing Baby Shark, and the spelling rule i before e except after c are more or less algorithms. To be a true computational algorithm all variation has to be nailed down. If human judgement is required in implementing an algorithm, as in knowing the random exceptions to the i before c rule, the algorithm isn’t reliable or provable. 

Jeff and JJ Sutherland, in their book Scrum: The Art of Twice the Work in Half the Time, don’t mention algorithms. Probably because what I’m calling algorithms don’t strictly fit the Wikipedia definition. But I believe if we refine these processes as close to true computation as we can get, Scrum works well. I believe it because I’ve seen it! So, let’s take a quick survey of each core algorithm in turn–we’re looping already.

The sprint (outer loop)

The outer loop of Scrum is the sprint. It’s a relatively simple Algorithm.

// pseudo-code implementation of a sprint loop
while value of epic count doesn’t yet equal 0 {
  play planning poker() with highest-priority epic
  for each work day in sprint duration {
  	standup() with sprint backlog for 15 mins
  }
  demo()
  if demo is not accepted {
    throw sprint broken error()
  }
  retrospective()
}

I like the idea of the sprint as algorithm because there isn’t a lot of room for human creativity. But there are a few hidden constraints!

• Scrum doesn’t want you to rest or waste time between sprints. Start the next sprint on the next working day.
• Scrum wants the whole team participating in the sprint.
• Scrum doesn’t want you to start new a sprint before the last one has completed.
• Most importantly: Scrum wants all development activities to take place inside the sprint. This constraint creates a huge headache for product management, UX design, and QA as they are commonly practiced.

One reason Agile is dead and Scrum’s hair is on fire is that anything that happens outside the sprint is not Scrum, does not go fast, and creates terrible stories. 

For example, designing all your screens upfront with focus groups is not Scrum. Manually testing all your code after the demo is not Scrum. Skipping the demo, adding more engineers during the sprint, or asking engineers to work harder is not Scrum. The sprint loop with its constraints works really well if you don’t do any work outside the sprint!

Planning poker (pre-condition)

The first thing a Scrum team does on the first working day of a sprint is to plan. The core of that meeting is the planning poker algorithm. It takes patience and practice to get right.

// pseudo-code implementation of planning poker
while consensus is not true {
  product ower explains story
  team asks clarifying questions
  for each developer in sprint team {
    compare story to previously developed story
    estimate work using story point value
    present estimate to team
  }
  if story points match {
    set consensus to true // breaks the loop
  }
}

The goal is to transform an epic into a prioritized backlog for the sprint. That means to break a vague unworkable narrative into a specific, measurable, achievable, realistic, and time-bound (SMART) story—and discovering new stories in the process. The result of planning poker is pre-condition, a state to which the backlog needs to conform, to enable a successful sprint.

In many Agile processes an epic is sometimes groomed or broken into stories before the sprint. It’s an honest attempt to get ahead of the game. But, honestly, breaking down an epic without the team playing planning poker means you get all the bad qualities of Waterfall–the qualities that Scrum was created to avoid.

Daily standup (inner loop)

Have you ever been stuck in a status meeting with no ending in sight and most of the participants in the room paying attention to their phones and not the person speaking? The daily standup algorithm was created to banish the status meeting from the realms of humankind.

// pseudo-code implementation of daily standup
accomplishments = List()
today's work = List()
impediments = List()
timer() start for 15 minutes
  for each developer in sprint team {
    announce() accomplishments, append to team accomplishments list
    announce() today’s work, append to team today’s work list
    announce() impediments, append to team impediment list
  }
  if timer() rings {
    throw standup duration error()
  }
}
timer() stop

I personally think this algorithm works for all types of work, not just development. Without a strict, formal model to follow, status meetings become planning meetings, brainstorming meetings, complaint sessions, political battle grounds, ad in finitum.

High performing Scrum teams hardly ever drift from the classic daily status formula as described by Jeff Sutherland. Unfortunately, I’ve seen struggling teams given into temptation and turn a good daily standup into a bad trouble shooting meeting. Don’t do it! Go around the room, check the boxes, and follow up with a cool head after all the accomplishments, today’s work, and impediments have been collected (so you know to start with the most urgent issues).

Retrospective (post-condition)

I have to admit that the retrospective is my favorite part of the sprint process. If you do it well and stick to the algorithm a poor performing Scrum process naturally evolves into a high performing Scrum process.

// pseudo-code implementation of daily standup
keep doing = List()
stop doing = List()
change = List()
for each member in sprint team {
  // includes product owner, devs, any other core team members
  announce() what went well, append to the keep doing list
  announce() what didn’t go, append to the stop doing list
  announce() what needs to change, append to change list
}

Like the daily stand up it takes a surprising amount of resolve to stick to the plan and not turn the retrospective into a war crimes trial or a cheerleading exercise. Oddly, the other major problem with the retrospective is lack of follow-up! We get these great lists of things to repeat, to stop repeating, and to change but many times they go nowhere.

It’s important to drive the items on each list into SMART territory so that a manager can do something about them. Noting that “the backlog was not well groomed” or “the stories needed more refinement” just isn’t enough signal to result in a meaningful change. And, of course, there are issues that can’t or won’t change. They have to be worked around.

While the retrospective is very much like a computational algorithm your response to its findings has to be creative and bold. After every retrospective I expect a scrum master to barge into my office, interrupt whatever I’m doing, and hand me a list of what must change. It’s the one output of the Scrum process that an engineering manager can participate in and it doesn’t happen often enough!

As our heroes, the algorithms of Scrum, walk arm-in-arm into the sunset let’s review the basic tenet of we what learned: The more you treat the sprint, planning poker, the daily standup, and the retrospective like the gears in a clockwork engine, the faster that engine runs. There is plenty of room outside of these algorithms but resist the temptation to add value. You’ll probably be surprised at how Scrum works when you respect it and don’t try to fix it.

In our next and final installment of ITSAM I’m going to actually talk about management: If a manager’s job doesn’t involve giving orders, taking temperatures, and holding people accountable–what is her job? Why do we even need managers if we have Scrum?

Our story so far: in part 3 I described the Scrum team as a data structure—an undirected graph. I tried to show how the properties of an undirected graph predict how a Scrum team behaves and how it can be optimized for productive behavior. Part of that optimization is keeping teams small, eliminating hubs, and breaking the sprint if anything doesn’t go as planned. Undirected graphs are harsh but if we respect them, they will reward us.

Today we’re looking at the third major data structure of Scrum: the story point. OMG! Let me just say that story points are the most powerful and most misunderstood idea in Scrum. Because story points are expressed as integers, its hard for even for experience engineering managers like me not to mistake them for integers.

The story point

This series of blog posts has become for me, my own A Song of Ice and Fire. Author George R.R. Martin originally estimated that he was writing a trilogy. But as Martin started writing, the series became six and now seven books. Honestly, I don’t trust Martin’s estimate of seven books. Given how popular “Game of Thrones” has become, if Martin lives forever, I expect he will be writing ASOIAF books forever.

When I started out writing an Introduction to Scrum and Management, I took my detailed notes from reading Jeff and JJ Sutherland’s book Scrum: The Art of Twice the Work in Half the Time and estimated I could express myself in three blog posts, maybe four just to be on the safe side. I need to time box my projects as spending too much time on any one project steals valuable time from others. As you can see from the subtitle of this post (Part 4 of 5 or 6) my estimate of the number of parts continues to increase. My project is over budget and the final post is delayed!

Jeff Sutherland, a good engineering manager, knows that people are terrible at estimating effort. Sutherland knows that less than one third of all projects are completed on time and on budget. He also knows that there are many reasons for this (poor work habits, under- or over-resourced teams, impediments that never get addressed) but the root cause is our inability to estimate timing (unless we have done the task before and have transformed it into a repeatable process).

The problem with writing fantasy novels and software is that they are not repeatable processes.

This is why Sutherland invented story points and George RR Martin still write his novels with WordStar running on a DOS PC. Since Sutherland and Martin cannot control the creative process, they put constraints around it.

The story point was invented by Jeff Sutherland because human beings really can’t distinguish between a 4 and 5. Jeff was looking for a sequence of numbers where the difference between each value was intuitive. Jeff realized that the Fibonacci numbers, a series of numbers that are known as the Golden Ratio, were the perfect candidates to do the job of estimating work. Art lovers, architects, mathematicians, and scientists, all agree that the world around us is built upon a foundation of Fibonacci numbers.

I could muse for endless paragraphs on how Fibonacci numbers are so elegant that they enable artists and artichokes alike to create beautiful compositions. But let’s just take Fibonacci numbers for granted and see how they are used to implement story points.

Here are the first eight Fibonacci numbers. It is easy to see that as the numbers increase in value the difference between each number increases. This acceleration in difference is in harmony with our ability to detect fine differences at a small scale but not a large scale.

1, 1, 2, 3, 5, 8, 13, 21

Each number in the sequence is the sum of the pair of numbers that immediately proceed it. You can do the math if you don’t want to take my word for it!

A diagram of Fibonacci squares shows the magnitude of Fibonacci progression nicely.

But let’s back up a bit. Why do we need Fibonacci numbers? We’re developing software not paintings or artichokes!

In Scrum a story is a simple description of a chunk of work to do. A sprint is a repeating and limited duration of time in which to do work. Since the work to be done is creative, it can’t fully be understood until the worker is doing it. Thus Scrum constrains process of doing the work but not the work itself.

In summary

• Stories constrain the definition of work
• Sprints constrain the time allotted to work
• Story points constrain the amount of work based on a story that is planned to be executed during a sprint.

If you have done something before, and absolutely nothing has changed, then you don’t need story points. But almost all software development projects involve new requirements, new technologies, and new techniques. When planning a software development project, the big problem is where to start. It’s hard to know how to break down a big project into nicely workable chunks.

Story points get the developers and product owner talking about where to start and how to break the problem down. In discussion during the sprint planning meeting, 13-point stories are broken into several 8-point stories. 8-point stories are broken down into many 5-pointers. And so on until all that is left are dozens if not hundreds of 1-point stories (which are, by their nature, very well understood stories).

Scrum masters and engineering managers know that a 13-point story isn’t dividable into one 5-pointer and one 8-pointer! A backlog of story points is not communicative, associative, or distributive like the ordinary numbers we grew up with. Story points can’t be added, subtracted, multiplied or divided together.

We also know that one team’s 13-point story is another team’s 21-point story. Story points are relative to the team, they change in value as the team gets better (or worse), and are not comparable unless the same people have worked together on the same project for hundreds of sprints.

As a data structure the enumerated values of story points are a wonderful set of flags, where the different between each flag is intuitive. Story points are signals not units.

Alright, this blog post was a bit long but in my defense story points are a nuanced concept. I think we’re just about at the end–which should be a relief to all of us. The good news is that my ability to estimate has significantly improved by doing the work. In the next blog post I’m going to talk about the Algorithms of Scrum.

Next, the penultimate episode, part 5

Ah, I can see from those weary, sleepy eyes, that like me, you are obsessed with improving your team’s WIP (work in progress). Stick with me and we’ll get to the bottom of the productivity conundrum with the power of our computational thinking!

In part two, I listed the three data structures and four algorithms of Scrum as described in Jeff and JJ Sutherland’s book Scrum: The Art of Twice the Work in Half the Time. I also dug deeply into the first data structure, the prioritized backlog, which from a computer science POV looks a lot like a sorted queue. I explained that if you don’t treat the backlog exactly like a queue you break your sprint and have to throw your sprint planning away and start over. Accessing an end of a queue enables O(1) efficiency. Accessing some random element in a queue… well let’s just say there be dragons of O(unknown).

In today’s blog post we’re going to look at the second data structure of Scrum, the team (an undirected graph). Like a queue, I’ll show that if you don’t treat this data structure with respect your Scrum process will fail, your sprints will leave story points on the table, and your stakeholders will demand status reports and commitments to dates!

The team

Every Scrum team is a communications network where the nodes are the people and their communication patterns are an undirected graph. Undirected here means there is no direction to the edges between nodes. Undirected communication is what you want in a Scrum team.

In the Waterfall days a manager would get all the requirements, analyze the work, and dole it out to the team members. If a team member had a question, she had to ask the boss for clarification. That kind of communications network is known as a directed graph and in particularly bad organizational patterns it becomes hub and spoke where bosses talk to bosses and team members talk to bosses and all communications require one or more hops before an answer arrives. This creates latency (delays in responses) and error (as each hop adds the opportunity for misunderstanding). 

Scrum avoids the hub and spoke model by eliminating the manager role. Any team member can talk to any other team member. Manager approval is not needed or even available. There are no hops and questions can be answered in real-time.

There is, however, a downside to a communications network based on an undirected graph model: limited scale.

If the team has 1 person, she only has to communicate with herself—which I assume is a low latency, high bandwidth connection. If the team contains 2 people, there is 1 bi-directional communication connection, or edge, between person 1 and person 2. 

So far so good! But as you add people to the team the number of potential connections between them increases with an accelerating growth rate of n * (n-1)/2. If we drop all the constants, we get O(n^2)—quadratic complexity. This means with each additional team member, communications become more and more difficult—if not impossible.

A team of 10 people creates an almost intolerable communications situation! There are 45 possible edges in an undirected graph with 10 nodes. This means a great deal of potential chatter, as many as 45 conversations happening simultaneously, with each person having to juggle threads with up to 9 other people. This also means treaded conversations in a large chat room become unreadable.

Jeff Sutherland knows all this. He’s a CTO. Scrum, as Jeff created it, requires you to keep the team small. As small as possible. 

This is also why you can often speed up a project by reducing the number of people involved. If a team of 10 is reduced to 8, then there are roughly 38% less possible conversations and each team member only has to ask up to 7 people (in the worst case) a question before she finds someone who can give her the answer. Theoretically, 8 people will accomplish less story points per sprint than 10 people. In practice communication efficiency gives a real-world advantage to the smaller team.

I want to empathize that not having a hub (a boss) and keeping the team small (less than 10) are hard requirements of Scrum. If you need a bunch of managers (engineering manager, project manager, scrum master, product owner) supervising the team you’re adding latency, error, and hops to your undirected graph. This is also why the ideas that the “product owner is the CEO of the team” or that the “Scrum master is the Engineering Manager” are bad ideas.

Now wait a minute, Mr. Pavley! We need all these bosses! What if something goes wrong during the sprint? What if a story is wrong? What if new work comes in? What if an engineer needs help that her team members can’t supply?

Break the sprint. Redo the plan. Start over. Design a new team and a new backlog. Before and after the sprint bring in all the bosses you want! Just leave the team alone during the sprint.

If you are still as excited as I am about getting Scrum to actually work as advertised, the next installment of Introduction to Scrum and Management will explore story points and Fibonacci numbers, the best numbers in the whole world!

And here is part 4!

Welcome back! In part one, I expressed my dismay that Scrum was conceived with no formal role for management, especially not Engineering Management. I also claimed that Scrum is Agile, that Scrum is not dead, and that Scrum was created long before the hyperconnected Internet we now inhabit came into being. I found that Jeff and JJ Sutherland’s book, Scrum: The Art of Doing Twice the Work in Half the Time, helped me, after years of supporting Scrum, actually understand Scrum.

In this blog post I want to dig a little deeper into how Scrum works from an engineering perspective. When I told my team (in our internet chat room) that I was reading the Sutherlands’ book, one of the comments I got went something like this: “2x the work in 1/2 the time… that sounds too good to be true!”

And you know something? The that engineering manager was right: “Twice the work in half the time” is what a gonzo diet supplement promises. Any engineer with good critical thinking skills is going be skeptical of a process that promises to break the laws of Physics.

But Scrum is not snake oil. I’ve seen it work time and time again. I’ve also seen it fail. What separates a successful Scrum process from an unsuccessful one? And by what mechanism does Scrum accelerate work? Let’s find out!

As an engineer I think of any system, even a human run system like Scrum, in terms of data structures and algorithms. These are the building blocks that determine how a system will scale deal with bottlenecks. We can even apply Big O analysis to Scrum and see if we can predict where it will be efficient, O(1), and inefficient, O(n!).

Scrum at its heart is a computational model for work. In this model Scrum has three primary data structures and four primary algorithms.

The well-known properties of these data structures and algorithms help Scrum operate efficiently but also point to why Scrum is hard to scale. 

The prioritized backlog

A sorted queue is very fast to access: no searching needed. You dequeue one end to get the current element and enqueue the other end to add a new element. As long as you don’t randomly access elements in the middle of the queue you are always assured to get the element with the highest priority–If the elements are added to the queue in order of priority during the sprint planning process. Access is O(1), aka constant time, whether there is one or one million stories.

This is why Scrum requires the backlog to be locked during the sprint: any addition or subtraction means that all your planning efforts were for naught. Changes to the backlog during the sprint is like randomly accessing the elements of a queue. It means that your story points are no longer relative, the team’s rhythm is broken, and predictability is compromised. This is why, if the backlog must change during the sprint, the sprint is “broken” and must be started over with a new queue of work to do. Range out of bounds error

I don’t know about you, but it’s hard to get the team, including the Scrum Master and Product Owner, to break a sprint. We all just sheepishly adjust the backlog. This is especially true when a high priority story becomes blocked and an engineer is sidelined. Instead of breaking the sprint and re-planning, the engineer is usually told to just grab the next story in the backlog–which ruins the whole queue. Access out of bounds error

Right here we can see a hint of a deep computer science basis to Scrum as it was originally conceived by Jeff Sutherland and his co-creators. We can also see why it’s important to stick to the original conception to get the benefits. If you’re not breaking sprints and if you’re access stories randomly during a sprint the efficiency of this part of the process jumps from 0(1), which is a good as it gets, to some other O(n) like O(n^2) or the dreaded O(n!). Take it from an Engineering Manager: you don’t want to go there!

In the next post we’ll take a hard computational look at the scrum team and prove that less team members is always better than more. If you want to speed up, remove members from the team!

Onwards to part 3!