Coming out of the closet - the life and adventure of a traditional project manager turned Agilist

I’m coming out of the closet today. No, not that closet. Another closet, the tabu closet in the Agile community. Yes, I was (and to a point still am) a control freak, traditional, command and control project manager. Yes, that’s right you read it correctly. Here’s why this is important: in 2003 when I first started to consider Agile in any shape or form I was a strong believer of the Church of Order. I did all the rites of passage, I did my Gantt charts, my PERT charts, my EVM-charts and, of course, my certification.

I was certified Project Manager by IPMA, the European cousin of PMI.

I too was a control freak, order junkie, command and control project manager. And I've been clean for 9 years and 154 days.

Why did I turn to Agile? No, it wasn’t because I was a failed project manager, just ask anyone who worked with me then. It was the opposite reason. I was a very successful project manager, and that success made me believe I was right. That I had the recipe. After all, I had been successful for many years already at that point.

I was so convinced I was right, that I decided to run our first Agile project. A pilot project that was designed to test Agile - to show how Agile fails miserably (I thought, at that time). So I decided to do the project by the book. I read the book and went to work.

I was so convinced I was right that I wanted to prove Agile was wrong. Turned out, I was wrong.

The project was a success... I swear, I did not see that coming! After that project I could never look back. I found - NO! - I experienced a better way to develop software that spoiled me forever. I could no longer look back to my past as a traditional project manager and continue to believe the things I believed then. I saw a new land, and I knew I was meant to continue my journey in that land. Agile was my new land.

Many of you have probably experienced a similar journey. Maybe it was with Test-Driven Development, or maybe it was with Acceptance Testing, or even Lean Startup. All these methods have one thing in common: they represent a change in context for software development. This means: they fundamentally change the assumptions on which the previous methods were based. They were, in our little software development world a paradigm shift.

Test-driven development, acceptance testing, lean startup are methods that fundamentally change the assumptions on which the previous software development methods were based.

NoEstimates is just another approach that challenges basic assumptions of how we work in software development. It wasn’t the first, it will not be the last, but it is a paradigm shift. I know this because I’ve used traditional, Agile with estimation, and Agile with #NoEstimates approaches to project management and software delivery.

A world premier?

That’s why me and Woody Zuill will be hosting the first ever (unless someone jumps the gun ;) #NoEstimates public workshop in the world. It will happen in Finland, of course, because that’s the country most likely to change the world of software development. A country of only five million people yet with a huge track record of innovation: The first ever mobile phone throwing world championship was created in Finland. The first ever wife-carrying world championship was created in Finland. The first ever swamp football championship was created in Finland. And my favourite: the Air Guitar World Championship is hosted in Finland.

#NoEstimates being such an exotic approach to software development it must, of course, have its first world-premier workshop in Finland as well! Me and Woody Zuill (his blog) will host a workshop on #NoEstimates on the week of October 20th in Helsinki. So whether you love it, or hate it you can meet us both in Helsinki!

In this workshop will cover topics such as:

• Decision making frameworks for projects that do not require estimates.
• Investment models for software projects that do not require estimates.
• Project management (risk management, scope management, progress reporting, etc.) approaches that do not require estimates.
• We will give you the tools and arguments you need to prove the value of #NoEstimates to your boss, and how to get started applying it right away.
• We will discuss where we see #NoEstimates going and what are the likely changes to software development that will come next. This is the future delivered to you!

Which of these topics interest you the most? What topics would you like us to cover in the workshop. Tell us now and you have a chance to affect the topics we will cover.

Contact us at vasco.duarte@oikosofy.com and tell us. We will reply to all emails, even flame bombs! :)

You can receive exclusive content (not available on the blog) on the topic of #NoEstimates, just subscribe to the #NoEstimates mailing list below. As a bonus you will get my #NoEstimates whitepaper, where I review the background and reasons for using #NoEstimates

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Picture credit: John Hammink, follow him on twitter

Humans suck at statistics - how agile velocity leads managers astray

Humans are highly optimized for quick decision making. The so-called System 1 that Kahneman refers to in his book "Thinking fast, thinking slow". One specific area of weakness for the average human is understanding statistics. A very simple exercise to review this is the coin-toss simulation.

Humans are highly optimized for quick decision making.

Get two people to run this experiment (or one computer and one person if you are low on humans :). One person throws a coin in the air and notes down the results. For each "heads" the person adds one to the total; for each "tails" the person subtracts one from the total. Then she graphs the total as it evolves with each throw.

The second person simulates the coin-toss by writing down "heads" or "tails" and adding/subtracting to the totals. Leave the room while the two players run their exercise and then come back after they have completed 100 throws.

Look at the graph that each person produced, can you detect which one was created by the real coin, which was "imagined"? Test your knowledge by looking at the graph below (don't peak at the solution at the end of the post). Which of these lines was generated by a human, and which by a pseudo-random process (computer simulation)?

One common characteristic in this exercise is that the real random walk, which was produced by actually throwing a coin in the air, is often more repetitive than the one simulated by the player. For example, the coin may generate a sequence of several consecutive heads or tails throws. No human (except you, after reading this) would do that because it would not "feel" random. We, humans, are bad at creating randomness and understanding the consequences of randomness. This is because we are trained to see meaning and a theory behind everything.

Take the velocity of the team. Did it go up in the latest sprint? Surely they are getting better! Or, it's the new person that joined the team, they are already having an effect! In the worst case, if the velocity goes down in one sprint, we are running around like crazy trying to solve a "problem" that prevented the team from delivering more.

The fact is that a team's velocity is affected by many variables, and its variation is not predictable. However, and this is the most important, velocity will reliably vary over time. Or, in other words, it is predictable that the velocity will vary up and down with time.

The velocity of a team will vary over time, but around a set of values that are the actual "throughput capability" of that team or project. For us as managers it is more important to understand what that throughput capability is, rather than to guess frantically at what might have caused a "dip" or a "peak" in the project's delivery rate.

The velocity of a team will vary over time, but around a set of values that are the actual "throughput capability" of that team or project.

When you look at a graph of a team's velocity don't ask "what made the velocity dip/peak?", ask rather: "based on this data, what is the capability of the team?". This second question will help you understand what your team is capable of delivering over a long period of time and will help you manage the scope and release date for your project.

The important question for your project is not, "how can we improve velocity?" The important question is: "is the velocity of the team reliable?"

Picture credit: John Hammink, follow him on twitter

Solution to the question above: The black line is the one generated by a pseudo-random simulation in a computer. The human generated line is more "regular", because humans expect that random processes "average out". Indeed that's the theory. But not the the reality. Humans are notoriously bad at distinguishing real randomness from what we believe is random, but isn't.

As you know I've been writing about #NoEstimates regularly on this blog. But I also send more information about #NoEstimates and how I use it in practice to my list. If you want to know more about how I use #NoEstimates, sign up to my #NoEstimates list. As a bonus you will get my #NoEstimates whitepaper, where I review the background and reasons for using #NoEstimates

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Creating options by slicing features - #NoEstimates technique

Each feature (or story) in a product backlog contains many undiscovered options. By taking features as they are without slicing them into thin slices of functionality we implicitly commit to an implementation strategy. However, when we slice features we create options that allow us to pro-actively manage the scope of a project.

Let’s return to the IT Support Ticketing System project we discussed in a previous post. A feature like the one below will not allow us to manage the scope actively.

• As an employee I want to be able to submit issues to IT so that I can fix a particular problem that prevents me from working.

The feature above is what I would call a “binary” feature. Either the employee is able to submit an issue to IT or not. This simple feature can have large implications in terms of the amount of work required to implement it. Taking the feature above and breaking it down into several smaller features or stories will allow us to make decisions regarding the implementation order, or delaying certain parts of the implementation. Let’s look at an example:

• As an employee I want to be able to email an IT issue to the IT department so that I can have a fix for a problem that prevents me from working As an IT helpdesk employee I want to have a queue of issues to handle so that I know what items I should be working on at any given time.

By slicing the original feature in this particular way we unpacked the functionality under the term “submit issues” in the original feature into two different features: Email (replaces submit) and Queue of issues (replaces the receiving end of the submission process). We’ve potentially reduced the scope of the initial feature (no need to have a system to enter IT tickets, just send an email), and we’ve given ourselves the option to implement a solution based on standard tools. The two features we created allow for a solution based on email and a spreadsheet program with shared editing, like Google Docs.

These two stories could still be implemented with a full-fledged IT issue tracking system, but that is an option. Not a mandatory outcome of the initial feature. Slicing features into separate functional parts helps us actively manage the scope by creating different implementation options that are often implicit and non-negotiable when we have larger features in the backlog.

Picture credit: John Hammink, follow him on twitter