Estimation, planning, teams and money, some data

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When I deliver Agile training for teams I run an exercise called “The Extended XP Game”. It is based on the old “XP Game” but over the years I’ve enhanced it and added to it. We have a lot of fun, people are laughing and they still talk about it years later. The game illustrates a lot of agile concepts: iteration, business value, velocity, learning by doing, specification by example, quality is free, risk, the role of probability and some more.

When I run the exercise I divide the trainees into several teams, usually three or four people to a team. I show them I have some tasks written on cards which they will do in a two minute iteration. They do two minutes or work, review, retrospect then do another two minutes of work – and possibly repeat a third time.

The first thing is for teams to Get Ready: I hand out the tasks and ask them to estimate, in seconds how long it will take to do each task: fold a paper airplane that will fly, inflate a balloon, deflate a balloon, roll a single six on a dice, roll a double six on two dice, find a two in a pack of cards and find all the twos in the pack of cards. Strictly speaking, this estimate is a prospective estimate, “how long will it take to do this in future?”

Once they have estimated how long each task will take someone is appointed product owner and they have to plan the tasks to be done (with the team).

What I do not tell the teams is that I’m timing them at this stage. I let the teams take as long as they like to get ready: estimate and plan. But I time how long the estimation takes and how long the following planning takes.

Once all the teams are “ready” I ask the teams: “how long did that take?”

At this point I am asking for a retrospective estimate: how long did it take. The teams have perfect estimation conditions: they have just done it, no time has elapsed and no events have intervened.

Typically they answer are 5 or 6 minutes, maybe less, maybe more. Occasionally someone gets the right number and they are then frequently dismissed by their colleagues.

Although I’ve been running this exercise for nearly 10 years, and have been timing teams for about half that time I’ve only been recording the data the last couple of years. Still it comes from over 65 teams and is consistent.

The total time to get ready to do 2 minutes of work is close to 13 minutes – the fastest team took just 5.75 minutes but the slowest took a whopping 21.25.

The average time spent estimating the tasks is 7 minutes. The fastest team took 2.75 minutes and the slowest 14 minutes.

The average time planning once all tasks are estimated is just short of 6 minutes. One team took a further 13.5 minutes to plan after estimates while another took just 16 seconds. While I assume they had pretty much planned while estimating it is also interesting to note that that team contained several people who had done the exercise a few years before.

(For statistics nuts the mean and median are pretty close together and I don’t think the mode makes much sense in this scenario.)

So what conclusions can we draw from this data?

1) Teams take longer to estimate than do

Everyone taking part in the exercise has been told – several times – that they are preparing to do a 2 minute iteration. Yet on average teams spend 12.75 minutes preparing – estimating and planning – to do 2 minutes of work!

Or to put it another way: teams typically spend six times longer to plan work than to do work.

The slowest team ever took over 10 times longer to plan than to do.

In the years I’ve been running this exercise no team has ever done a complete dry run. They sometimes do little exercises and time themselves but even teams which do this spend a lot of time planning.

This has parallels in real life too: many participants tell me their organization spend a long time debating what should be done, planning and only belatedly executing. One company I met had a project that had been in planning for five years.

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2) Larger teams take longer to estimate than small teams

My second graph shows there is a clear correlation between team size and the time it takes to estimate and plan. I think this is no surprise, one would expect this. In fact, this is another piece of evidence supporting Diseconomies of Scale: the bigger the team the longer it will take to get ready.

This is one reason why some people prefer to have an “expert” make the estimate – it saves the time of other people. However this itself is a problem for several reasons.

Anyone who has read my notes on estimation research (and the later more notes on estimation research) may remember that research shows that those with expert knowledge or in a position of authority underestimate by more than those who do the work. So having an expert estimate isn’t a cure.

But, those same notes include research that shows that people are better at estimating time for other people than they are at estimating time for themselves, so maybe this isn’t all bad.

However, this approach just isn’t fair. Especially when someone is expected to work within an estimate. One might also argue that it is not en effective use of time because the first person – the estimator – has to understand the task in sufficient detail to estimate it but rather than reuse this learning the task is then given to someone else who has to learn it all over again.

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3) Post estimation planning is pretty constant

This graph shows the planning delta, that is: after the estimates are finished how long does it take teams to plan the work?

It turns out that the amount time it takes to estimate the task has little bearing on how long the subsequent planning takes. So whether you estimate fast or slow on average it will take six more minutes to plan the work.

Perhaps this isn’t that surprising.

(If I’ve told you about this data in person I might have said something different here. In preparing the data for this blog I found an error in my Excel graphs which I can only attribute to a bug in Excel’s scatter chart algorithm.)

4) Vierordt’s Law holds

People underestimate longer periods of time (typically anything over 10 minutes), and overestimate short period of time (typically things less than two minutes).

Not only do trainees consistently underestimate how long it has taken them to get ready – which is over 10 minutes – but teams which record how long it takes to actually do each task find that their estimates are much higher than the actual time it takes. Even when teams don’t time themselves observation shows that they do the work far faster than they thought they would.

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5) Less planning makes more money

One of my extensions to the original game is to introduce money: teams have to deliver value, measured in money. This graph shows teams which spend less time planning go on to make more money.

I can’t be as sure about this last finding as the earlier ones because I’ve not been recording this data for so long. To complicate matters a lot happens between the initial planning and the final money making, I introduce some money and teams get to plan for subsequent iterations.

Still, there are lessons here.

The first lesson is simply this: more planning does not lead to more money.

That is pretty significant in its own right but there is still the question: why do teams which spend less time planning make more money?

I have two possible explanations.

I normally play three rounds of the game. When time is tight I sometimes stop the game after two rounds. In general teams usually score more money in each successive round. Therefore, teams who spend longer in planning are less likely to get to the third round so their score comes from the second round. If they had time to play a third round they would probably score higher than in round two.

This has a parallel in real life: if extra planning time delays the date a product enters the market it is likely to make less money. Delivering something smaller sooner is worth more.

This perfectly demonstrates that doing creates more learning than planning: teams learn more (and therefore increase their score) from spending 2 minutes doing than spending an extra 2 minutes planning.

The second possible explanation is that the more planning a team does the more difficult they might find it to rethink and change the way they are working.

The $1,600 shown was recorded by a Dutch team this year but the record is held by a team in Australia who scored over $2,000: to break into these high scores teams need to reinterpret the rules of the game.

One of the points of the game is to learn by doing. I suspect that teams who spend longer in planning find it harder to break away from their original interpretation of the rules. How can you think outside the box when you’ve spent a lot of time thinking about the box?

In one training session in Brisbane last year the teams weren’t making the breakthrough to the big money. Although I’d dropped hints of how to do this nobody had made the connection so I said: “You know, a team in Perth once scored over $2,000.” That caused one of the players to rethink his approach and score $1,141.

I’ve since repeated the quote and discovered that simply telling people that such high scores are possible causes them to discover how to score higher.

* * *

I’m sure there is more I could read into all this data and I will carry on collecting the data. Although now I have two problems…

First, having shared this data I might find people coming on my agile software training who change their behaviour because they have read this far.

Second: I need more teams to do this to gather data! If you would like to do this exercise – either as part of a full agile training course or as a stand alone exercise – please call (+44 20 3286 4292) or mail me, contact@allankelly.net, my rates are quite reasonable!

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Closing the Product Owner mini-series: they are all different!

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With some final words I’d like to draw this mini-series on the Product Owner to a close and open a new chapter with a new book. I’ve written six blog posts in the last two months and I have drafts for more but there are other things I want to blog about.

I have drafts for more posts and ideas for even more. So its time to make this into another book: Product Ownership. This is on the LeanPub site now and you can buy it. So far it just contains a new prologue story but I’ll add these posts soon as the first chapters.

Ever since I wrote Little Book of User Stories I’ve thought there should be a companion volume: “Little Book of Product Ownership”. The intention is for the first part of the new book to discuss the product owner role – and whether it should even exist – and then quickly get into the tools of Product Ownership.

Now some closing words…

While I’ve suggest a lot of things that a Product Owner should do, and a few that they should not do, there are really no hard and fast rules about what a Product Owner should or should not do.

In the language of business schools: there is no contingent way of being a product owner, every product owner and organization is different and they need to find their own path. I cannot give you a flow chart for what a product owner does or should do, nor can I give you a set of rules to say “When the customer says Foo the Product Owner should do Bar.”

Every Product Owner has to work out what is right for them because every organization is different. And every organization will – rightly or wrongly – expect different things from the people it christens Product Owner.

Additionally every team is different and contains different skills and experience. As a result every team will differ in what it needs from the Product Owner(s) and how the team members can support the Product Owner and share the work.

And every Product Owner is themselves different and brings different skills, experience and insights to the role.

Job #1 for a newly appointed Product Owner is to sit down and decide what type of Product Owner they are expected to be and what type of Product Owner they want to be:

  • They may be a Backlog Administrator taking instructions from others.
  • They may be a Subject Matter Expert using their expert knowledge of the domain to decide what the right product to build is and help other team members understand the details of what is being built.
  • They may need to analyse internal process and business lines using the skills of Business Analysis.
  • They may need to get out on the road to meet customers – and potential customers – to understand the market and where the opportunities are using the skills of Product Management.
  • They may need to call on skills from other fields to: Project Management, Consulting and Entrepreneurship to name a few.

But a Product Owner is not some other things:

  • If they were a developer they need to accept they will not be coding any more. There simply isn’t time and anyway, they need to trust the team.
  • If they were a Project Manager, Development or Line Manager they need to resist any urge to tell people what to do or look too far into the future. They need to re-focus on value not time, and recognise that their authority comes from their competence not from a position on a chart.
  • Product Owners from a Business Analysis background need to look beyond Business Analysis, specifically they need to immerse themselves in the world of Product Management.
  • While Product Owners who were Product Managers probably have the easiest ride they too need to change, they need to think more about internal stakeholders, processes and delivery.

Every Product Owner and everyone working with Product Owners needs to read and reflect on the role. Hopefully some of the words in my recent posts – and the new book – will help with that – and hopefully some of you might like to hire me for advice or a training course – just call 🙂

Finally, I sincerely believe there are better Product Owners and not-so-good Product Owners, and that some organizations (teams, companies, enterprises) which offer a better environment for Product Ownership and equally there are those which are downright hostile to product ownership.

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The Product Owner refactored: the SPO/TPO model

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Surprisingly I’ve never blogged about the Strategic Product Owner / Tactical Product Owner model, this is surprising because it is a model I both find again and again and advocate again and again.

I find lots of companies who have a version of this model in place, they have created the model to deal with their own situation. But few of these companies realise that this is a reoccurring solution and is quite legitimate. (I should write it up as a Pattern but I haven’t written any patterns for a while.)

More importantly I find that many companies and individuals faced with problems around Product Owners benefit from adopting this model. Specifically, as I’ve already mentioned there is a lot of work for a Product Owner to do and one way of doing this is to share the load.

If I were to write this up as a pattern the thumbnail version would say something like:

The Product Owner lacks the time – and sometimes skill – to fill the role fully therefore split the role in two. One person, the SPO (Strategic Product Owner), looks long term, they focus on customers and strategy. The other, the TPO (Tactical Product Owner), focuses on the near term (this sprint, the next sprint, the next quarter). The TPO spends most of their time with the delivery team while the SPO spends most of their time with customers and senior stakeholders.

Sometimes the Product Owner lacks time simply because – as I’ve said before – there is so much work the Product Owner should be doing they simply don’t have time.

Sometimes they lack time because the team is large, or the team lack domain knowledge (and therefore need to ask the PO lots of questions). Sometimes POs need to travel a lot to meet customers and even the most talented PO can’t be in two places at once.

They may also lack time because they have another job to do. While I think the Product Owner role is a full time job sometimes the person who is the right person to hold the role – usually because they command authority – needs to combine the work with another role.

For example, on a trading desk the Product Owner should probably be a senior trader who both knows the domain and has the authority to say Yes and No to features. But by definition such a person lacks time. Normally I’d want a dedicated Product Owner in place but sometimes the only way to have the necessary authority is to have another job.

And sometimes the person who is should be Product Owner – think our trader again – lacks the skills and experience to do the role. So again they need help.

The key thing about the SPO/TPO model is that the two people who hold the role need to speak with one voice. If they do not then the model will fail. Ideally the SPO will stand in when the TPO is unavailable and vice verse.

There is another occasion when the SPO/TPO model can be useful: big teams.

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Ideally there is one product owner, one team and one stream of work. But sometimes there are several products, teams and streams. Here you might have an SPO who looks at the long term and several TPOs each of whom works with one team on one stream.

Now, like all good patterns this one is not without its downsides…

I’ve heard Scrum-advocates argue against this model: One True Product Owner they say. And they have a point… putting more people between the delivery team and the customer does detract from communication.

One of the problems software development faces is when multiple people think they have the right to say what is built next. Another problem occurs when the customer is remote from the development team and multiple people mediate what is asked for.

Ideally developers can talk to customers directly but that is often not possible or desirable – I won’t go into the reasons right now. So a good solution is One True Product Owner.

But then the One True Product Owner becomes a bottleneck so we split the role SPO/TPO. Yet every-time we introduce another link – another person – between the coders and the customer the greater the propensity to introduce problems. So it becomes a balancing act.

Nobody in between can be ideal.

One person can make it better.

Two people can be an improvement over one.

Three… I need some convincing this is an improvement over two.

Four… I find it hard to believe that having four people mediate the voice of the customer is an improvement… unless of course you previously had five!

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The Product Owner is dead, long live the Product Owner!

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For years I have been using this picture to describe the Product Owner role. For years I have been saying:

“The title Product Owner is really an alias. Nobody should have Product Owner on their business cards. Product Owner is a Scrum defined role which is usually filled by a Product Manager or Business Analyst, sometimes it is filled by a Domain Expert (also known as a Subject Matter Expert, SME) and sometimes by someone else.”

Easy right?

In telling us about the Product Owner Scrum tells us what one of these people will be doing within the Scrum setting. Scrum doesn’t tell us how the Product Owner knows what they need to know to make those decisions – that comes by virtue of the fact that underneath they are really a Product Manager, BA or expert in the field.

In the early descriptions of Scrum there was a tangible feel that the Product Owner really had the authority to make decisions – they were the OWNER. I still hope that is true but more often than not these days the person playing Product Owner is more likely to be a proxy for one or more real customers.

I go on to say:

“In a software company, like Microsoft or Adobe, Product Managers normally fill the role of Product Owner. The defining feature of the Product Manager role is that their customers are not in the building. The first task facing a new Product Manager is to work out who their customers are – or should be – and then get out to meet them. By definition customers are external.”

“Conversely in a corporate setting, like HSBC, Lufthansa, Proctor and Gamble, a Product Owner is probably a Business Analyst. There job is to analyse some aspect of the business and make it better. By definition their customers are in the building.”

With me so far?

Next I point out that having set up this nice model these roles are increasingly confused because software product companies increasingly sell their software as a service. And corporate customer interact with their customers online, which means customer contact is now through the computer.

Consider the airline industry: twenty years ago the only people who interacted with airline systems from United, BA, Lufthansa, etc. were airline employees. If you wanted to book a flight you went to a travel agent and a nice lady used a green screen to tell you what was available.

Today, whether you book with Lufthansa, SouthWest or Norwegian may well come down to which has the best online booking system.

Business Analyst need to be able to think like Product Managers and Product Managers need to be able to think like Business Analysts.

I regularly see online posts proclaiming “Product Managers are not Product Owners” or “Business Analysts are not Product Owners.” I’ve joined in with this, my alias argument says “they might be but there is so much more to those roles.”

It makes me sad to see the Product Manager role reduced to a Product Owner: the Product Owner role as defined by Scrum is a mere shadow of what a good Product Manager should be.

But the world has moved on, things have changed.

The world has decided that Product Owner is the role, the person who deals with the demand side, the person decides what is needed and what is to be built.

I think its time to change my model. The collision between the world of Business Analysts and Product Managers is now complete. The result is an even bigger mess and a new role has appeared: “Digital Business Analyst” – the illegitimate love child of Business Analysis and Product Management.

The Product Owner is now a superset of Product Manager and Business Analyst.

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Product Owners today may well need the skills of business analysis. They are even more likely to need the skills of Product Management. And they are frequently expected to know about the domain.

Today’s Product Owner may well have a Subject Matter Expert background, in which case they quickly need to learn about Product Ownership, Product Management and Business Analysis.

Or they may have a Business Analysis background and need to absorb Product Management skills. Conversely, Product Owners may come from a Product Management background and may quickly need to learn some Business Analysis. In either case they will learn about the domain but they may want to bring in a Subject Matter Expert too.

To make things harder, exactly which skills they need, and which skills are most important is going to vary from team to team and role to role.

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What Product Owners should not do

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Last time I set out some of the things a Product Owner should be doing – or at least considering doing. Even a quick look at that list will tell you the Product Owner is going to be a busy person.

So in this post I’d like to suggest some thinigs Product Owners should NOT be doing.

Product Owners Cutting code should NOT be cutting code

Having a former coder in the Product Owner role can be a great boom. Not only do they know how to talk with the technical team and (hopefully) can command their respect but they can also see how technology can apply.

But to be an effective Product Owner they need to step away from the keyboard and stop writing code.

Two reasons.

One: time.
Product Owners add value by ensuring that the code which is written addresses the most valuable opportunities with the smallest, most elegant, delightful way possible.

Every minute spent coding is a minute not doing that.

Second: Product Owners need to empathise with the customer, with the business users, they need to eat-sleep-and-breath customers.

Being a good coder – let alone someone called an architect – is to empathise with code, the system, the mechanics of how a system works.

Importantly both requirements and code need to be able to come together and discuss what they see and find a way to bring the two – sometimes opposing – views together. It is a lot easier to have that discussion if the two sides are represented by different people.

Asking one person to divide their brain in two and discuss opposing views with themselves is unlikely to bring about the best result and is probably a recipe for confusion and stress.

Thats not to say both sides shouldn’t appreciate the other. I said before, former coders have a great advantage in being a Product Owner. And I want the technical team to meet customers. But I want discussions to be between two (or more) people.

(I might allow an exception here for Minimally Viable Teams but once the team moves beyond the MVT stage the PO should stop coding.)

Product Owners should NOT be line managers

OK, senior Product Owners should might line manage junior product owners but they certainly should not be line managing anyone else. Most certainly they should not be line managing the technical team.

Product Owner authority comes not from a line on an organization chart, or the ability to award (or deny) a pay rise or bonus. Product Owner authority stems from their specialist knowledge of what customers want from a product and what the organization considers valuable.

If the Product Owner cannot demonstrate their specialist knowledge in this way then either they should learn fast or they should consider if they are in the right role.

Product Owners need to trust the technical team and the technical team need to trust the Product Owner. Authority complicates this relationship because one side is allowed to issue orders when trust is absent and the other side has to obey.

And again, Product Owner simply don’t have the time to line manage anyone.

Being a good line manager requires empathy with employees and time to spend observing and talking to employees, helping them develop themselves, helping them with problems and so on.

Product Owners should not Make Promises for Other People to keep

Specifically that means they should not issue “Roadmaps” which list features with delivery dates based on effort estimates. The whole issue of estimation is a minefield, very few teams are in a position to estimate accurately and most humans are atrocious at time estimation anyway. So any plans based on effort estimation are a fantasy anyway. But even putting that to one side…

Issuing such plans commits other people to keep promises. That is just unfair.

Product Owners can create and share scenario plans about how the product – and world – might unfold in the future.

Product Owners can co-create and share capacity plans which should how an organization intends (strategically) to allocate resources. And Product Owners can work with teams in executing against those capacity plans in order to deliver functionality the Product Owner thinks should be delivered by a date the Product Owner thinks is necessary.

In other words: provided a Product Owner is making the promise that they intend to keep themselves (i.e. they have skin in the game) then they might issue some kind of forward plan.

Product Owners should dump outbound marketing at the first opportunity

Outbound marketing, e.g. advertising, press releases, public relations and product evangelism, often ends up on the Product Owner plate – particularly when the Product Owner is a Product Manager. And in a small company (think early stage start-up) this just needs to be accepted.

However, in a larger organization, or a growing start-up, Product Owners should seek to pass this work to a dedicated Product Marketing specialist as soon as possible. Both roles deserve enough time to do the job properly.

The Marketing Specialist and Product Owner will work closely together – they are after all two sides of the same coin, the Marketing coin. The Marketing Specialist handles outbound marketing (telling people about the product) and the Product Owner handles inbound marketing (what do people want from the product?). (Again, in organizations with established Product Management this is usually easier to see.)

Product Owners should dump pre-sales at the first opportunity

As with outbound marketing Product Owners often get dragged in as pre-sales support to account managers. And again this is more common in small companies and early stage start-ups.

There are some advantages to playing second fiddle to a sales person. The Product Owner might get actual customer contact (sales people too often block Product people from meeting customers.) And Product Owners should be exposed to some of the commercial pressures that sales people – and customers – encounter.

But doing pre-sales is very time consuming. And being wheeled in to help deliver a sales will distort the Product Owner’s view of the market – just ‘cos this customer wants the product in Orange doesn’t mean other customers want Orange.

And again, pre-sales is more effectively done by specialist staff as soon as the company can afford them.

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Busy busy busy: What Product Owners do

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If you hadn’t noticed I’m building a blog mini-series on the Product Owner role. Its a role I’ve long felt didn’t get the attention it should have. Frankly, in a Scrum setting, I think the Scrum Master gets too much attention and the Product Owner not enough.

One aspect in particular of the Product Owner role really annoys me: they have so much work to do.

Or rather, a Product Owners who is doing their job properly – as opposed to simply administering the backlog – has so many things they should potentially be doing.

So a few days ago I started to make a list…

Backlog administration: writing stories, reviewing and discussing suggested stories, splitting stories, weeding the backlog (throwing stories away), improving stories, putting value on stories, writing acceptance criteria

Working with the team: talking to the stories, reviewing work in progress, reviewing “completed” work, potentially signing-off or formally accepting stories, participating in 3-Amigos meetings with testers and developers, helping to improve the development processes

UXD: working even more closely with an UXD specialists because the two roles overlap, and possibly substituting for UXD specialists where they are absent.

Meetings: prioritisation pre-planning meeting, planning meeting themselves, stand-up meetings, retrospectives, show & tell demonstrations (potentially delivering them the show & tell themselves)

Interfacing to the wider organization: reporting and listening to internal stakeholders in authority, attending Governance and/or Portfolio review meetings, aligning product strategy and plans with company strategy and plans, plus feeding back to company strategy about their own product strategy and plans.

Planning: participating in Sprint planning with the team, planning for upcoming iterations (the rolling quarter plan as I like to call it), longer term planning which might take the form of a roadmap, a capacity plan, a scenario plan or all three

Customers 1: identifying customers and potential customer, segmenting the customer base, creating customer profiles and personas.

Customers 2: visiting customers, observing customers, talking to customers about stories and potential future work, reflecting on customer comments and feeding back to the team and other stakeholders.

Customers 3: similar activities to #2 for people and organizations who are not currently customer but who are potential customers (because potential customers who have unmet needs represent growth.)

I’m sure some of you are saying: “But we don’t have external customers, we have internal (captive) users”. And your right, if you have such “customers” then you have a subset of these activities. But then again, shouldn’t you be thinking about how our product is used by internal users to service the needs of external customers? And how you could improve that experience (for the customers) and improve the process (for the users?)

Marketing: inbound marketing the items just mentioned under customers plus market scanning (checking out the competitors) and potentially outbound marketing (advertising, PR, trade shows, etc.)

Sharing expert knowledge: providing knowledge about the domain and subject of development to the development team, supporting sales calls, demonstrating the product at shows. (And when the company is small helping the training and support teams.)

The offering: using the information gained in all these activities to refine the product/service offering to satisfy customers or improve business processes; Is it the right offering? Are you targeting the right customer segment? Should you be offering something else?

Close the loop: evaluating the effect on customers and/or process: Are the features bing used? Are non-feature improvements making a difference? What shouldn’t have been done? What arises form the changes that have been made? More software changes? Process changes?

Money: is all this making money? if the continued existence of the team positive to ROI?

Coincidentally, while I was preparing this blog Marty Cagan published a blog entitled “CEO of the Product Revisited” in which he discussed offered a list of all the discussions a Product Manager can expect to be involved with. That is no short list either. And as anyone who follows my writing already knows I see the Product Owner role as a kind-of Product Manager – more on that in a future blog.

This is not to say that all Product Owners should be doing all of these things. Asking one person to take all this on is probably setting them up to fail. Every product owner should recognise every item on this list. If they aren’t doing any of these items themselves then I expect they can either cross it off (doesn’t need doing where they work), or name the person who is doing it.

And I also expect every product owner can add some things to this list which I have overlooked.

In future blog posts I intend to discuss (again) the Product Owner as a Product Manager and how Product Owners can reduce their work load.

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Sprint Zero

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Starting a new project? A new product? Have a team transitioning to “Agile” (aka Scrum) and wanting to get ready?

Why not try a Sprint Zero? – Why not indeed.

Sprint Zero is a way to get everything ready to start work, to buy the tools you need, set up your databases, perhaps do some architecture review, tell the rest of the organization about what you are doing, maybe write some requirements, get some additional training, etc. etc.

Sprint Zero doesn’t need to happen in a time box, you can take all the time you need, after all, you haven’t become “agile” yet, Sprint Zero will make you Agile.

Good idea?

Please don’t.

Sprint Zero is a get-out-of-jail-free card that pushes really change down the road a little bit further.

The truth is: there is work to do.

Just start “sprinting”. Schedule an iteration to start tomorrow and get going. Do what you plan for the next week, then review and plan again.

Doing anything else delays the change and potentially reduces motivation.

If you want to call your first iteration Zero then fine; you can call it Zero, or One, or Two, or 57 or even -1 if you want, I don’t care. Give it a number and make sure the next iteration adds one to that number.

All those things I just listed – and more – that you need to do to “get started” are themselves work that needs doing. Why not write them on a card and schedule them like you would any other piece of work in an iteration?

If you haven’t worked out what those things are then No problem, try and schedule some real work. When you find you don’t have something (a database instance for example) simply write out a card, schedule it, do it. The things you need to do to “get ready” are actually things you need to do to build something you do want.

My big worry with Sprint Zero (apart from it injecting unnecessary delay and loss of change momentum) is that it makes work. You may think you need to do something in advance and you might be right. But, then again, you might be wrong. You might do something – like install a database – when you could quite easily postpone that work for a few weeks or even skip it entirely.

Similarly some say “How can we start work until we know what we need to do? – we need to gather requirements, write some stories….”

But again: determining what needs doing, requirements gathering, is itself work to be done.

If it is not clear what needs to be done on day-1 then the first thing to do is to do some work to find out what needs to be done. The initial work may well be requirements discover and story writing with only a little bit of product building.

Speculative, early, requirements gathering, may actually increase the amount of work to do because you capture all the things people think you need to do. Once you start to put the product together you may not need those things but once they are listed as part of the work to do – and especially if they have been promised to someone – then not doing them can be problematic.

Anyway, having written this blog I now wonder if I should have bothered. Now I look about a lot of other people have made similar points already. As I have been writing this post I thought I should find a definition of Sprint Zero, so I did a search. Interestingly, most of the articles that came up on page 1 of Google are also critical of Sprint Zero for similar reasons to myself. So maybe Sprint Zero is an idea whose time has already come and gone.

When does a Start-Up need Agile?

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I started writing another piece on more economic and agile/software development but it got to long, so right now, an aside…

Back in 1968 Peter Drucker wrote:

“Large organizations cannot be versatile. A large organization is effective through its mass rather than through its agility.”

Last week I presented “Agile for Start-ups” here in London for the third time. Each time I’ve given this talk it has been largely rewritten – this time I think I’ve got it nailed. Part of the problem is I tend towards the view that “Start-Ups don’t need Agile”, or rather they do, but agile comes naturally and if it doesn’t then the start-up is finished. Its later when the company grows a bit that it needs Agile. And notice here, I’m differentiating between agile – the state of agility – and Agile as a recognised method.

New, energetic, start-ups are naturally agile, they don’t need an Agile method. As they grow there may well come a time when an Agile method, specific Agile tools, are useful in helping the start-up keep its agility. Am I splitting hairs?

For a small start-up agile should be a natural advantage. On day one, when there are two people in a room making the startup it isn’t a question of what process they are going to follow. At the very beginning a start-up lives or dies by two things: passion and a great idea. In the beginning it should be pure energy.

In many ways the ideas behind Agile are an effort to help companies maintain this natural agility as they grow. Big, established, companies who have lost any natural agility seem to resemble middle aged men trying to recapture a lost youth.

So when does a start-up need to get Agile? – a more formal way of keeping fit as it where.

Not all day-1 start-ups are pure passion, ideas and energy. Some need to find their thing. They need an approach to finding their reason for being. Agile can provide that structure.

And start-ups which are taking a Lean Start-Up approach also need a method. They may have passion and energy in the room but the lean startup market test driven approach demands discipline and iteration. Lean Start-Up demands you kill your children if nobody wants them.

When I look at Lean Start-Up I see an engineer’s solution to the problem of “What product should our company build to be successful?” The engineered solution is to try something, see what happens, learn from the result, maybe build on the try or perhaps change (pivot) and repeat.

In both these cases a start-up needs to be able to Iterate: Try something, see what happens, learn from it and go round the loop again.

You can generalise these two cases to one: Product Discovery through repeated experimentation.

That requires a discipline and it requires a method – even if the method is informal and subject to frequent change. It can be supplemented with traditional research and innovation approaches.

The next time a start-up can benefit from Agile (as in a method) is as it grows: as it becomes a “scale-up” rather than a start-up. This might be when you grow from two to three, or from 10 to 13, or even 100 to 130 but at some point the sheer energy driven nature of a start-up needs to give way to more structure.

This probably coincides with success – the company has grown and survived long enough to grow. Someone, be they customers or investors, is paying the company money. It is no longer enough to rely on chance.

The problem now is that introducing a more defined method risks damaging the culture and way the start-up is working – which is successful right now. So now the risk of change is very real, there is something to loose!

Just as the company can think about the future it needs to risk that future. But no change is also risky, with growth the processes and practices which brought initial success may not be sustainable in a larger setting.

This is the point where I’ve seen many companies go wrong. They go wrong because they decide to become a “proper company” and do things properly. Which probably means adding some project managers and trying to be like so many other companies. They give up their natural agility.

Innovation in process goes out the window and attempts to turn innovative work into planned projects are doomed. Show me the project plan with a date for “Innovation happens here” or “Joe gets great idea in morning shower” or “Sam bumps into really big contact.”

It is at this point that I think Agile methods really can help. But those approaches need to be introduced carefully working with the grain of the organization. Some eggs are bound to be broken but this shouldn’t be a scorched earth policy.

Start-ups and scale-ups need to approach their products and Agile introduction as they do their business growth: organically. Grow it carefully, don’t force feed it, don’t impose it – inspire the staff to change and let them take the initiative.

It is much easier to do this while the team is small. Changing the way one team of five works is far easier than changing the way four teams of eight work. Its also cheaper because once one team is working well it can grown and split – amoeba like – and later teams will be born with good habits.

Unfortunately companies, especially smaller ones, put a lot of faith in hiring more people to increase their output and thereby postpone the day when the team adopt a more productive and predictable style of working.

This might be because they believe new hires will have the same work ethic and productivity as the early hires: they probably won’t if only because they have more to learn (people, code, processes, domain) when they start.

Or it might be because the firm doesn’t want to loose productivity while they change: in my experience, when the change is done right short term productivity doesn’t fall much and quickly starts growing.

It might just be money saving: why pay for training and advice today? – yet such advice isn’t expensive in the scheme of things, certainly delaying a new hire by a couple of months should cover it.

Or it might just be the old “We haven’t got time to change” problem. Which always reminds me of a joke Nancy Van Schooenderwoert once told me:

“A police officer sees a boy with a bicycle walking along the road at 10am.
Police: Excuse me young sir, shouldn’t you be in school?
Boy: Yes officer, I’m rushing there right now.
Police: Wouldn’t it be faster to ride your bike down the hill?
Boy: Yes officer, but I don’t have the time to get on the bike.”

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Kanban paradox

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For a while now I’ve been seeing a paradox with Kanban. Specifically, Kanban compared to Scrum.

For a team new to Agile – although some regard this as heretical I place Kanban under the Agile umbrella, yes I know its more about Lean than Agile but of cause Agile is itself a Lean method, anyway…

For team, specifically a software team, looking to adopt a new process there is a choice:

  • Kanban has a very low barrier to entry, to get started Kanban essentially says “visualise your work and manage the result.” Starting Kanban can be as simple as putting up a board and tracking work items. In Kanban visualisation should drive improvement. Change can be incremental and gradual. Change is rooted in learning.
  • Scrum has a far higher barrier to entry: essentially Scrum says, “Adopt Sprints, designate a Product Owner, appoint a Scrum Master and build out a backlog.” Once these changes are done you can run with Scrum and then the Scrum Master and retrospectives will kick-in and drive further improvement.

Interestingly, neither method says explicitly “Improve your quality.” Yet I always believe a lot of the success of Agile methods is down to good old quality improvement: writing fewer bugs and having fewer bugs to fix means greater predictability and more time to deliver valuable software. But I digress.

It is easier to start with Kanban because it requires less up front change. However that does mean the improvements are slower coming.

Conversely, Scrum drops in, changes a lot and most teams see an immediate improvement. Scrum relies less on subsequent change.

Because Kanban relies more on ongoing change it is more difficult. It is easy to get stuck at the “we built a Kanban board so we are doing Kanban stage.” Change in Kanban requires one to see the need to change, understand what will fix a problem and then follow the change through. That often requires experience. Thus in teams adopting Kanban there is a greater need for a coach, a consultant, someone who has done it before.

Scrum on the other hand makes far more changes upfront and the recipe for improvement is more straight forward. And of cause there are a lot more books on Scrum, blogs on Scrum, Certified Scrum Masters and Scrum experience out there. So while it is harder to get started with Scrum (because more needs to change) there is less need for further change and you change does not require the same level of knowledge.

You see this specifically when you look at statistics. Watching the numbers should be important in both processes but with Kanban it is near essential. Anyone with real understanding of Kanban knows that queuing theory, lead times, possibly weighted lead times, and a bunch of other numbers need to be examined.

Scrum on the other hand doesn’t go much further than a burn-down chart. Yes, making more improvement with Scrum will also benefit from understanding lead times, queuing theory and the rest but you can quite happily use Scrum, and even improve Scrum, a fair bit without understanding these ideas.

So here is the paradox:

It is easier to start with Kanban than it is Scrum without expert knowledge, but it is harder to improve Kanban than Scrum without expert knowledge.

In many ways I prefer Kanban but I find this need for expert knowledge troubling. I suppose I shouldn’t, I’m a consultant, I am that expert, people hire me to help improve their Kanban processes so it does make more work for me.

In the longer run, the Kanban approach is more likely to lead to a genuine all inclusive culture of improvement and is less likely to get stuck in a sub-optimal position – yes Scrum fixes things, but is it the best fix possible?

Looked at like this gives me a new perspective on Xanpan.

I wanted Xanpan to be two things:

  • An understandable description of actually following an Agile process, specifically a Kanban/XP hybrid processes
  • An example of how, and why, teams should create their own processes.

The same paradox is here: Xanpan should be easy to start but allow you to improve; creating your own process requires a bit more knowledge that only really comes with experience.

To step back a minute and ask another question: What amount of change can a team handle to start with?

I find that I advocate more initial change than I used to. Because I’m fearful of creating a learned dependency I really want teams to learn to change and improve themselves. But… once a team has decided to change I want to seize the opportunity and install a bunch of changes while enthusiasm is there.

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Utilisation and non-core team members

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“But we have specialists outside the team, we have to beg… borrow… steal people… we can never get them when we want them, we have to wait, we can’t get them enough.”

It doesn’t matter how much I pontificate about dedicated, stable, consistent teams I hear this again and again. Does nobody listen to me? Does nobody read Xanpan or Continuous Digital?

And I wonder: how much management time is spent arguing over who (which individuals) is doing what this week?

Isn’t that kind of piecemeal resourcing micro-management?

Or is it just making work for “managers” to do?

Is there no better use of management time than arguing about who is doing what? How can the individuals concerned “step up to the plate” and take responsibility if they are pulled this way and that? How can they really “buy in” to work when they don’t know what they doing next week?

Still, there is another answer to the problem: “How do you manage staffing when people need to work on multiple work streams at once?”

Usually this is because some individuals have specialist skills or because a team cannot justify having a full time, 100%, dedicated specialist.

Before I give you the answer lets remind ourselves why the traditional solution can make things worse:

  • When a resource (people or anything else) is scarce queues are used to allocate the scarce resources and queues create delays
  • Queues create delays in getting the work done – and are bad for morale
  • Queues are an alternative cost: in this case the price comes from the cost-of-delay
  • Queues disrupt schedules and predictability
  • In the delay people try to do something useful but the useful thing is lower value, and may cause more work for others, it can even create conflict when the specialist becomes available

The solution, and it is a generic solution that can be applied whenever some scarce resource (people, beds, runways):

Have more of the scarce resource than is necessary.

So that sounds obvious I guess?

What you want is for there be enough of the scarce resource so that the queues do not form. As an opening gambit have 25% resource more than you expect to need, do not plan to use the scarce resource more than 75% of the available time.

Suppose for example you have several teams, each of who needs a UX designer 1-day a week. At first sight one designer could service five teams but if you did that there would still be queues.

Why?

Because of variability.

Some weeks Team-1 would need a day-and-a-half of the designer, sure some week they would need the designer less than a day but any variability would cause a ripple – or “bullwhip effect”. The disruption caused by variation would ripple out over time.

You need to balance several factors here:

  • Utilisation
  • Variability
  • Wait time
  • Predictability

Even if demand and capacity are perfectly matched then variability will increase wait time which will in turn reduce predictability. If variability and utilisation are high then there will be queues and predicability will be low.

  • If you want 100% utilisation then you have to accept queues (delay) and a loss of predicability: ever landed at Heathrow airport? The airport runs at over 96% utilisation, there isn’t capacity to absorb variability so queues form in the air.
  • If you want to minimise wait time with highly variable work you need low utilisation: why do fire departments have all those fire engines and fire fighters sitting around doing nothing most days?

Running a business, especially a service business, at 100% utilisation is crazy – unless your customers are happy with delays and no predicability.

One of the reasons budget airlines always use the same type of plane and one class of seating is to limit variation. Only as they have gained experience have they added extras.

Anyone who tries to run software development at 100% is going to suffer late delivery and will fail to meet end dates.

How do I know this?

Because I know a little about queuing theory. Queuing theory is a theory in the same way that Einstein’s Theory of Relativity is theory, i.e. it is not a theory, its proven. In both cases mathematics. If you want to know more I recommend Factory Physics.

So, what utilisation can you have?

Well, that is a complicated question. There are a number of parameters to consider and the maths is very complicated. So complicated in fact that mathematicians usually turn to simulation. You don’t need to run a simulation because you can observe the real world, you can experiment for yourself. (Kanban methods allow you to see the queues forming.)

That said: 76% (max).

Or rather: in the simplest queuing theory models queues start to form (and predictability suffers) once utilisation is above 76%.

Given that your environment is probably a lot more complicated then the simplest models you probably need to keep utilisation well below 76% if you want short lead times and predictability.

As a very broad rule of thumb: if you are sharing someone don’t commit more then three-quarters of their time.

And given that, a dedicated specialist doesn’t look so expensive after all. Back to where I came in.

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