Product Owners need 4 things


To be an effective Product Owner – and that includes product managers and business analysts who are nominating work for teams to do – you need at least four things. You may well need more than these four but these are common across all teams and domains.

  1. 1. Skills and experience

There is more to being a Product Owner than simply writing user stories and prioritising a backlog. Yes, you need to know how to work with a development team and how to work in an Agile-style process. Yes you need to be able to write user stories and acceptance criteria, perhaps BDD style cucumbers too; yes you need to be able to manage a backlog and prioritises and partake in planning meetings.

But how do you know what should be a priority?
How do you know what will deliver value? And please customers? Satisfy stakeholders?

Importantly Product Owners need to be able to do the work behind the backlog.

Product Owners need to meet people, have the conversations, do the analysis and thinking behind those things. Any idiot can pick random items form a backlog but it takes skills and experience to maximise value.

Product Owners need to be able to identify users, segment customers, interview people, understand their needs and jobs to be done. They need to know when to run experiments and when to turn to research journals and market studies. And that might mean they need data analysis skills too.

If the product is going to sell as a commercial product you will need wider product management skills. While if your product is for internal use you need more business analysis skills. And product managers will benefit from knowing about business analysts and business analysts will benefit from knowing about product management.

You may also need specialist domain knowledge – you might need to be a subject matter expert in your own right, or you might become an SME in given time.

Some understanding of business strategy, finance, marketing, process analysis and design, user experience design and more.

Don’t underestimate the skills and experience you need to be an effective Product Owner.

  1. 2. Authority

At the very least a Product Owner needs the authority to nominate the work the team are going to do for the next two weeks. They need the authority to choose items form a backlog and ask the team to do them. They need the authority not to have their decisions overridden on a regular basis. (OK, it happens occasionally.)

As a general rule the more authority the Product Owner has the more effective they are going to be in their role.

The organization may confer that authority but the team need to recognise and accept it too.

I’ve seen many Product Owners who while they have the authority to nominate work for a team don’t have the authority to throw things out of the backlog. When the only way for a story to leave the backlog is for it to be developed it is very expensive. This leads to constipated backlogs that are stuffed full of worthless rubbish and where one can’t see the wood for the trees.

If the Product Owner doesn’t have sufficient authority then either they need to borrow some or there is going to be trouble.

  1. 3. Legitimacy

Legitimacy is different from authority. Legitimacy is about being seen as the right person, the bonafide person to exercise authority and do the background work to find out what they need to find out in order to make those decisions.

Legitimacy means the Product Owner can go and meet customers if they want. And it means that they will get their expenses paid.

Legitimacy means that nobody else is trying to fill the Product Owner role or undermine them. In particular it means the team respect the Product Owner and trust them to make the right calls. Most of all they accept that once in a while – hopefully not too often – the Product Owner will have to say “I accept technologically X is the right thing but commercially it must be Y; full ahead and damn the torpedoes.”

It can be hard for a Product Owner to fill their role if the team believe a senior developer – or anyone else – should be managing the backlog and prioritising work to do.

  1. 4. Time

Finally, and probably the most difficult… Product Owners need time to do their work.

They need time to meet customers and reflect on those encounters.

They need time to work-the-backlog, value stories, weed out expired or valueless stories, think about the product vision, talk to stakeholders and more senior people, and then ponder what happens next.

Time to evaluate what has been delivered and see if it is delivering the expected value. Time to understand whether that which has been delivered is generating more or less value than expected. Time to feedback those findings into future work: to recalibrate expected values and priorities, generate more work or invalidate other work.

Product Owners need time to look at competitor products and consider alternatives – if only to steal ideas!

They need time to work with the technical team: have conversations about stories, expand on acceptance criteria, review work in progress, perhaps test completed features and socialise with the team.

They also need time to enhance their own skills and learn more about the domain.

And if they don’t have the time to do this?

Without time they will rush into planning meetings and say “I’ve been so busy, I haven’t looked at the backlog this week, just bear with me while I choose some stories…”

More often than not they will wing-it, they substitute opinion and guesswork instead of solid analysis, facts and data. They overlook competition and fail to listen to the team and other managers.

And O yes, they need time for their own lives and family.

I sometimes think that only Super Human’s need apply for a Product Owner role, or perhaps many Product Owners are set up to fail from day-1. Yet the role is so important.

I plan to explore this topic some more in the next few posts.

Down with management!


Si: Welcome Peter, thank you for taking the time to attend this annual performance review

Peter: As long as you know I don’t want to be here, performance reviews are pointless, I told that to my manager Roger. I should be at my desk coding.

Si: Yes, I understand your position, in fact many of us sympathise with it, including Roger and myself

Peter: Well, good, does he also agree that management is a waste of time? If we were really following Agile the way we should then Roger wouldn’t be getting in the way all the time and I could write more code.

Si: Yes, well, we’ve had that feedback from other people and the company is committed to change. In fact, one of the things I wanted to speak to you about today was to tell you Roger is stepping aside.

Peter: What?

Si: Yes, Roger is undertaking retraining and will become a Product Specialist, he himself rejected the title Product Manager because he doesn’t see the need for Managers and even refused the title Product Owner because he doesn’t feel he can “own” something that is a community effort and is ultimately owned by the company shareholders.

Peter: Wow… he has been listening to me?

Si: Yes, in my long conversations with him he has spoken many times of the points you have been making. Of course as part of the management team it wouldn’t have been right for him to speak about his changing views too publicly lest people wonder what was happening.

One of the things I wanted to discuss with you today was how we go about distributing Roger’s other responsibilities. As one the longest serving employees Roger suggested you would have the best insights.

Peter: Right, erh, so what responsibilities are these? – apart from asking me “how long will it take?”

Si: Well some relate to the product. As Product Specialist he will continue to support sales staff making customer visits, he will continue administer the backlog, prioritise work in the planning meeting and draw-up the product roadmap – although he wants to involve more people and change the concept of a roadmap. He plans to increase the number of customer visits, competitor analysis and market scanning activities he has been doing already.

Peter: Good, we need a proper product owner, he was doing the role anyway but didn’t have enough time.

Si: Well yes, in fact he has also requested that you and the other engineers accompany him on customer visits in future.

Peter: What? – but our customers are everywhere but here!!! Saudi Arabia, Finland, Argentina, USA… it takes days to get to those places, I’m needed here, I need to be coding!

Si: Well I’ll let the two of you come to a decision on that. Right now we need to redistribute Roger’s work.

There are the administration matters, signing off holiday, arranging sabbaticals, maternity/paternity leave; the new monthly check-points replacing annual performance reviews need to be staffed. There is a lot of work around contractors, time-sheets, extensions and terminations, to be honest there are constant political battles over the number of contractors and whether we should be using them at all. Personally, I believe these changes will abolish office politics but some people think I’m being naive.

Anyway, a lot of this work could be for someone like myself in HR…

Peter: But that will slow everything down! HR is never responsive and most of them – unlike yourself – don’t know one end of a code stack from another

Si: Yes, quite right, HR is atrociously understaffed so we take forever to do anything, and many of my colleagues just aren’t close enough to the teams – frankly they don’t understand technology.

For those reasons the company is also closing the HR department. The work will be distributed to the teams. So my colleagues and I are all being made redundant. Some larger teams, such as yours, will be allowed to hire “Talent specialists” to help with recruitment matters and I hope to secure a such a job myself. But that also means members of your team will need to interview and select their own Talent Specialist. I expect you’ll be on the interview panel, I hope you will see the depth of my experience.

Peter: O, but we are all so busy coding, that will slow us down! And we don’t know anything about recruiting HR people. Can’t Roger arrange that before he leaves?

Si: Roger’s re-assignment is immediate, he insisted on it. I suggest you and your colleagues get started on selecting your Talent Specialist as quickly as possible.

Still, many of the things I just listed will not fall under the Talent Specialists remit. A self-managing team really should manage a lot of those things itself. The Talent Specialist will be there for staffing issues, CV filtering, negotiating with recruitment agents, diversity monitoring, visa applications, university recruitment fairs, exit interviews, legal issues, and so on.

Peter: Right, well I’m sure out team can dispense with a lot of the administration, we can trust one another to take holiday responsibly.

Si: Quite so, I’m sure you can organise away a lot of the admin work but there will be a rump.

Peter: Could we bring back the Scrum Masters?

Si: Well the Scrum Masters we had were always at pains to point out they were not managers or administrators – I know they are in some other places. Plus, they said they “did themselves out of a job” in the end and recommended their own removal.

Peter: Arh… I remember, I guess we will share the administration around the team. We can work out a process for that – the same as we do in planning meetings.

Si: Remember as you do that some of your team may need management training

Peter: What?

Si: Management, even administration, has its own on standards, rules, jargon, if members of your team are going to do administration and management work, let alone talk to others doing it then they need to know their CapEx from their OpEx, their 4 Ps from their 5 Forces, front-of-house from back-of-house, what you can and can’t say in an exit interview, a SWOT analysis from a …

Peter: Yes, yes I get it, a lot of mumbo jumbo if you ask me…

Si: All the same, is it fair to throw people in at the deep-end? Shouldn’t we help them learn if they want to?

Moving on next we come to Roger’s role in the hierarchy. Sharing company information down to the team and team information up the chain. Plus annual strategy meetings, quarterly reviews, product portfolio boards, key customer engagement.

Peter: We have Slack and Wiki’s so I’m sure we can manage the information alright.

Si: Arh… well, when we rolled this program out in Norway some of the teams tried just that. In a couple of months most of the team members were complaining about information overload. I believe the quote was “How can you expect me to do any coding when Slack keeps interrupting me?”

Peter: Arhhh.

Si: And the Norwegian executives felt they never got a consistent message from the teams because different people would turn up to quarterly reviews each time while strategy sessions started to resemble a Stackover flow argument. Some customers complained that they no longer had a point of contact or got conflicting messages. And lets not even talk about the auditors.

Peter: Well the thing is, none of us have been trained in Strategy or, what did you call it? Portfolio review?

Si: Good thinking, I guess one of Roger’s strengths was the time he spent studying that on his American MBA. Do you want to arrange a coupe of days training in strategy and portfolio review for your team?

Peter: Erh, I’m not sure I know enough to book a training course, and isn’t that going to take people away from coding?

I mean the whole team, all 10 of us, out for 2 days training? And the people going to attend meetings with other departments and teams?

Si: Yes of course it is, but you will be self-managing. The team won’t have anyone else.

And mentioning the team, there is also another issue you’ve raised yourself in the past. Here we are, two very similar males discussing this. In fact your whole team is quite like the two of us. The team are going to have to challenge themselves to improve their diversity.

Peter: Right…

Look, we’ve been here half-an-hour and haven’t started even reviewing my performance, will this take much longer? I’ve got code to write.

Si: Well, there is quite a bit more of Roger’s work to discuss, plus his boss, Jane, is also stepping aside so we need to reconvene with people from the other teams to allocate her remaining work.

Peter: If we are self-managing, could we decide to employ a specialist to pick up a lot of this work?

Si: I expect you could, you’d need to put some costings together.

Peter: Right, I’ll talk it through with the team and see if we can hire a secretary.

Si: Good idea, although the secretary is going to need more than just typing skills.

Peter: Sure, secretary might be the wrong job title. We need someone with skills who we can trust to make these decisions, someone with experience.

Si: Although, you know, such a person may become a bit of a gate-keeper to the team

Peter: Certainly, thats what we need, we don’t want interruptions!

Si: Well, the problem with a gate keeper is that they decide what goes through the gate and what does not. That gives them a degree of power.

And if someone is picking up all the left over work, making decisions for the team, and controlling access to the team, and information flows into and out of the team…

Peter: Yes, is there a problem? That all sounds good to me, this gate keeper will have the power to defend the team

Si: Well… what I was about to say is such a person might be seen by some as having authority… and how will the team members feel about someone else deciding what is told to others?

Peter: Well, we could review their decisions and tell them what to do

Si: Yes… well, that might be seen as a lacking trust or even micro-manegement…

Outsourcing banana skins: Warning signs that your supplier isn’t as good as they claim


So you think you want to contract out some development work? – yes, you know this area is full of banana skins to slip on, and you know others have problems but you still want to do it.

And you want to contract it out to an “Agile” development shop?

There are no laws against opening a development shop – a digital agency, an outsourcer, a consultancy, call it what you like. That is the beauty of capitalism, it allows pluralism. The hard part is choosing one that is competent, some outsourcers are pretty awful.

Everyone who can spell “Agile” can claim to be Agile, and most hire a copywriter to give them an online spin so they all end up making the same claims.

Those who are half good can coach their staff in what to say. And in truth, most of them genuinely want to do the best possible job for you – and be agile too. But how can you really tell?

Well… when I’m being cynical I think you can’t. The only way to really tell is to give them some work and see what happens. Of course once you’ve engaged someone you need to be both legally and mentally prepared to walk away.

So to help you here are some warning signs that you have stepped on a banana skin and your supplier isn’t as good as you – and they – want to be. You might even say these are warning signs that they aren’t really Agile.

1) Customer involvement

They don’t want customer involvement. They don’t want your people on site. They claim that your people get in the way. They want to be left alone to do things.

Obviously I’m thinking primarily of actual Customers, Users, Product Owners, Business Analysts and so on. These people should be working closely with the suppliers people. They should have direct contact, they should be discussing stories.

If your supplier isn’t embracing your people and treating them as their own team members something is probably wrong.

The supplier should be challenging your people – after all the suppliers are the experts, if they are simply accepting everything you ask for then something is wrong.

The same is true of other people you might want involved: a consultant or Agile coach should be welcome too. And if you decide to ask a third party to inspect their development then they should be open to this too.

Naturally they should also be open about the code too. After all the code will be yours one day.

2) Regular demonstrations

The supplier should be providing regular demonstrations – “show and tell” – as a very minimum. Every couple of weeks I expect the supplier to show the latest work. You – and your people – should be able to see working software offering more than the previous demo.

If the supplier is NOT providing regular demonstrations then you should be worried. Likewise, if the demonstrations don’t show progress get worried. Most of all, if the supplier doesn’t want to talk about why demonstrations aren’t happening, or how they can show progress then something is wrong.

3) Release, release, release

Show and tell demonstrations are good but the real test is to release to live. Releasing all the way to your live system. You might hide it on an obscure URL that nobody knows, or call it a beta release or something, I don’t care, the closer they can get to real live the better.

You supplier should be releasing to a live environment – or an exact copy – very very regularly.

The longer the supplier goes without an actual release the more nervous you should be. Sure, once in a while things go wrong and nobody is perfect. They may go two weeks with nothing to show for it. I don’t like it – and neither should you – but an occasional gap is OK.

Going four weeks without a release… I suppose it might happen in the early stages of the work. But it is in the early stages that you most need reassurance that they can deliver something – anything!

Six weeks with no release… well here we are into “3 Strikes and you are out.” Sure they will be able to give technical reasons why things messed up three times in a row. But take it from me, something is wrong.

The longer they go without an actual release to more concerned you should be and the more you should offer to work with them to address the issue.

Eight weeks? – eject, eject, eject.

4) Show me the tests

Maybe this should be warning sign #1 but for this you need someone technical, someone who knows what a test looks like. In the show-and-tells your supplier should be able to show you automated tests executing. Not very exciting perhaps and certainly not meaningful to the business but if they can’t show you then how do you know they even exist?

And if your supplier doesn’t have an automated test suite then it is certainly time to get out.

Ultimately the system they are building is yours. Your people will need to maintain it, or you will need to pay someone else to maintain it. Without automated tests that is going to be hard. Skipping tests now might make it look like you are saving money but you are not, even in the short term the lack of tests will bite you hard, it will push up costs and destroy schedules.

5) “Feature complete”

The phrase alone should be a warning sign. Equally the words “75% feature complete” (or any other percentage) is a big red flag.

If the supplier doesn’t have a test suite, if they can’t show working (preferably releasable) code then its probable they are feature stuffing. They will say they are making progress because “60% of features are done”. They may even start to claim they are feature complete but remember: a feature without a test isn’t done.

A feature without a test is pure risk. At any time a defect can put a hand up and say “Fix me!”

An automated test isn’t a guarantee of bug free code but without automated tests then I guarantee you have defects waiting to appear.

If you are in a relationship exhibiting any of these five sign then it is certainly time to talk. It may be time to end. But how do you avoid getting into that position?

Let me be as clear as I can: both you and your supplier should prioritise working, usable, functionality over more functionality. As the old saying goes “A bird in the hand is worth two in the bush”, working, deliverable (even better released) features are the priority, there should be less work in progress, fewer incomplete features, fewer “almost done” and as few as possible defects.

While cynical me thinks you might not be able to avoid it that doesn’t mean you shouldn’t try, so here are four warning signs that you are about to step on a banana skin:

1) “Agile is not that different”

Don’t let them tell you Agile isn’t different. In many ways it isn’t but if a supplier is trying to persuade you that you don’t need to change the way you work then it is a sign that either a) they don’t appreciate the magnitude of the change or b) they will tell you anything to get the contract signed.

Since you want a supplier who will challenge you it might not be a good idea to hire one who doesn’t like challenging you or doesn’t prepare you for difficulties.

2) “We are certified”

An extension of warning sign #1 is that the supplier is proud of how certified they are: ISO-9001, PMI, PRINCE-2, CMMI – some in the Agile world would regard these certifications as warning signs in their own right.

Scrum Master Certified, Agile Project Manager Certified, Scrum Product Owner Certified: these are slightly better but anyone who can’t tell you the flaws in all these certifications has myopia.

Question any organization that offers up badges rather than working products.

(Disclosure: for better or worse I hold a couple of Kanban certifications, while I think Lean Kanban University have done a better job than many in making their certifications meaningful I don’t think they are a panacea either. Anyway, Kanban certifications aren’t as recognised as those I just mentioned.)

3) Fixed or long term contract

IT suppliers have a long history of locking clients into “fixed contracts” – fixed scope, cost and time. These contracts are utterly flawed. Anyone claiming they can fix everything is a charlatan. Give them a copy of “Dear Customer: The Truth about IT” (the Xanpan prologue) and say goodbye.

Similarly locking yourself into a long term contract with a supplier before you have done some work with them is a bad sign. Do a small piece of work, for a small fee, with your potential supplier and see if they are as good as they say.

In my experience the best – most “agile” – digital suppliers can pick and choose their customers. If your supplier needs you more than you need them then it is a bad sign.

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Minimally Viable Team in a nutshell


Last week I was in Holland helping a client with their agile adoption and digital transformation. When the subject of teams came up I started talking about Minimally Viable Teams. Yesterday I found myself writing an e-mail to the client expanding on the idea. And it seemed to me that the e-mail – or an edited version – was worth sharing here…

The idea of an Minimally Viable Team (MVT) is based on the observation that if a team is overstaffed then team members will find work for themselves – Parkinson’s Law.

Mix in Conway’s Law: the recognised phenomenon where team copy the organization structure they are in. So for example, if you have a database expert on the team the final design will use a database whether one is needed or not.

If one is aiming for a self-organizing, goal-directed, value-seeking team then making any decisions about the team, the software design, or even the problem before work begins is questionable. The more decisions that are made the more the team is constrained, the more the team is constrained the less it is master of its own destiny.

Further, those decisions made before work begins: one expects them to be rational, which means some pre-work is needed to understand what decisions are needed and make the decisions. That pre-work costs time and money. The more money that is committed then the more difficult (i.e. more career threatening) it becomes to reverse those decisions if things go wrong.

Some companies spend an awfully long time thinking and planning to do something: longer than it takes to actually do the thing. I once visited a company which had spent five years planning a particular project and not building anything.

Add two more things to this.

We know from experience that planning has rapidly diminishing returns. A little bit of planning creates great learning, but after a little while the rate of learning drops off. Very soon learning by doing becomes more effective, i.e. switch from thinking about what might be done to trying to do it.

This has never been truer than today – 2018: with the computing power and tools it is faster and cheaper than ever to build a prototype, a concept, an MVP, version 1, alpha version or whatever else you want to call it.

However, going to the other extreme and doing no pre-work doesn’t make a lot of sense either.

Enter the Minimally Viable Team: the team jumps to doing, all that pre-work is given to the team. They get to decide what is needed.

To traditionalists the team/project/product is launched prematurely but actually all we are doing is extending the start date backwards so that the pre-work is now part of the thing. By tasking the initial team with all the traditional pre-work the team becomes master of their own destiny again. And they can choose to approach the mission with a traditional approach (market research, architecture design, resource planning) or in an agile/digital fashion (build a small product and test it) – that is their choice.

The MVT idea is to “starve” the team and make them pull only the necessary resources as and when they need them. When organizations decide who (which roles) will be on the team in advance they are in effect designing the software.

And since agile approaches and modern tools allow us to make progress that much faster why not move more quickly to a working product? Minimise the design, postpone the architecture.

This approach also means the initial team can be kept small which means they are cheap. So if they conclude the project shouldn’t be done the organizational inertial is less and the project can be cancelled. Which hopefully means the organization will take more chances on more ideas.

Try this thought experiment.

On Day-Zero there is nothing.

Someone decided there should be Product X. How did this happen? They may have had the idea days ago and have spent the intervening time researching the market, the competition, the problem and so on. (During which time their normal job has been disrupted, the sooner they can dedicate themselves to the new idea the faster things will happen.)

On Day-Zero they talk to an architect who considers a design.

This takes a few days at the end of which there is an outline design and the architect suggests the team needs four programmers, two testers, a UXD and a DBA. Plus a project manager and product owner. 10 in all.

It now takes time to make the business case and gather those resources.

At that point work can officially begin, call that D-Day.

Then the team need to learn to work together, build something and launch it into a market.
They also need to understand what the architect had in mind.

Officially the project began on D-Day, or perhaps the day the business case was approved.

How much time has been spent so far? Without testing the market? Allowing competitors to do something? – all this time cost of delay has been at work changing the business case.

How has all that “getting ready” time been accounted for? How has this work been managed?

The MVT approach says: Time is of the essence the team should decide all these things.

So, as quickly as you can, spend a little venture money on an MVT.

That team has to investigate the market, competition, problem, etc. The team can think about architecture but their primary aim is to build something, and MVP, a prototype, a proof of concept, whatever – build it, show it to customers, launch it, put it in the market.

By keeping it small the team can quickly invalidate ideas which don’t work. Ideas that do work can be built on. They are free to learn.

The initial MVT will do all the same things that a “pre project” phase would do but in a much more agile/digital way. The MVT also allows for continuity, when the team find success the team that can be expanded and grown – applying Gall’s Law.

This also looks a lot more like a start-up than a normal corporate project.

If the idea of a Minimally Viable Team is new to you then check out the discussion in Continuous Digital or some of my earlier blog posts on MVTs.

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Nature abhors an information void


No. 6: What do you want?
Voice: Information
No. 6:You won’t get it
Voice: By hook or by crook we will

Information… we all want information… Facebook updates, Tweets, 24-hour rolling news, the Donald Trump Big Brother House… the opening scenes and words of The Prisoner continue to echo, Patrick McGoohan and the other writers got it right, they were just 50 years early.

Human beings have insatiable thirst for information – even when we know rationally that information is useless is pointless we still want it. We persuade ourselves that something might be happening that we need to know about.

Just today I was driving when my mobile phone started to ring. It was highly unlikely to be anything but still my mind started to think of important things it could be. I had to stop the car and try to answer it. Of course, it was spam, a junk call, caller-ID told me that so I didn’t answer.

Every one of us has information weaknesses. In part it is dopamine addiction. We may look down on those who watch “vanity metrics” but we all information fetishes whether they be, metrics, scores, “facts” or celebrity gossip.

Whether e-mail, Twitter, Facebook, WhatsApp, SMS, Slack, some other medium or social media we all need information and a dopamine fi. Has only replied to my tweet? Has anyone retweeted my last tweet? Has anyone followed me today?

Sometimes it is impossible to believe that nobody has retweeted my fantastic tweet, or that a potential client hasn’t immediately replied to an e-mail, or that… I’ve even on occasions found myself picking up my phone and going to the mail app when I’ve only just walked away from answering e-mail on my PC – as if the e-mail on my phone is better than the e-mail on my PC!

The only thing worse than having a mailbox full of unanswered e-mails is an empty mailbox – mailbox zero – which stays empty.

Sometimes one demands information when there just isn’t any. I think that is what number 6 really meant when number 2 repeatedly asked him for information: there wasn’t anything more than he had said. He had given his information, if others demanded more then it was simply because they couldn’t accept what they had been told.

I’m sure all parents have experienced children in the back of the car who ask: “Are we there yet?”. To which you reply “No – it will be at least an hour”. And then, five minutes later you hear “Are we there yet?”

And who hasn’t felt the same way about project managers? Or technical leads? Or product managers? product owners? business analysts?

Children don’t stop asking because… well, maybe because they don’t understand the answer, they have a poor concept of time. Or maybe because they really want the answer to be “Yes we are there.” As small people children also want information.

Isn’t that the same when other people ask you the “Have you finished foo yet?” and even “When will it be ready?” While one hopes they have a better concept of time they don’t necessarily take in the answer, and they hope and hope and hope that the answer will soon be the answer they want it to be. People are very bad at handling information voids.

Manager types might dress the question up in terms of “The business needs to know” how often does that disguises the real truth: somebody didn’t like the last answer and is hoping that if the question is posed again the answer might be the one they want.

The project manager who checks in every few hours is no different than the developer who leaves their e-mail open on a second screen, or the tester keeps Twitter in the background. Each of them wants to know information!

Our difficult in dealing with information voids means we constantly search for information. And if we can’t find it we create pseudo-information: time based project plans which purport to show when something will be done or system architecture documents which claim to show how everything will work. Are the project managers and architects who create these documents are just seeking information? Dopamine?

Long time readers may remember my review of time-estimation research. Some of the research I read showed that people in positions of authority, or who claimed expert knowledge, underestimates how long work will take more than the people who do the work. Researchers were not clear as to whether this effect was because those in authority and experts let their desire for the end state influence their time estimation or whether it was because these people lacked an understanding of the work in detail and so ignored complications.

And it is not just time based information. Requirements documents are often an attempt to discern how a system may be used in future. System architecture designs are an attempt to second guess how the future may unfold. Unfortunately, as Peter Drucker said “We have no facts about the future”.

Faced with an information void we fill it with conjecture.

Sadly I also see occasions where the search for answers disables people. Sometimes people search for information and answers which are within their own power to give. Consider the product owner inundated with work requests for their product. They search for someone to tell them what they should do and what they should not do. Faced with an information void they look for answer from others. But sometimes – often? always? – the answer is within: as product owner they have the authority to decide what comes first and what is left undone.

I have become convinced over the years that often people ask for information that simply doesn’t exist. When the information isn’t presented they fill in the blanks themselves, they assume the information does exist and isn’t being shared. In some cases they create conspiracy theories or they accuse others of being secretive. But because of doubt they they don’t act on the information.

It is easy to think of examples in the public eye but I think it also happens inside organizations. Often times managers really don’t know what the future will hold but if they don’t tell people then they are seen as hiding something. If they deny information exists they may be seen as stupid or misleading.

The same happens the other way around, the self same managers – who really don’t know as much as people think they do – ask programmers, testers, analysts, etc. for information which doesn’t exist and which maybe unknowable. Telling your manager “you don’t know” might not be something you feel safe doing, and if you do then they may go and ask someone else.

In almost every organization I visit people tell me “We are not very good at communicating around here.” Again and again people tell me they are not told information they “should” be told. I’ve never visited an organization where people tell me “Communication is great around here” and while I’ve visited places where people say “We have lots of pointless meetings” nobody tells me “We are told too much.”

My working assumption in these cases is simply: The information doesn’t exist.

This is Occam’s razor logic, it is conspiracy free, it doesn’t assume the worst of people. I don’t assume people are keeping information secret – either deliberately or through naive understandings of what other people want.

So, the real answer for No. 6 should be “I’ve told you the truth, maybe you can’t accept it.”

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Conclusion: Who works on what – Comparative advantage part 3 of 3


In my last two posts – Who should work on what? part1 and part 2 – I’ve tried to apply the comparative advantage model from economics to the question of which software developer should work on what. The model has come up with two different answers:

  • If productivity (measured by quantity of features is the goal) then it probably makes sense for everyone to work on the product that they are comparatively most productive on (comparatively being the key word here.)
  • If value produced in the goal then it may well make sense for everyone to work on the most valuable features (or product) regardless of personal strengths.

Along the way I’ve highlighted a number of difficulties in applying this model:

  • If common resources are being used, or if doing one piece of work impacts another, then the model doesn’t work.
  • There is no consideration of time or urgency in the model. When urgency enters the picture then productivity may well suffer.
  • Over time things may change: backlogs will stratify and people will learn.
  • Operating this model in practice requires data which is usually unavailable and so getting the data would itself take time.

At this point it is tempting to throw ones hands up in the air and say: “We’ve learned nothing!”

But I don’t think so. I think there are lessons in here.

Right at the start of this I knew this was a difficult question to answer, trying to answer it has shown just how hard it is to get a definitive answer. There are still more assumptions which could be relaxed in this model and still more variables that could be added.

The model has also shown how important it is to have a sense of value. Not only between products but between features. That in turn demonstrates the importance of both valuing work in the backlog and regularly reviewing those valuations.

However, the first big lesson I think that needs learning here is: you have to know what your intention is.

You need to know what you are trying to optimise.
You need a strategy.

For example:

  • Do you want to maximise the quantity of features delivered?
  • Do you want to maximise the value delivered? (probably measured in money)
  • How much do you want to allow for urgent work? And to what standard are you going to hold those requests?
  • Do you want to promote specific knowledge (so one person can become more productive in one domain) or spread knowledge around (so many people can work on many different things)?

In many this is going to be a self-fulfilling prophecy, the result will be what you put in. That is, if people only work on one product then moving people between products will get harder and less productive. If people follow the value then value delivered will increase as people become more productive in the products with the higher value.

Knowing what your intention is should be the first step to formulating a strategy. And having a strategy is important because answering that question – “who should work on what?” – is hard.

To answer that question rationally one needs to create a model, a model far more complex than my model, then calculate every variable in the model – plus keep the variables up to date as they change. Then to apply that model to every work question which arises.


Alternatively one can formulate a rule of thumb, a heuristic, a rough guideline, a “good enough” decision process. This might sound a bit amateurish but as Gerd Gigerenzer says in Risk Savvy:

“To make good decisions in an uncertain world, one has to ignore part of the information, which is exactly what rules of thumb do. Doing so can save time and effort and lead to better decisions.”

To build up such rules of thumb requires experience and reflection, something which might be described as intuition.

So to answer my original question in terms an economist would recognise: It depends.

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Adding value – Who works on what? – part 2 of comparative advantage


In my previous post I tried to use the economic theory of comparative advantage to answer the question:

Who should work on what? or Shouldn’t every developer work on the software where they are most productive?

The economic model gave an answer but more importantly it provided a framework for answering the question. As I examined the assumptions behind the model it became clear there are many other considerations which deserve attention.

Perhaps the most important one is: value.

The basic economic model looks, perhaps naively, at quantity of goods produced. Really, one should consider the value of the goods produced. Not only did the model assume that every feature is the same size but it also assumed that all features have the same value.

Flipping back to the basic model, lets assume that each Bonds feature generates $10,000 in revenue while each Equities feature generates $20,000. Now the options are:

  1. Jenny and Joe both work on Equities, they produce seven features and generate $140,000 in revenue.
  2. Jenny and Joe both work on Bonds, they produce seven features and generate $70,000 in revenue.
  3. Joe works on Equities and Jenny on Bonds, the six features they produce generate $80,000 in revenue.
  4. Joe works on Bonds and Jenny on Equities, the eight features they produce generates $130,000 in revenue.

Clearly option #1 is the one to choose because it generates the greatest revenue even though Joe would be more productive if he were to work on Bonds. Adding value to the basic model changes the answer.

Now, again there is an assumption here: all features produce the same value. That is unlikely to be true.

Indeed, over time if no work is done on Bonds it would be reasonable to assume the value of the features would increase. Not that all features would increase in value but failure to do any would mean some of those in the backlog would become more valuable. In addition new requests might arise which may be more valuable than existing requests.

Further, while the value of Bonds features would be increasing the value of Equities might be falling. This follows another economic theory, the law of diminishing marginal utility. This law states that as one consumes more of a given product the added utility (i.e. value) derived from one more unit will be less and less.

So now we have exposed another assumption in the model: the model is static. The model does not consider the effects over time of how things change – I’ll come back to this in another context later too.

Over time the backlogs for both products will stratify, each will contain some items which are higher in value than average and some which are lower in value.

Lets suppose each product has its own backlog:

  • Equities backlog contains seven features with the values: $60,000, $54,000, $48,000, $42,000, $36,000, $30,000 and $24,000.
  • Bonds backlog contains another seven features with the values: $32,500, $10,000, $7,000, $6,000, $5,000, $4,000 and $,3000.

Now there are (at least) four options open:

  1. Equities: both Jenny and Joe work on the equities product. Together they will deliver seven features and a total of $294,000 of value.
  2. Bonds: both Jenny and Joe work on the bonds product. Together they will deliver seven features and a total of $67,500 of value.
  3. Specialise: Jenny does five equities features ($240,000) and Joe three bonds features ($49,500) delivering a total of eight features and $289,500.
  4. Value seeking: Jenny does her five equities features but Joe delivers one bonds feature, one equities feature and gets to go home early. In total they deliver six features and $302,500.


The highest value option if #4, which delivers $13,000 more than if they specialise. That might seem counter intuitive: the option that delivers the most money delivers the least features. And again it shows deciding work in the absence of value can be misleading.

The second best option is for both to do Equities only, this delivers $8,500 more than specialisation. Adding value to the basic model isn’t a big change but it has changed the answer. When output was measured in features then specialisation looked to be the best option.

Returning to the question of the static model, there is one more assumption to relax: Learning. Economist J.K.Galbraith pointed out that the comparative advantage neglects to factor in learning, and I’ve done the same thing so far.

Assuming Joe specialises in Bonds and spends most of his time working there he will learn and in time he will become more productive. Suppose after a year he can produce 5 bonds features in the time he takes to produce 2 equities features – a 66% improvement.

Now how to the numbers stack up? What is the revenue maximising choice now?

And perhaps more importantly, how long would it take before Joe’s increased output paid for all the time he spent learning?

But, another what-if, what if Joe had specialised in Equities instead? He would now be more productive on a product with higher value features.

Again the question “Who should work on what?” needs to consider intent. Which product do you want Joe to learn? Which product is expected to have the highest value? Are you maximising value or quantity?

As usual, you can argue with my model and question my assumptions but I think that only demonstrates my point: these things need thinking about.

If you want you can continue relaxing the assumptions and do more what-if calculations – for example I’ve assumed Jenny and Joe cost the same. Nor have I factored in risk or cost-of-delay. This model can get a lot more complicated. I’ve also assumed that partially done features have no value at all, each week starts afresh and no work carries over.

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Who should work on what? – Comparative advantage part 1


Returning to my theme of numerical and economic analysis of software development, I’d like to address that old chestnut:

Shouldn’t every developer work on the software where they are most productive?

We can model this question using a bit of economic theory called Comparative advantage – which is also the economics that justifies free trade. However, while this model will give us an answer it also raises a number of questions which are outside the model. In this case the model gives us a structure for examining the issues rather than providing an answer.

By the way, this discussion is going to span two blog posts, or perhaps three.

Lets set up the model with a simple case. As before there are some assumptions needed, its when we examine these assumptions that things get really interesting.

Imagine a small trading desk. The desk invests in corporate bonds and equities. Jenny has been working for the desk for some years and has written two applications for trading imaginatively called Equities and Bonds. She wrote Equities after Bonds and prefers Equities and is more productive on Equities.

Measured in features Jenny can produce 5 new Equities features or 4 new Bonds features in one week. (We’ll assume that all features are the same size for now.)

The company hires a new developer, Joe. He is new to the code bases he can only produce 2 Equities features or 3 Bonds features a week. Thus Jenny is the most productive developer on both apps.

Features per week

Now comparative advantage theory tells us not to look at the total output of either party but at the relative output. In other words:

  • For Jenny every bond feature costs 1.2 equities features. Equally Jenny can produce one equities feature at a the cost of 0.8 (4/5ths) bonds features.
  • For Joe every bond feature costs 0.66 (2/3rds) equities features. Or, to put it the other way round, Joe’s equities features cost 1.5 bond features.

Looked at this way, relatively, Jenny is a better (more productive) Equities developers and Joe is the most productive Bonds developer.

Think about that.

During one week Jenny can produce more Bonds features than Joe but when measured in terms of the alternative Joe is the more productive Bonds developer. This is the important point. You might say “look at everyones individual strengthens.” Relatively Joe is better at Bonds.

Together Jenny and Joe could produce 7 features for either product. If Jenny works where she is stronger, Equities, and Joe works where he is strongest, Bonds, then together they will produce 8 features. If they both worked on their weaker product then they will only produce 6 features combined but four of those six would be Bonds features.

So, it seems the case solved: Everyone should specialise and work on the product where the individual is relatively strongest. Although this is not necessarily the same as “who is the best developer” for a product.

But… things are more complex. Now we have the model we can start changing the assumptions and see what happens.

First off, we could relaxed the assumption about all features being a different size. However this doesn’t make any real difference. It doesn’t matter how big a feature is, Jenny is always 20% more productive on Equities than Bonds and similarly Joe is 50% more productive on Bonds than Equities. Using different size features complicates the model without creating new insights.

Varying the size of features doesn’t change the integrity of the model but it does make a difference if we start to look at throughput and consider time.

So lets relax the time assumption. What happens if Joe is in the middle of a Bonds feature and another feature gets flagged up as urgent. Should Joe drop what he is doing and pick up the urgent Bond feature?

The model doesn’t answer this question. The model is only measuring output. If we are attempting to maximise output then changing work part way through the week only makes sense if the both pieces of work – the part done original and the urgent interrupt – can still be completed by the end of the week.

So one needs to ask: is the feature urgent enough to justify Joe halting his current work and doing the new feature? Then perhaps returning to his current work?

Possibly but in making one feature arrive faster another would be delayed. Statistically there is little difference because the differences cancel each other out. Which itself demonstrates how managing by numbers can be misleading.

And what is Joe couldn’t finish both pieces by the end of the week? Would it make sense to reduce overall efficiency to expedite some work?

What if Jenny becomes available, should she work on Bonds? Even though she is relatively less productive at Bonds and would thus delay even more Equities features?

These questions can be answered in many different ways but answering them depends on what you are trying to maximise. And lets also note that in real life the data is unlikely to be so clear cut

On average Joe takes two and a half days to complete an Equities feature while Jenny completes one Equities feature a day. On average Jenny can complete her current feature and a second one before Joe could. But it doesn’t take much to invalidate that answer, in particular if feature sizes vary things change.

What if Jenny is working on an over-sized feature? – well call it urgent #1. Suppose urgent #1 is twice as big as urgent #2 and she has just started #1. Jenny will take three days to finish both features. If goes starts urgent #2 he will have it finished in 2.5 days, during that time Jenny will have urgent #1 finished. Looked at this way it makes sense for Joe to work on the highest priority even if it takes him longer.

And what happens if Equities has three, or more, urgent features? Even with Joe working more slowly than Jenny all the urgent features will be delivered sooner if Joe works on Equities too. Again, total productivity would be impacted but what is more important: total productivity or rapid delivery?

If efficiency is your objective then all is well, simply understand the relative efficiency of individuals and do the maths. (Except of course, understanding the efficiency of any individual isn’t that straight forward.) Adding time dependent features complicates things, the comparative advantage model helps show the cost of urgency although it cannot answer the question.

It is entirely possible, even likely, that efficiency is not the only concern, it may not even be the primary concern. Rather the timeliness of feature delivery may be more important.

Specifically, I have assumed that all features are about the same effort but I’ve assumed they are also the same value. Efficiency has been measured as quantity of units produced is a poor measurement compared with efficiency in value delivered. I’ll turn my attention to value in the next blog.

But before I leave this post, one more assumption to surface.

In this model Joe and Jenny are completely independent. There work does not impact the other and they share no resources. What if they did?

What if both Joe and Jenny handed their completed work to the same Tester? Or they both needed use of s single test environment? Or their work needed to be bundled into a common release?

In such cases the shared resource – the tester, the environment, the release schedule – would become the constraint on productivity. This is getting towards Theory of Constraints space.

For Joe and Jenny to work at their most productive not only would that bottleneck need enough capacity to service them both it would actually need more capacity to cope with the variation and peak load (when Jenny and Joe delivered at the same time.)

Providing that extra capacity at the bottleneck would allow Joe and Jenny to work at their maximum throughput but would introduce waste because the extra capacity would sometimes be idle. To tackle that question one needs a far more complex theory: Queuing Theory – which I’ve discussed in previous posts, Utilisation and non-core team members and Kanban: efficient or predictable, you decide.

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When does a Start-Up need Agile?


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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1% improvement

Keeping with the numerical and financial theme of the last couple of blogs I want to turn my attention to improvement and how really small improvements add up and can justify big spending. This also turns out to be the case for continual improvement and continual delivery…


How would you like it if I promised to improve your team by 1%? – I’m sure I can!

How much difference would it make if your team were 1% more productive?

Not a lot I guess.

More importantly, you’re going to have trouble making that sale to the powers that be.

You: Boss, I’d like to hire Allan Kelly as consultant for a few days to advise the team on how to improve.
Boss: How much do you expect them to improve?
You: He guarantees a 1% improvement or your money back
Boss: One Percent? 1%? Just 1%? Whats he charging $10?

No, thats not going to work is it.

People who hold the money like to see big numbers. The problem is, if the numbers are too big they become unbelievable. Those in authority want to see a significant improvement but the bigger the numbers are then the more evidence they want to see that the improvement is achievable. And when the number are big they need to be proven and that can slow everything down.

On the other hand, there are stories of teams winning (and I do mean winning) by focusing on 1% improvements. At Pipeline conference last year John Clapham talked about how the UK cycling team worked on 1% improvements. And I’ve heard several stories about Formula-1 racing teams who work hard to get 1% improvement. After all, Formula-1 racing cars are already pretty fast so getting 1% is pretty hard.

So what is it about 1%?

Surely 10% is better?

The thing is, 10% is going to be better but getting 10% is hard. Getting 1% can be hard enough, getting 10% can be 100 times harder. Even finding the things that deliver 10% improvement can be hard. On the other hand, for the typical software team, there are usually a bunch of 1% improvements to be had easily.

The trick with 1% is to get 1% again and again and again…

The trick with 1% improvement is… iteration: to get 1% improvement on a regular basis and then allow the effects of compound interest to work their magic.

The size of the improvement is less important than the frequency of the improvement. Taking “easy wins” and “low hanging fruit” makes sense because it gets you improving. Sure 10% may make a much bigger difference but you have to find the 10% improvement, you have to persuade people to go for it, you probably have to mobilize resources to get it and so on.

1% should be far easier.

Suppose you can get 1% improvement each week. Over a year that isn’t just more than 50% improvement it is well over 60% improvement – because each 1% is 1% of something bigger than the the previous 1%. Therefore a 1% improvement in week 50 is actually equivalent to 1.6% improvement in week 1.

Here is another spreadsheet where I’ve modelled this.

Suppose you have a team of 5. Suppose the cost $100,000 each per year, thats $500,000 for the team or $10,000 per week (to keep the numbers simple I’m calculating with a 50 week year.)

Now, suppose the team make a 10% value add, i.e. they add 10% more value then they cost, so each year they generate $550,000 of value. That is $11,000 per week.

Next, assume they improve productivity 1% per week. In week one they improve by $110, not much.
Week two they improve by $111, week three $112 and so on.

At this point you are probably thinking: why bother? – even in week 49 the team only add $177 to their total in week 48.

But… these improvements are cumulative. In the last week the team are delivering $6,912 more value than week one: $17,912 of value rather than $11,000. The total annual value added $159,095. That is $11,110 in week one, $11,221 in week two, …. $17,912 in week 47, $17,734 in week 48 and $17,559 in week 49.

The team are now delivering $709,095 value add per year – a 29% increase!

Put it another way: $159,095 is $31,819 per person per year, or $3,181 per week on average, and $636 per person per week.

At first glance this seems crazy: the team are adding 1% extra value per week, even in the last week they only add $177 of extra value compared to the previous week. But taken together over the year the power of accumulation means they are adding over $3,000 per week.

Go back to the start of this piece: you want to convince a budget holder. $177 isn’t even worth their time to talk about it but $3,181is.

Want to buy a book for everyone on the team? $30 per book is $150, do it.

A two hour retrospective? Thats 10 working hours for the whole team, about $2,200, well worth it.

Want to send someone to a 2-day conference, say, $1,000 for a ticket and $4,000 for lost productivity, $5,000 in total. If they come back with one 1% improvement idea then the conference pays for itself in one and a half weeks.

Suppose you invite a speaker from the conference to give a lunch and learn session. Say $1,000 for the speaker and $50 for pizza. If they give the team a 1% idea then it pays for itself that day.

Like it so much you buy a 2-day course? Now your talking big money. Although the $10,000 for the speaker is still less than the cost of having people not work. Five people each on a two day course means 10 days, $20,000 so $30,000 in total. That will take nine and a half weeks of 1% improvements. But then, one might hope that such a course delivers a bit of a bigger boost.

(Is now a good time to plug the agile training I offer? – or is that too blatant a plug?)

The important thing is to make iterate quickly and keep getting 1%, 1%, 1%. There should’t be time for agonising “Is this the best thing we should do?” – “wouldn’t doing X give more improvement than Y?” – just do it! The other ideas will still be good next week.

And don’t worry if it goes wrong. Not every possible improvement will deliver 1%, some will probably go so wrong they damage performance. Just recognise such changes don’t work and quickly back them out.

When you do the numbers it all makes sense.

Now you can call me 🙂

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