Business

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…

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.

Phew.

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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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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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	Equities	Bonds
Jenny	5	4
Joe	2	3

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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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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