How to build better products by betting small and keeping an eye on the race

The Grand National is a National Hunt horse race held annually at Aintree Racecourse in Liverpool, England. First run in 1839, it is a handicap steeplechase over 4 miles 514 yards with horses jumping 30 fences over two laps. It is the most valuable jump race in Europe, with a prize fund of £1 million in 2017 [1].

In the UK and Ireland, the Grand National is the most watched race of the year. It is an amazing spectacle to watch for the excitement, the fierce competition, the danger that comes with the massive fences and obviously to see if the horse we backed wins.

Every year, I put a small bet on one of the horses, but I don’t remember ever winning in my last 20 attempts. Predicting the winner on a field of 40 horses is not a simple matter, it is indeed very complex.

So many things can happen during the race that can throw even the most experienced and knowledgeable horserace expert. Among the things that happen during the race, changing weather and track conditions, the form in which the horse woke up in the morning, the training or lack thereof he had the week preceding, the horse in front of yours falling at the fence and bringing you down with him, the amount of drinks that the jockey had the night before, how distracted he might be from other thoughts in his head, after all his wife is divorcing him, the effect that the noise from the crowd has on your horse, and just about another million random events that could happen in those 5 minutes of racing.

Predicting a winner among the 40 participants is hard

But what if we could bet while the race has already started? Would that help us with our prediction?Well, yes. We could discard the horses that have fallen at the first fence, the unseated ones and the ones that have fallen too much behind.

That’s interesting, does that mean that now our chance of backing the right horse has increased?

Yes indeed and this does not only work for horse racing, it works for product development and innovation too.

Let’s imagine an organisation with €1M budget. Based on their understanding of the market they decide to invest the full budget into delivering “my shiny project” that again according to their understanding of the market will give them a return on investment when finished of €10M.

In gambling terms they are making a $1M bet on the “my shiny project” horse at 9/1 odds.

So what do they do? They use the €1M for assembling a team getting them the necessary resources to get the work done and do the work to deliver the project. While they are delivering, the race is on, but they are not watching it, they are busy building the project. Once they are done, only then they will know if their bet was a winning one.

If things go well, they might even get better odds as return, they might make $100M instead of 10, but history has thought us that this happens only in statistically negligible cases, while most of the time the result is that we have lost most of our money if not all of it. There are more fallers than winners at the Grand National year after year; and indeed there are more losers than winners, in fact on a field of 40, 39 lose, and one wins.

What can we do to help our organisation win the bet? Is there a possible approach that gives us the edge over other companies?

Oh yes, there are ways to help you win the race, let’s look at one example.

We take our €1M budget and allocate 9% of it to bet on the product we believe will win based on our understanding of the market and we pay an extra 1% to watch the race.

How do we watch the race? We invest 1% of our budget in tracking how our customers use our product with the intent of understanding what our customer really needs.

This would equate to spending €90k on betting on one horse and €10k on a live stream of the race we are betting on.

bets
Don’t bet all your money when you have the least amount of information

 

While we are watching we find out that our original horse has fallen at the first fence, and we also observe that another horse, we never thought could be the winner, is jumping very well and is at the centre of the leading group, a very good strategic position.

Then obviously we decide to invest another €90k betting on that horse and another €10k to keep on watching the race.

risk
Gain an unfair advantage against your competitors by watching how your customers behave and limit the risk of your bets

You can clearly see where I am going with this analogy. By limiting the risk of the initial bet and investing in observability I am gaining a clear advantage on the people that made their big bet before the start of the race.

 

In product development we can do this by making sure we create customer observability in our product to have a live stream view of the customer behaviours that informs our future bets.

Sources:

[1] https://en.wikipedia.org/wiki/Grand_National

What can Product Development learn from our body temperature regulating system?

Systems are all around us, and inside us. Understanding the systems we live and work in is fundamental if we want to navigate the problems that they carry.

Let’s think about our body as a system. It is a pretty complex and advanced one as it went through millions of years of evolution to get to the current state. Its working is truly fascinating and even though we have been studying it for millennia, there are parts that we still don’t fully understand.

Our body, as all systems, contains feedback loops. One we are very familiar with, is our body temperature regulating system. In order for our body to work well, its temperature needs to be between 36.5 and 37.5 degrees Celsius. In extreme circumstances, if it goes under 25 degrees we can die and a rising body temperature can damage our organs very quickly.

How do we make sure that this doesn’t happen without a thermometer that measures our temperature constantly?

Our body has a very sophisticated yet simple feedback loop system that allows us to regulate the temperature to the optimal range. In fact if we go out naked in the snow, we immediately feel a cold sensation, decide to get back into the house to put on some heavy clothes and then go out again. If we are running or exercising for a prolonged period of time and our temperature raises, our system will automatically start sweating to reduce and regulate it to its optimal range.

body temperature
This in Systems Thinking terms is called a balancing feedback loop.

What happens every day of our life is that our body temperature will oscillate within a range of acceptable temperatures because, through evolution, it has designed a defence system based on fast feedback loops. By continuously knowing when it’s too hot or too cold we are guaranteed we won’t die frozen or for too much heat. A slower feedback might have caused our body to freeze, imagine for example if we felt the cold only after 24 hours in the snow, but a feedback loop fast enough prevents all that.

What a great system our body is. But even such a wonderfully evolved system has its Achilles heel, in fact it can be damaged by slow feedback loops.

Let’s think for example about the effects of smoking tobacco on our body. How come we only recently found out that smoking is so dangerous for our body? Why did it take so long?

When we smoke our first cigarette, chemicals such as tar and formaldehyde penetrate the cells in the lung tissue and change them. At first our body may be able to repair the damage. With repeated exposure, normal cells that line our lungs are increasingly damaged. Over time, the damage causes cells to act abnormally and eventually cancer may develop.

The problem is in the fact that the time that will take for the cancer to develop is so long that only recently we could only start hypothesising tobacco smoke was a potential cause. You can easily imagine why. In ~30 years our body absorbs an incredible amount of chemicals coming from all sources, the food we eat, the air we breathe and the tobacco smoke we inhale. How could we have guessed? It was very difficult and only through data driven medical studies we managed to identify a clear link between tobacco smoke and cancer.

tobacco smoke
Slow feedback loop

Tobacco smoking is a very recent habit in human history if we consider humans evolution and our body has not yet developed a defence from it. What happens unfortunately is that our body will alert us of the cancer (feedback loop) only when it has damaged one of our organs. At that point, unfortunately, it is often too late to repair the damage.

A slow feedback loop can often look like the this picture. No impact for a long period of time, then a massive impact all of a sudden.

Slow feedback loops are everywhere around us, look at how some policies that seemed harmless at first sight, have caused migrations, desertification and destruction, years after, look at how the excess co2 in the atmosphere is damaging our planet slowly but nevertheless unequivocally.

Not being able to predict the effects of policies and changes to a system is troubling, but how does this fit with modern organisations and product development?

The link is in the feedback loops. We have seen earlier how the fast feedback built in our body temperature regulating system has helped our body defend itself from changes in temperature for millions of years, can we learn something from it?

A system that oscillates within a healthy range, like our body temperature, is a balanced and healthy system. What produces the healthy oscillation is the speed at which the feedback is delivered. Our body senses the changes in temperature very quickly and allows for regulating it in a way that appears to us to be continuous.

Now let’s think about organisations that rather than regulate their own temperature are trying to achieve an outcome.

Let’s look at product development for example.

Traditional organisations spend quite a lot of time studying the market to identify new solutions or products that can help them achieve an outcome. This work is often done by internal experts. Such experts have been involved in delivering previous products and are naively expected to have the wisdom that is required to design the product that will achieve the goal.

A lot of work happens inside the organisation and a lot of time passes before the new product is given to the customers. The more time the idea stays within the organisation the more we build assumptions upon assumptions instead of satisfying real customer needs. After one year or two we finally have a new product on the market, but very often we realise that the outcome we had originally thought we would get is not materialising. We often are very far from the expected outcome. We are objectively surprised, sometimes astonished at the results.

Our plan didn’t survive contact with reality.

Reality indeed, this is the first time we get feedback from our customers and it’s not what we thought it would be.

waterfall
Effect of slow feedback loops in Product Development

 

Organisations rarely understand the cause of the problem and often an unsuccessful product causes blame games. Everybody is to blame, excluding the people that designed the product. “The market has changed!”, “The latest regulatory policy makes it more difficult to use!”, “The downturn of the economy killed us!” Et cetera. I even heard leaders blame the customers, saying things like “they don’t understand!”

Slow feedback loops for product development cause something like this picture. The delta can be very big in the presence of slow feedback loops.

What can we do to avoid the truly sobering experience of showing our customers the fruit of years of our work and seeing it rejected? Well, we can’t avoid it completely but we can make it much less painful if we introduce a balancing feedback loop like the one

fastfeedback
Fast feedback loops in Product Development

that regulates our body temperature. 

The first thing to do is to identify an outcome we want to reach over time. This outcome is for us now the ideal body temperature we want to maintain.

The second thing we need to do is to plan regular and frequent feedback loops with our customer to verify directly in the real world how we are doing against the outcome.

The third thing is to measure how we are doing against the outcome and design the next bit of functionality taking in consideration the learning from the last feedback loop.

A fast feedback loop on product development causes a behaviour like the one in the picture.

By doing this we make sure not to diverge too much from our desired outcome. We know that we will make bad decisions that might move us further away from reaching it, but we also know that through frequent feedback loops we will be able to discover our mistakes early and take actions to repair them in a cost effective way.

It looks like we can learn a lot from our body temperature regulating system and treat our customers like our body regulates our temperature, with care and continuously.

How to maximise the work not done

Recently, due to a new exciting engagement, I have been thinking a lot about simple ways to explain the advantages of iterative incremental delivery over big bang releases. When speaking to C-level executives, coaches like me often have very little time, so we need to be able to engage with language they understand to make those few minutes count.

Money and impact on revenue seems to be a topic that is widely understood and accepted, so I have used and will use it during those conversations. My recent musings have brought me to a place where there is a currency that for modern organisations might be more important than revenue itself. Knowledge.

Now, let me expose differences between iterative incremental delivery and bing bang releases in terms of revenue and of knowledge.

Let’s start with revenue.

This picture is probably familiar to most of the agile/lean coaches out there and it is a good one to start the conversation on why releasing often while monitoring our customers behaviours has an immediate positive effect

revenue

From the picture is clear that incremental delivery has an earlier return on investment, while for big bang we have to wait until time t1 to get any return.

This is quite powerful but I don’t think it tells the full story.

Can we express the differences in term of something else?

To look at other aspects of the full story, we need to start visualising a different dimension than the usual revenue or $.

This new dimension is the currency of modern organisations, and it is called knowledge.

Let’s look at what happens with knowledge in a big bang delivery approach:

bigbang

At the start of envisioning and planning we start building knowledge about what the customer wants but at the same time, being humans and fallible, we start building wrong assumptions about it. We will get full knowledge (wisdom) only after the release date t1. At that point we will be able to validate our assumptions and will build the knowledge about what the customer really wants. Isn’t it a bit too late?

Now let’s look at the same dimensions for an incremental iterative delivery

incremental

As we can see, even in our iterative incremental world we build up wrong assumptions about what the customer wants, the difference is that we disprove them very quickly by delivering early and validating them through customer behaviour. This reduces the risk of building assumptions over wrong assumptions but most of all frees our mind to look at how the customer behaves using our product and understand it better. As you can see the assumptions get eliminated and rebuilt often but the knowledge grows continuously. This is very different from our previous graph, what happens if we overlay the two is the following

Maximising the amount of work not done

Every time we deliver a small increment we must ask ourselves 2 questions:

  1. Have we satisfied our customer needs?
  2. Do we still need to do more to satisfy them?

worknotdone

Given K the point at which we can answer one of the 2 questions with a NO then a decision that impacts on the amount of work to be done can be made.

Case A: If at point K i have enough knowledge to decide that I have done enough to satisfy the customer and any other added work will have diminishing return on investment, I can decide to stop.

Case B: It at point K I have enough knowledge to decide that the customers aren’t happy with the product and I am losing revenue, I can decide to stop.

In cases A and B the red area is the work not done as a consequence of my decision. Such decision could not be taken with the knowledge gained with a big bang release if not AFTER the release and all the work had been done.

So, in conclusion, you should use iterative incremental delivery to maximise the amount of work not done. This way, you will save money on work not done, will have the opportunity to spend that money on something your customers really want, obviously through iterative incremental and knowledge seeking delivery.

From Consulting to Coaching, what’s the difference for the client?

“An expert is a man who has made all the mistakes which can be made, in a narrow field.” Niels Bohr

In order to resolve complex problems, organisations often hire experts for help.

When I am such expert, very early in the engagement I explain how I can help resolve the problem they have in a few different ways.

What I tell them is that I can provide my service at three different levels:

  1. Coaching, where I help the people i coach ask the right questions, find the answers within themselves and resolve the problem.
  2. Mentoring, where I use a combination of coaching, training and also offer some options that might help resolve the problem .
  3. Consulting, where I either solve the problem as individual contributor (doing) or give instructions in order to solve the problem (telling)

The idea is that the paying client will decide what service level he requires, but the decision is not very straight forward if I only provide the three definitions above.

Lately I have found useful using a new approach (new to me) to help my clients make the right choice. With this approach, I show the impact of each of the different levels of service on “things” paying clients care about.

I use three simple visualisations, if you think they can help you, feel free to use them. If you have another interesting visualisation you found useful, please share it with me.

Future dependence on my service

Dependence

Through this view the client will be able to see how one approach is going to create a dependence on my service to be continued in the future, while the opposite is going to remove completely the need for my service. This is useful for clients to verify what fits better with their long term strategy on dealing with problems similar to the ones I am helping resolve.

Time to Resolution:

TimetoResolution

Through this view I highlight the impact of the approach on the time that it will take to resolve the problem I have been hired for. This will help the customer make a decision based on the urgency of the problem. The more urgent the more to the left we will go.

Resistance to Change

ResistancetoChange

This view in my opinion is extremely important because it is not always obvious to my clients but has massive impact on the organisation’s ability to deal with the result of my work.

If I am a specialist working on my own (doing) i won’t have any resistance as I am the one that is changing to resolve the problem. If I have to tell other people how to change to resolve a problem by giving instructions (telling) I am very likely going to be told “go away!”. If I exercise power to apply the change, things won’t be much better as people will do what I say but on a spectrum from unhappy compliant to angry saboteur. If I help people find the right way to change for the better, I will acquire many allies that will be invested in making change happen and most importantly sustain it in the long run.

Are we shifting left or in a continuous loop?

leftI just read a refreshing point of view on a subject I love, from one of my testing heroes, Janet Gregory.

Before proceeding, please take your time to read her point of view.

I use it

I have been using the Shift Left and even Shift Right terminology, as we say in Dublin, for donkeys years. I am not sure who I heard it from, I seem to vaguely recall a conversation with Gojko Adzic, or was it Dan North? I am getting more and more forgetful, whomever I pick I am very likely wrong, so forget about where i heard it from. and let’s move on.

I love it, it’s leaner!

I have to say I love Shift Left, it has helped me and my teams understand where we needed to take our attention to. In my mind it was never only about redistributing testing activities, but it brought great emphasis on prevention over detection, going towards a leaner testing, some people like to call #NoTesting.

I am all for prevention and I was always going to end up with the Shift Left party.

I understand

Now, reading what Janet says, I understand perfectly where she is coming from and there is no denying that the continuous infinity loops models describe the new reality much better than Shift + direction does.

Was it always like this?

Software development today is certainly non linear 100% agree.

Now correct me if I am wrong here, but waterfall is linear.

The water goes in one direction and there is no going back unless we are on some planet with different gravitational laws, which would be pretty cool to see now that i think about it 🙂

The key is “what” are you shifting from?

My take is that, maybe, in the early days, testers suffering with a bad waterfall hungover, would have understood Shift Left with more ease than if presented with the new continuous models (that in fact, didn’t exist yet).

We are improving

Think about being a waterfall tester in the mid late 200x. You are in a room and I talk to you about how to think and use Shift left. Somebody else maybe Janet, talks to you of either of the (excellent) continuous models we have today. What do you reckon you are going to do next?

If I had to guess, I’d say you’d Shift left. It is easier to understand if all you know is waterfall.

There is a left, a right and a centre in waterfall and people get it when you ask them to go left.

It was the first step to move from waterfall to agile, at least for me.

I see the continuous models as the reflection of the growing maturity of modern testers.

We are ready

We are now ready, bring the new continuous models on and let’s leave the shifting to rally drivers.

The models are good now, because testers are ready to understand, embrace and apply them.

Janet, thank you so much for making me think!

 

 


					

The hidden dangers of Process Debt

Most of us involved in software development are familiar with the term “technical debt”.

As a quick reminder, it was introduced by Ward Cunningham to describe the phenomenon that occurs when we use code that is easy to implement in the short run instead of applying the best overall solution we have identified.

It is by definition a conscious decision to take a shortcut for short term gratification (like taking out a new credit card for a holiday) that in thew long term will cost us to spend extra time in development when improving the system (like paying interest on the credit card debt).

Until the capital is repaid in full we will always pay interest when adding features to the system as the debt creates obstacle to adding new code efficiently.

Ward suggests that when we are in a situation of rushing something out we need to be conscious that we will have to pay the capital in the future or the interests will cripple us.

Ward suggests refactoring as a solution to paying off technical debt.

I really like Ward’s vision and I want to expand the metaphor one level up.

Now if we change the word “software” in my description of technical debt above with “processe” we can define Process Debt.

Replacing we get:

Process Debt is the phenomenon that occurs when we use a processes that is easy to implement in the short run instead of applying the best overall solution we have identified.

Let’s look at one example

You notice that lately the system seems to be more unstable than usual. You know this because there are more calls from customer care and more defects get raised.

Option 1: You want to get to the bottom of the situation and believe that a root cause analysis with 5 whys could get you there. If you use this approach you will probably identify a change in your process to help you prevent some defects in the future.

Option 2: You implement a better policy for developers to select the defects to work on reducing the time the defects are in the unresolved queue while maintaining new features creation throughput relatively stable.

You know Option 1 is better in the long run because it will generate a change in the process to reduce the amount of rework. This will mean less time spent fixing and more time spent on new features that as a consequence means higher throughput and lower lead time for new features.

Option 1 requires some investment. You need to hold one or more root cause analysis sessions, identify the problem(s) experiment with solutions until you find a solution that mitigates your problem.

If you are under pressure, because the new product needs shipping and the old defects need fixing, you are likely to choose option 2.

By doing this you have introduced process debt

The capital of this debt is the lack of change in the process  you could have identified if using Option 1.

Oh, yes, I forgot, change is difficult.

The worst part of it is the interest on the debt you will pay forever until you pay the capital. This interest will appear in the form of.
1. bugs keep on coming, we seem to be fixing bugs all the time
2. new features get delayed because now the queue is in the new features to be delivered
3. the lead time of any new feature is expanded by a factor X
4. the customers continue on complaining because of your defects in production
5. your product owner starts being annoyed at the amount of work spent by the development team on defects
6. developers are tired of always fixing defects
7. …
n. need I say more?

This interest will be paid for the rest of your life if you don’t fix the problem.

Months after you are covered in defects, are stressed by your customers and change job.

Is this healthy? Certainly not.

Is there a cure? Oh yes, indeed.

Continuous improvement has the same effect on process that refactoring has on software. It repays the capital of your process debt.

With small changes in the form of experiments you will be able to clearly discuss the problem that the team is having and make small tweaks continuously.

My esteemed colleague and fervid innovator Claudio Perrone presents his continuous improvement model called PopcornFlow saying

If change is hard, make it continuous

I could not agree more.

By making process change a continuous activity, we enable behaviours that will stay with teams forever and we unleash the power of people’s creativity in improving their own way of working.

 

 

Test coach versus test specialist, impact on queues

I recently had a very interesting conversation with a group of skilled testers on whether or not there should always be a test specialist in a cross-functional team.

A lot of people say yes, my personal experience says not.

My personal experience tells me that the most effective and efficient approach uses a test coach that slowly makes himself scarce. My experience focuses on creating competencies for completing an activity (testing) in a collaborative context.

One aspect I am finding difficult to explain is the impact on queues of a test coach approach, I will use a series of Scenarios and pictures to facilitate my reasoning.

If you feel like substituting activity A = development and activity B = testing feel free, but this approach is activity agnostic in my experience as I have applied it to analysis, and development obtaining similar results.

Scenario 1 – Test specialist

In the presence of one specialist on Activity B and many specialists on activity A.
Problem: Long q
ueues form in Ready for Activity B (Case1) or worse specialist multitasks on many activities at the same time slowing down flow of work as per Little’s law (Case2)

Screen Shot 2017-05-29 at 10.40.47

Scenario 2 – Test coach

Stage 1: In the presence of one coach on Activity B and many specialists on activity A
Initially coach pairs on Activity B with people that do activity A. This way we obtain 2 benefits:

  1. Queue in “Waiting for Activity B” is reduced as one person normally performing activity A is busy pairing with coach on one activity B task
  2. By pairing on activity B feedback loops are shortened
  3. Person with activity A acquires new skills to perform activity B from pairing with coach
  4. Quality of activity B increases as it is a paired activity
  5. Flow improves because of 1 and 2

Screen Shot 2017-05-29 at 11.22.49

Stage 2: When cross-pollination of skills required for activity B starts to pay off, we have 2 benefits

  1. Normally some activity A person will show particular abilities with activity B, in this case this person can pair with another less skilled activity A person to perform activity B
  2. Queue in “Waiting for Activity B” is reduced as more people with activity A skills are performing activity B
  3. Flow of value improves lead time decreases
  4. More activity A people get skills on activity B

Screen Shot 2017-05-29 at 10.56.00

Stage 3: All activity A people are able to perform activity B
Activity B Coach can abandon the team to return only occasionally to check on progress. Benefits:

  1. Activity A and activity B can be performed by every member of the team
  2. the WIP limit can be changed to obtain maximum flow and eliminate the queue in Ready for Activity B.
  3. The flow of value is maximised
  4. The lead time is minimised

Screen Shot 2017-05-29 at 10.58.05

WARNING: I have applied this approach to help teams with testing for many years. It has worked in my context giving me massive improvements in throughput and reduction of lead time. This is not a recipe for every context, it might not work in your context, but before you say it won’t work, please run an experiment and see if it is possible.

This is not the only activity that a good test coach can help a team on, there are many shift left and shift right activities that will also reduce the dependency on activity B.

I have been told a million times “it will never work”, I never believed the people who told me and tried anyway, that’s why it worked.

Try for yourself, if it doesn’t work, you will have learned something anyway.

Is agile Alive? Dead? Misunderstood?

Lats Sunday, after reading multiple “Agile is Dead” articles, I posted this short update on linkedIn

screen-shot-2017-03-05-at-11-03-37

As you can see from the stats, in less than 7 days it has received a lot of attention (I am not an influencer and my updates generally do not receive such large feedback)

My contacts in linkedIn include a lot of Agile or Lean Coaches and as expected, initially the message got some positive comments. Soon after some agile detractors joined the conversation and made it much more interesting as generally feedback that comes from different perspectives enriches the conversation adding dimensions that sometimes cannot be expressed by a biased mind.

I noticed 3 interesting trends in the messages.

  1. When agile is not driven by technology, agile fails
  2. When agile is driven by technology and not the business stakeholders, agile fails
  3. Agile is only useful to deliver something nobody wants quickly

The first 2 are extremely interesting, in fact they say exactly the opposite thing but they both come to the same conclusion “agile is dead”. I read and reread those messages and then I saw it

If agile is driven by one part of the organisation, whichever it is, and trust is not built within the whole organisation, it will fail. Do it like this and agile is dead before you even start.

If you try to own something that will change your organisation and run with it, you better make sure you share your vision, your responsibilities and your success with the rest of the organisation. How do you expect people outside your little world to want to follow you in this difficult change if they don’t know, understand, own and help you change. Agile/lean transformations are not driven by a department, they are driven by the whole.

And the result might be that you even stop talking about departments and only talk about the whole.

Now on objection #3.

Agile is only useful to deliver something nobody wants quickly

I have seen this very often and honestly makes me sad. A lot of scrum implementations have a Product Owner that is seen as the heart of the product, the person that understands the vision of the product and that takes the responsibility to take the important decisions for the future of the product in regards to strategy, prioritization and so on.

If you look at it this way, you might think that the PO is a single point of failure, in fact what if he is not able to make good decisions, how about his bias, is he a dictator?

As an agile coach I make sure that any product owner that works with me will have the tools for making good decisions. He in fact will know how to manage flow using WIP limits, he will be aware and become proficient in UX techniques, he will learn how to monitor, gather and use feedback from his customers, he will understand the importance of small experiments, he will be aware of cost of delay and when prioritising his features and user stories will have access to many advanced prioritization techniques.

Being agile does not mean automatically ignoring lean startup, lean UX, research. No that is not being agile, that is being a scrum master after 2 days training.

 

Testers, what business are you in?

businesscat

Recently I read an inspirational story from Jesse Lyn Stoner’s excellent blog  I would like to share with you.

The owner of a window shades company, was asked by a business consultant “What business are you in?” He initially answered “We are in the window shade business”. Then he was asked “When someone walks into your store, why do they want a window shade? What are you really selling?” he had to pause and think before he saw it

“We’re in the light-control and privacy business! – not the window shade business.”

The implications of this discovery were that by understanding the company real purpose, he was able to introduce new products that his customers loved.

You can find the full story (here)

Reading this story reminds me of the struggle of the testing community to get buy in from business owners. Years and years of hearing complaints that business owners don’t understand the importance of testing, they don’t appreciate the hard work of testers, I have grown tired of it.

I believe it’s not the business owners not to understand testers, I believe it is testers not understanding the business they are in.

The majority of the testers I know believe that they are either in

“The business of finding bugs”

or

“The business of sourcing information to help make decisions”

The testers I want to work with are in “the business of delighting their customers”, that is what we all need to be in.

If we make our business “delighting our customers” believe me, we will find a lot of innovative ways to do it as testers.

Forget about defects, forget about information, focus on delighting your customers and

  1. you will find innovative ideas to improve the quality of your product
  2. the business owners will love your work – and most of all
  3. you will have a lot of fun in the process!