If you think you would like to use these ideas at your company, but you are unsure where to start, I can describe what we did at Avvo. I joined when the company was already nine years old. It had a mostly monolithic architecture running in a single data center with minimal redundancy.
There were some things that we did quickly to move to a more fail-safe world.
Moving from planning around objectives to planning around priorities
First, we worked to build a supportive culture that could handle the inevitable failures better. We moved from planning around specific deliverable commitments to organizing our work around priorities.
Suppose specific achievements, my output, measure my performance. This way of measuring performance often creates problems.
Suppose I need to coordinate with another person, and their commitments do not align to mine. That situation will create tension. If the company’s needs change, but my obligations do not, there is little incentive to reorient my work. To achieve my commitments, I can be thwarted by dependencies or hamper the priorities of the company.
People in leadership like quarterly goals or Managing By Objectives because they create strict accountability. If I commit to doing something and it is not complete when I say it will be, I have failed.
Suppose you think instead about aligning around priorities. In that case, those priorities may change from time to time. Still, if everyone is working against the same set of priorities, you can be sure that they are broadly doing the right things for the company. Aligning to priorities sets an expectation of outcome, not output.
Talk about failure with an eye to future improvement instead of blame
The senior leadership team must be aligned with these approaches. The rest of the organization may not be initially. When leaders talk about failure, they must do it with a learning message rather than blame or punishment. People should know that the expectation is that they may fail. If they are avoiding failure, then they probably aren’t thinking big enough. It is a message that “we want to see you fail, small, and we want to make sure we learn from that failure.”
I created our slack channel to share the lessons from our failures. I sent a message to my organization, making it clear that I don’t expect perfection. I shared my vision that we become a learning organization in town halls and one-on-ones.
Monoliths are natural when building a new company or when you have a small team. Monoliths are simple to make and more straightforward to deploy when you don’t have multiple teams building together. As the codebase and organization grow, microservices become a better model.
It is critical to recognize the point where a monolith is becoming a challenge instead of an enabler. Microservices require a lot more infrastructure to support them. The effort to transition from one architecture to another is significant, so it is best to prepare before the need becomes urgent.
Avvo had already started moving to a microservices architecture, but lack of investment stalled the transition. I increased investment in the infrastructure team. The team built tools that simplified the effort of creating, testing, monitoring, and deploying services. We then made rapid progress.
In every company, I use the lessons that I have shared in this article to build a culture where teams can innovate and learn from their users. It manifests differently with each group, but every team that has adopted these ideas has improved both business outcomes and employee satisfaction. Work with your peers to adopt some of these ideas. Start small and grow. The process of adopting these concepts mirrors the product development process you are working to build.
If you decide that it isn’t a good fit for your company, you will have failed smart by failing small.
I will leave you with a final thought from Henry Ford.
If you are a long-time Spotify user, you probably won’t recognize the interface shown in the photo below. In May of 2015, though, Spotify was very interested in telling the whole world about it. It was a new set of features in the product called “Spotify Now.”
I lead the engineering effort at Spotify on the Spotify Now set of features. It was the most extensive concerted effort that Spotify had done at the time, involving hundreds of employees across the world.
Spotify Now was a set of features built around bringing the right music for you at any moment in time. The perfect, personalized music for every user for every moment of the day. This effort included adding video, podcasts, the Running feature, a massive collection of new editorial and machine learning generated playlists, and a brand new, simplified user interface for accessing music. It was audacious for a reason. We knew that Apple would launch its Apple Music streaming product soon. We wanted to make a public statement that we were the most innovative platform. Our goal was to take the wind out of Apple’s sails (and sales!)
Given that this was Spotify and many of the things I’ve shared come from Spotify, we understood how to fail smart.
As we launched the project, I reviewed the project retrospective repository. I wanted to see what had and had not worked in large projects before. I was now prepared to make all new mistakes instead of repeating ones from the past.
We had a tight timeline, but some of the features were already in development. I felt confident. However, as we moved forward and the new features started to take shape in the product’s employee releases, there was a growing concern. We worried the new features weren’t going to be as compelling as the vision we had for them. We knew that we, as employees, were not the target users for the features. We were not representative of our users. To truly understand how the functionality would perform, we wanted to follow our product development methods and get the features in front of users to validate our hypotheses.
Publicly releasing the features to a narrow audience was a challenge at that time. The press, also aware of Apple’s impending launch, was watching every Spotify release exceptionally closely. They knew that we tested features, and they were looking for hints of what we would do to counter Apple.
Our marketing team wanted a big launch. This release was a statement, so we wanted a massive spike in Spotify’s coverage extolling our innovation. The press response would be muted if our features leaked in advance of the event.
There was pressure from marketing not to test the features and pressure from product engineering to follow our standard processes. Eventually, we found a compromise. We released early versions of the Spotify Now features to a relatively small cohort of New Zealand users. Satisfied that we were now testing these features in the market, we went back to building Spotify Now and preparing for the launch while waiting for the test results to come back.
After a few weeks, we got fantastic news. For our cohort, retention was 6% higher than the rest of our customer base.
For a subscription-based product like Spotify, customer retention is the most critical metric. It determines the Lifetime Value of the customer. The longer you stay using a subscription product, the more money the company will make from you.
With a company of the scale of Spotify, it was tough to move a core metric like retention significantly. A whole point move was rare and something to celebrate. With Spotify Now, we had a 6% increase! It was massive.
Now, all of our doubt was gone. We knew we were working on something exceptional. We’d validated it in the market! With real people!
On the launch day, Daniel Ek, Spotify’s CEO and founder, Gustav Söderstrom, the Chief Product Officer, and Rochelle King, the head of Spotify’s design organization, shared a stage in New York with famous musicians and television personalities. They walked through everything we had built. It was a lovely event. I shared a stage in the company’s headquarters in Stockholm with Shiva Rajaraman and Dan Sormaz, my product and design peers. We watched the event with our team, celebrating.
As soon as the event concluded, we started the rollout of the new features by releasing them to 1% of our customers in our four largest markets. We’d begun our Ship It phase! We drank champagne and ate prinsesstårta.
I couldn’t wait to see how the features were doing in the market. After so much work, I wanted to start the progressive roll out to 100%. Daily, I would stop by the desk of the data scientist who was watching the numbers. For the first couple of days, he would send me away with a comment of “it is too early still. We’re not even close to statistical significance.” Then one day, instead, he said, “It is still too early to be sure, but we’re starting to see the trend take shape, and it doesn’t look like it will be as high as we’d hoped.” Every day after, his expression became dourer. Finally, it was official. Instead of the 6% increase we’d seen in testing, the new features produced a 1% decrease in retention. It was a seven-point difference between what we had tested and what we had launched.
Not only were our new features not enticing customers to stay longer on our platform, but we were driving them away! To say that this was a problem was an understatement. It was a colossal failure.
Now we had a big quandary. We had failed big instead of small. We had released several things together, so it was challenging to narrow down the problem. Additionally, we’d just had a major press event where we talked about all these features. There was coverage all over the internet. The world was now waiting for what we had promised, but we would lose customers if we rolled them out further.
Those results began one of the most challenging summers of our lives. We had to narrow down what was killing our retention in these new features. We started generating hypotheses and running tests within our cohort to find what had gone wrong.
The challenge was that the cohort was too small to run tests quickly (and it was shrinking every day as we lost customers). Eventually, we had to do the math to figure out how much money the company would lose if we expanded the cohort so our tests would run faster. The cost was determined to be justified, and so we grew the cohort to 5% of users in our top four markets.
Gradually, we figured out what in Spotify Now was causing users to quit the product. We removed those features and were able to roll out to the rest of the world with a more modest retention gain.
In the many retrospectives that followed to understand what mistakes we’d made (and what we had done correctly), we found failures in our perceptions of our customers, failures in our teams, and other areas.
It turns out that one of our biggest problems was a process failure. We had a bug in our A/B testing framework. That bug meant that we had accidentally rolled out our test to a cohort participating in a very different trial. A trial to establish a floor on what having no advertising in the free product would do for retention.
To Spotify’s immense credit, rather than punish me, my peers, and the team, instead, we were rewarded for how we handled the failure. The lessons we learned from the mistakes of Spotify Now were immensely beneficial to the company. Those lessons produced some of the company’s triumphs in the years that have followed, including Spotify’s most popular curated playlists, Discover Weekly, Release Radar, Daily Mixes, and podcasts.
This graph shows investment into a feature over time through the different phases of the framework. Investment here signifies people’s time, material costs, equipment, opportunity cost, whichever.
Imagine this scenario: you are coming back from lunch with some people you work with, and you have an idea for a new feature. You discuss it with your product owner, and they like the idea. You decide to explore if it would be a useful feature for the product. You have now entered the “Think It” phase. During this phase, you may work with the Product Owner and potentially a designer. This phase represents a part-time effort by a small subset of the team–a small investment.
You might create some paper prototypes to test out the idea with the team and with customers. You may develop some lightweight code prototypes. You may even ship a very early version of the feature to some users. The goal is to test as quickly and cheaply as possible and gather some real data on the feature’s viability.
You build a hypothesis on how the feature can positively impact the product, tied to real product metrics. This hypothesis is what you will validate against at each stage of the framework.
If the early data shows that the feature isn’t needed or wanted by customers, your hypothesis is incorrect. You have two choices. You may iterate and try a different permutation of the concept, staying in the Think It phase and keeping the investment low. You may decide that it wasn’t as good an idea as you hoped and end the effort before investing further.
If you decide to end during the Think It phase, congratulations! You’ve saved the company time and money building something that wasn’t necessary. Collect the lessons in a retrospective and share them so that everyone else can learn.
The initial tests look promising. The hypothesis isn’t validated, but the indicators warrant further investment. You have some direction from your tests for the first version of the feature.
Now is the time to build the feature for real. The investment increases substantially as the rest of the team gets involved.
How can you reduce the cost of failure in the Build It phase? You don’t build the fully realized conception of the feature. You develop the smallest version that will validate your initial hypothesis, the MVP. Your goal is validation with the broader customer set.
The Build It phase is where many companies I speak to get stuck. If you have the complete product vision in your head, finding the minimal representation seems like a weak concept. Folks in love with their ideas have a hard time finding the core element that validates the whole. Suppose the initial data that comes back for the MVP puts the hypothesis into question. In that case, it is easier to question the validity of the MVP than to examine the hypothesis’s validity. This issue of MVP is usually the most significant source of contention in the process.
It takes practice to figure out how to formulate a good MVP, but the effort is worth it. Imagine if the Clippy team had been able to ship an MVP. Better early feedback could have saved many person-years and millions of dollars. In my career, I have spent years (literally) building a product without shipping it. Our team’s leadership shifted product directions several times without ever validating or invalidating any of their hypotheses in the market. We learned nothing about the product opportunity, but the development team learned a lot about refactoring and building modular code.
Even during the Build It phase, there are opportunities to test the hypothesis: early internal releases, beta tests, user tests, and limited A/B tests can all be used to provide direction and information.
Your MVP is ready to release to your customers! The validation with the limited release pools and the user testing shows that your hypothesis may be valid–time to ship.
In many, if not most, companies shipping a software release is still a binary thing. No users have it, and now all users have it. This approach robs you of an opportunity to fail cheaply! Your testing in Think It and Build It may have shown validation for your hypothesis. It may have also provided incorrect information, or you may have misinterpreted it. On the technical side, whatever you have done to this point will not have validated that your software performs correctly at scale.
Instead of shipping instantly to one hundred percent of your users, do a progressive rollout. At Spotify, we had the benefit of a fairly massive scale. This scale allowed us to ship to 1%, 5%, 10%, 25%, 50%, and then 99% of our users (we usually held back 1% of our users as a control group for some time). We could do this rollout relatively quickly while maintaining statistical significance due to our size.
If you have a smaller user base, you can still do this with fewer steps and get much of the value.
At each stage of the rollout, we’d use the product analytics to see if we were validating our assumptions. Remember that we always tied the hypothesis back to product metrics. We’d also watch our systems to make sure that they were handling the load appropriately and didn’t have any other technical issues or bugs arising.
If the analytics showed that we weren’t improving the product, we had two decisions again. Should we iterate and try different permutations of the idea, or should we stop and remove the feature?
Usually, if we reached this point, we would iterate, keeping to the same percentage of users. If this feature MVP wasn’t adding to the product, it took away from it, so rolling out further would be a bad idea. This rollout process was another way to reduce the cost of failure. It reduced the percentage of users seeing a change that may negatively affect product metrics. Sometimes, iterating and testing with a subset of users would give us the necessary direction to move forward with a better version of the MVP. Occasionally, we would realize that the hypothesis was invalid. We would then remove the feature (which is just as hard to do as you imagine, but it was more comfortable with the data validating the decision).
If we removed the feature during the Ship It phase, we would have wasted time and money. We still would have wasted a lot less than if we’d released a lousy feature to our entire customer base.
The shaded area under this graph shows the investment to get a feature to customers. You earn nothing against the investment until the feature’s release to all your customers. Until that point, you are just spending. The Think It/Ship It/Build It/Tweak It framework aims to reduce that shaded area; to reduce the amount of investment before you start seeing a return.
You have now released the MVP for the feature to all your customers. The product metrics validate the hypothesis that it is improving the product. You are now ready for the next and final phase, Tweak It.
The MVP does not realize the full product vision, and the metrics may be positive but not to the level of your hypothesis. There is a lot more opportunity here!
The result of the Ship It phase represents a new baseline for the product and the feature. The real-world usage data, customer support, reviews, forums, and user research can now inform your next steps.
The Tweak It phase represents a series of smaller Think It/Build It/Ship It/Tweak It efforts. From now, your team iteratively improves the shipped version of the feature and establishes new, better baselines. These efforts will involve less and less of the team over time, and the investment will decrease correspondingly.
When iterating, occasionally, you reach a local maximum. Your tweaks will result in smaller and smaller improvements to the product. Once again, you have two choices: move on to the next feature or look for another substantial opportunity with the current feature.
The difficulty is recognizing that there may be a much bigger opportunity nearby. When you reach this decision point, it can be beneficial to try a big experiment. You may also choose to take a step back and look for an opportunity that might be orthogonal to the original vision but could provide a significant improvement.
You notice in the graph that the investment never reaches zero. This gap reveals the secret, hidden, fifth step of the framework.
Even if there is no active development on a feature, it doesn’t mean that there isn’t any investment into it. The feature still takes up space in the product. It consumes valuable real estate in the UI. Its code makes adding other features harder. Library or system updates break it. Users find bugs. Writers have to maintain documentation about the functionality.
The investment cost means that it is critical not to add features to a product that do not demonstrably improve it. There is no such thing as a zero-cost feature. Suppose new functionality adds nothing to the product in terms of incremental value to users. In that case, the company must invest in maintaining it. Features that bring slight improvements to core metrics may not be worth preserving, given the additional complexity they add.
Expect failure all the time
When you talk about failure in the context of software development from the year 2000 to now, there is a substantial difference. Back then, you worked hard to write robust software, but the hardware was expected to be reasonably reliable. When there was a hardware failure, the software’s fault tolerance was of incidental importance. You didn’t want to cause errors yourself, but if the platform was unstable, there wasn’t much you were expected to do about it.
Today we live in a world with public clouds and mobile platforms where the environment is entirely beyond our control. AWS taught us a lot about how to handle failure in systems. This blog post from Netflix about their move to AWS was pivotal to the industry’s adapting to the new world.
Netflix’s approach to system design has been so beneficial to the industry. We assume that everything can be on fire all the time. You could write perfect software, and the scheduler is going to come and kill it on mobile. AWS will kill your process, and your service will be moved from one pod to another with no warning. We now write our software expecting failure to happen at any time.
We’ve learned that writing big systems makes handling failure complicated, so micro-service architectures have become more prevalent. Why? Because they are significantly more fault-tolerant, and when they fail, they fail small. Products like Amazon, Netflix, or Spotify all have large numbers of services running. A customer doesn’t notice if one or more instances of the services fail. When a service fails in those environments, the service is responsible for a small part of the experience; the other systems assume that it can fail. There are things like caching to compensate for a system disappearing.
Netflix has its famous chaos monkey testing, which randomly kills services or even entire availability zones. These tests make sure that their systems fail well.
Having an architecture composed of smaller services that are assumed to fail means that there is near zero user impact when there is a problem. Failing well is critical for these services and their user experience.
Smaller services also make it possible to use progressive rollout, feature flags, dark loading, blue-green deploys, and canary instances, making it easier to build in a fail-safe way.
If innovation requires failure, to build an innovative product or company, how your culture handles the inevitable failures is key to creating a fail-safe environment.
Many companies still punish projects or features that do not succeed. The same companies then wonder why their employees are so risk-averse. Punishing failure can take many forms, both obvious and subtle. Punishment can mean firing the team or leader who created an unsuccessful release or project. Sanctions are often more subtle:
Moving resources away from innovative efforts that don’t yield immediate successes.
Allowing people to ridicule failed efforts.
Continuing to invest in the slow, steady, growth projects instead of the more innovative but risky efforts. Innovator’s dilemma is just the most well-known aspect of this.
Breeding innovation out
I spend several years working at a company whose leadership was constantly extorting the employees to be more innovative and take more risks. It created ever-new processes to encourage new products to come from within the organization. It was also a company that had always grown through acquisition. Every year, it would acquire new companies. At the start of the next year’s budget process, there would inevitably be the realization that the company had now grown too large. Nearly every year, there would be a layoff.
If you are a senior leader and need to trim ten percent of your organization, where would you look? In previous years, you likely had already eliminated your lowest performers. Should you reduce the funding of the products that bring in your revenue or kill the new products that are struggling to make their first profit? The answer is clear if your bonus and salary are dependent on hitting revenue targets.
Through the culture of the company, it communicated that taking risks was detrimental to a career. So the company lost its most entrepreneurial employees either through voluntary or involuntary attrition. Because it could not innovate within, innovation could only happen through acquisitions, perpetuating the cycle.
If failure is punished, and failure is necessary for innovation, then punishing failure, either overtly or subtly, means that you are dis-incentivizing innovation.
Don’t punish failure. Punish not learning from failure. Punish failing big when you could have failed small first. Better yet, don’t punish at all. Reward the failures that produce essential lessons for the company and that the team handles well. Reward risk-taking if you want to encourage innovation.
Each failure allows you to learn many things. Take the time to learn those lessons
Learning from failure
It can be hard to learn the lessons from failure. When you fail, your instinct is to move on, to sweep it under the rug. You don’t want to wallow in your mistakes. However, if you move on too quickly, you miss the chance to gather all the lessons, which will lead to more failure instead of the success you’re seeking.
Lessons from failure: Your process
Sometimes the failure was in your process. The following exchange is fictional, but I’ve heard something very much like it more than once in my career.
“What happened with this release? Customers are complaining that it is incredibly buggy.”
“Well, the test team was working on a different project, so they jumped into this one late. We didn’t want to delay the release, so we cut the time for testing short and didn’t catch those issues. We had test automation, and it caught the issue, but there have been a lot of false positives, so no one was watching the results.”
“Did we do a beta test for this release? An employee release?”
The above conversation indicates a problem with the software development process (and, for this specific example, a bit of a culture-of-quality problem). If you’ve ever had an exchange like the one above, what did you do to solve the underlying issues? If the answer is “not much,” you didn’t learn enough from the failure, and you likely had similar problems afterward.
Lessons from failure: your team
Sometimes your team is a significant factor in a failure. I don’t mean that the members of the group aren’t good at their jobs. Your team may be missing a skillset or have personality conflicts. Trust may be an issue within the team, and so people aren’t open with each other.
“The app is performing incredibly slowly. What is going on?”
“Well, we inherited this component that uses this data store, and no one on the team understands it. We’re learning it as we’re doing it, and it has become a performance problem.”
Suppose the above exchange happened in your team. In that case, you might make sure that the next time you decide to use (or inherit) a technology, you make sure that someone on the team knows it well, even if that means adding someone to the team.
Lessons from failure: your perception of your customers
A vein of failure, and a significant one in the lesson of Clippy, is having an incorrect mental model for your customer.
We all have myths about who our customers are. Why do I call them “myths”? The reason is that you can’t precisely read the minds of every one of your customers. At the beginning of a product’s life cycle, you may know each of your customers well when there are few of them. That condition, hopefully, will not last very long.
How do you build a model of your user? You do user research, talk to your customer service team, beta test, and read app reviews and tweets about your product. You read your product forums. You instrument your app and analyze user behavior.
We have many different ways of interacting with the subsets of our customers. Those interactions give us the feeling that we know what they want or who they are.
These interactions provide insights into your customers as an aggregate. They also fuel myths of who our customers are because they are a sampling of the whole. We can’t know all our customers, so we create personas in our minds or collectively for our team.
Suppose you have a great user research team, and you are very rigorous in your effort to understand your customers. You may be able to have in-depth knowledge about your users and their needs for your product. However, that knowledge and understanding will only be for a moment in time. Your product continues to evolve and change and hopefully add new users often. Your new customers come to your product because of the unique problems they can solve. Those problems are different from the existing users—your perception of your customers ages quickly. You are now building for who they were, not who they are.
Lessons from failure: your understanding of your product
You may think you understand your product; after all, you are the one who is building it! However, the product that your customers are using may be different from the product you are making.
You build your product to solve a problem. In your effort to solve that problem, you may also solve other problems for your customers that you didn’t anticipate. Your customers are delighted that they can solve this problem with your product. In their minds, this was a deliberate choice on your part.
Now you make a change that improves the original problem’s solution but breaks the unintended use case. Your customers are angry because you ruined their product!
Lessons from failure: yourself
Failure gives you a chance to learn more about yourself. Is there something you could do differently next time? Was there an external factor that is obvious in hindsight but could have been caught earlier if you approached things differently?
Our failures tend to be the hardest to dwell on. Our natural inclination is to find fault externally to console ourselves. It is worth taking some time to reflect on your performance. You will always find something that you can do that will help you the next time.
Collecting the lessons: Project Retrospectives
The best way that I have learned to extract the lessons is to do a project retrospective.
A project retrospective aims to understand what happened in the project from its inception to its conclusion. You are looking to understand each critical decision, what informed the decision, and its outcome.
In a project retrospective, you are looking for the things that went wrong, the things that went well, and the things that went well, but you could do better the next time. The output of the retrospective is neutral. It is not for establishing blame or awarding kudos. It exists to make sure you learn. For this reason, it is useful for both unsuccessful and highly successful projects.
A good practice for creating a great culture around failure is to make it the general custom to have a retrospective at the end of every project in your company. Having retrospectives only for the unsuccessful projects perpetuates a blame culture.
Since the project retrospectives are blameless, it is good to share them within your company. Create a project retrospective repository and publicize it.
The repository becomes a precious resource for everyone in your company. It shows what has worked and what has been challenging in your environment. It allows your teams to avoid making the mistakes of the past. We always want to be making new mistakes, not old ones!
The repository is also handy for new employees to teach them about how projects work in your company. Finally, it is also a resource for documenting product decisions.
The retrospective repository is a valuable place to capture the history of your products and your process.
Spotify’s failure-safe culture
I learned a lot about creating a failure safe culture when I worked at Spotify. Some of the great examples of this culture were:
One of the squads created a “Fail Wall” to capture the things they were learning. The squad didn’t hide the wall. It was on a whiteboard facing the hallway where everyone could see it.
This document is a report from one of the project retrospectives. You don’t need any special software for the record. For us, it was just a collection of Google docs in a shared folder.
One of the agile coaches created a slack channel for teams to share the lessons learned from failures with the whole company.
Spotify’s CTO posted an article encouraging everyone to celebrate the lessons that they learned from failure. Which inspired other posts like this:
If you look at the Spotify engineering blog, there are probably more posts about mistakes that we made than cool things we did in the years I worked there (2013-2016).
These kinds of posts are also valuable to the community. Often, when you are searching for something, it is because you are having a problem. We might have had the same issue. These posts are also very public expressions of the company culture.
Failure as a competitive advantage
We’re all going to fail. If my company can fail smart and fast, learning from our mistakes; while your company ignores the lessons from failure, my company will have a competitive advantage.
How we approach failure is critical in any industry, but it is especially crucial in building software.
The answer is simple: invention requires failure.
We don’t acknowledge that fact enough as an industry. Not broadly. It is something we should recognize and understand more. As technologists, we are continually looking for ways to transform existing businesses or build new products. We are an industry that grows on innovation and invention.
Real innovation is creating something uniquely new. If you can create something genuinely novel without failing a few times along the way, it probably isn’t very innovative. Albert Einstein expressed this as “Anyone who has never made a mistake has never tried anything new.”
Filmmaker Kevin Smith says, “failure is success training.” I like that sentiment. It frames failure as leading to success.
Failure teaches you the things you need to know to succeed. Stated more strongly: failure is a requirement for success.
Creating a fail-safe environment
To achieve success, what’s important isn’t how to avoid failure; it’s how to handle failure when it comes. The handling of failure makes the difference between eventual success and never succeeding. Creating conditions conducive to learning from failure means creating a fail-safe environment.
In the software industry, we define a fail-safe environment as setting up processes to avoid failure. Instead, we should ensure that when the inevitable failure happens, we handle it well and reduce its impact. We want to fail smart.
When I was at Spotify, a company that worked hard to create a fail-smart environment, we described this as “minimizing the blast radius.” This quote from Mikael Krantz, the head architect at Spotify during that time, sums up the idea nicely: “we want to be an internal combustion engine, not a fuel-air bomb. Many small, controlled explosions, propelling us in a generally ok direction, not a huge blast leveling half the city.”
So, let us plan for failure. Let’s embrace the mistakes that are going to come in the smartest way possible. We can use those failures to move us forward and make sure that they are small enough not to take out the company. I like the combustion engine analogy because it embraces that failure, well-handled, pushes us in the right direction. If we anticipate, we can course correct and continue to move forward.
One way you can create these small, controlled explosions is to fail fast. Find the fastest, most straightforward path to learning. Can you validate your idea quickly? Can you reduce the concept down so that you can get it in front of real people immediately and get feedback before investing in a bunch of work? Failing fast is one of the critical elements of the Lean Startup methodology.
A side benefit of small failures is that they are easier to understand. You can identify what happened and learn from it. With a big failure, you must unpack and dig in to know where things went wrong.
I worked at Microsoft when the company created Office Assistant. Although I didn’t work on that team, I knew a few people who did.
It is easy to think that the Office Assistant was a horrible idea created by a group of poor-performing developers and product people, but that couldn’t be farther from the truth. Extremely talented developers, product leads, researchers with fantastic track records, and PhDs from top-tier universities built Clippy. People who thought they understood the market and their users. These world-class people were working on one of (if not THE) most successful software products of all-time at the apex of its popularity. Microsoft spent millions of dollars and multiple person-years on the development of Clippy.
So, what happened?
What happened is that those brilliant people were wrong. Very wrong, as all of us are from time to time. How could they have found their mistake before releasing widely? It wasn’t easy at the time to test product assumptions. It was much harder to validate hypotheses about users and their needs.
How we used to release software
Way back before we could assume high-bandwidth internet connections, we wrote and shipped software in a very different way.
Software products were manufactured, transcribed onto plastic and foil discs. For a release like Microsoft Office, those discs were manufactured in countries worldwide, put into boxes, then put onto trucks and trains and shipped to warehouses, like TV sets. From there, trucks would take them to stores where people would purchase them in person, take them home and spend an afternoon swapping the discs in and out of their computers, installing the software.
With a release like Office, Microsoft would need massive disc pressing capability. It required dozens of CD/DVD plants across the world to work simultaneously. That capability had to be booked years in advance. Microsoft would pay massive sums of money to take over the entire CD/DVD pressing industry essentially. This monopolization of disc manufacturing required a fixed duration. Moving or growing that window was monstrously expensive.
It was challenging to validate a new feature in that atmosphere, peculiarly if that feature was a significant part of a release that you didn’t want to leak to the press.
That was then; this is now.
Today, the world is very different. There is no excuse for not validating your ideas.
You can now deploy your website every time you hit save in your editor. You can ship your mobile app multiple times per week. You can try ideas almost as fast as you can think of them. You can try and fail and learn from the failure and make your product better continuously.
Thomas J Watson, the CEO of IBM from 1914 until 1956, said, “If you want to increase your success rate, double your failure rate.” If it takes you years and millions of dollars to fail and you want to double that, your company will not survive to see the eventual success. Failing Fast minimizes the impact of your failure by reducing the cost and delay in learning.
I worked at an IBM research lab a long time ago. I was a developer on a project building early versions of synchronized streaming media. After over a year of effort, we arranged to publish our work. As we prepared, we learned there were two other labs at IBM working on the same problems. We were done, it was too late to collaborate. At the time, it seemed to me like big-company stupidity, not realizing that three different teams were working on the same thing. Later I realized that this was a deliberate choice. It was how IBM failed fast. Since it took too long to fail serially, IBM had become good at failing in parallel.
One of my family’s quarantine projects is re-assembling all my daughter’s old Lego sets. The pieces from the sets are in several large storage totes, mixed at random from years of building and taking things apart. As I was digging through a box today looking for some specific piece, I started noticing the system I had started to use.
As I looked for a piece, I would start to collect identical pieces and join them up. Joining pieces allows me later to find those pieces later more efficiently, even if I put them back into the box. It also reduced the number of pieces I would have to sort through to find anything. I do this unconsciously because I have done this ever since I was a kid.
Today I realized that this was a perfect metaphor for paying down technical debt.
Grouping the Legos as you are building means that you take a little bit longer on the sets you make at the beginning, but each successive set gets faster. Not only are there fewer Legos to sort through, but the Legos that are there are becoming more and more organized.
When working in a code base that has accumulated a lot of technical or architectural debt, cleaning things up as you go means that your velocity increases over time. Ignoring technical debt is like adding a few random Legos to the box as you take pieces out. Not only does it not get simpler or faster. It gets slower. Eventually, you have to go to the store to buy a new set because it is just easier than finding the pieces for the old one. Or worse, you have to go to eBay and pay twice as much for the same set because Lego stopped manufacturing it. (I am probably abusing the metaphor here.)
I’ve also been thinking about the difference between building a set by pulling out Legos from a big box versus building a brand-new set.
When you build a new set, the pieces come in smaller bags. Lego numbers the bags, so you only need to open one at a time to find the parts you need. Bigger sets may have multiple instruction books, also ordered by number.
The grouping of Lego pieces into bags is a metaphor for Agile software development.
By narrowing the scope and limiting the options, you make the work go faster, even when the problem is involved (like one of their expert models).
The next time you are trying to explain to your product manager (or anyone) why you need to add more tech-debt stories into the backlog even though it means a feature will take longer to deliver, bring in a big box of Legos as a teaching tool. If it doesn’t work, you’ll at least have a fun team meeting…
The great thing about being in technology is that it is a growing field. There are tons of jobs, and companies are always complaining about how hard it is to find talent.
That is until it isn’t.
It isn’t clear what will happen during this pandemic, but the layoffs and hiring slowdowns or pauses have started.
Many developers have never known a time when companies were not clamoring for their services and bidding against each other.
Having my startup go bust in the dot-com bubble bursting of 2000-2001 and having seen what employment options were like in 2008-2009, I thought I could give some advice to those of you who may be in brand new territory.
If you find yourself unemployed during a bust, you will need to change your tactics. You may be used to an employee’s market, but you are entering an employer’s market. Companies will suddenly find many options for their roles—very qualified, even overqualified, folks who are willing to take a lower salary for their positions. Jobs you wouldn’t have considered previously are no longer returning your e-mails. They can find people better than you cheaper.
Even if you currently have a good job, the security of that role may not be what you expected. Be prepared.
Save your money
If you live somewhere expensive, the rent and other prices are likely to go down more slowly than salaries. You need to start cutting your expenses and build up a cash reserve.
Hopefully, you already have a few months of money put aside somewhere other than the stock market. In a crash, selling your investments in the dip is the last resort. If you don’t have that cash reserve, start building it now.
Stop unnecessary purchases: buy groceries instead of door-dashing meals, take public transportation instead of Lyft, do your laundry instead of sending it out. This is the way that most people live.
You will be surprised how much money you can save by making a few changes. Once you have your reserve (enough to live on with a reduced, reasonable, lifestyle for a few months), you can go back to your spending patterns, or continue to build your savings (which is smarter). During the previous busts, very good, very senior developers ended up unemployed for many months.
I left a good job at Microsoft to join a startup in 1999. That startup crashed a few months later. After trying to help the leadership revive it for a few months, I joined a friend in building a new startup. We had some angel money, but I was paying the rent and some of the other bills myself sometimes. We were ready to go out for our first real round of funding in September of 2001.
After 9/11, what was left of the investment and job market completely fell apart. We could not raise money. After a few months of trying to keep things going, I needed to start earning a paycheck again. I was selling my stock to pay the bills, and those shares were now worth half of it they had been before.
I assumed it would be easy to find a job. It always had been.
Jobs were suddenly scarce.
I found myself applying blindly to companies that I would never have considered before. Few replied.
Weeks turned into months. I started to lose faith. Occasionally, I would get an actual interview. But by then, the fire had gone out in my eyes. I was passed over for roles that required way less experience than I had. I had lost my confidence, and it showed in interviews.
Eventually, through a friend, I was able to land a contracting job, back at Microsoft. It was a bit humiliating, but I was happy to be working and earning again.
I later found out that I was one of the last people to get that level of contract. The contracting company realized that it could hire people of my seniority at lower contract rates.
Working again gave me my confidence back. I did well and got converted back into a full-time role. When I did, though, my manager negotiated my salary down. I had to take a serious pay cut. I had no choice, and he knew it. I took the job.
Now, as an employee again, I was on interview loops. I spoke to many good developers who were in the same situation I had just left. They had lost their confidence, just like I had. Their answers to questions were non-committal. They were tentative. They were so used to being rejected that it made it hard to approve them.
If you find yourself in that situation, you need to do your absolute utmost to project a positive aspect and some self-confidence in your discussions with recruiters and employers. Even if you have to fake it. Displaying confidence will make a substantial difference in how you interview.
Some money is better than none
If you get an offer, it may be well for a lot less than you expect. You may need to take it anyway. Some money coming in is better than none. The market will rebound eventually, and salaries will go up again. When that happens, you will be able to find a new role that will pay you appropriately. I left Microsoft for a second time when the market rebounded, and I got an offer for more than my old salary from somewhere else.
If the offer is much, much too low, take it. Keep looking for another role, but now with some security. No one says you have to put every position on your resume.
What you want to do and what you can do
Are you a developer, but a company is open to giving you a job as a tester or program manager? Are you an engineering manager, but a company is interested in talking to you about a product manager role?
If you are finding it hard to find a job doing what you want to do, it may be time for a temporary (or permanent) career change.
I wouldn’t automatically recommend this, though. If you switch into another role, when the market opens up, you may find it hard to go back to your preferred job. The market now sees you as a product manager or tester and may not consider you for a development role (especially if you are in the new position for a while). You may find that you enjoy the new job and want to pursue a new career, which could be a benefit. This situation happened to a few friends of mine during the 2008 crash.
If you decide to take the career-switching role out of necessity, make sure you keep your development chops up: contribute to open source, build apps or sites, whatever keeps your skills up-to-date.
You may be tempted to take a role outside the industry. This should always be the absolute last resort. Even with a strong resume, you may find it hard to get back in if you are in a very different field for a few years.
The sad fact for those who struggle during these periods is that most folks in the field will keep their jobs. When their companies start hiring again, these people will have a strong survivor’s bias. They may not understand the choices you had to make.
Build (and maintain) your network
Keeping up with your friends in the industry is probably going to be your best bet at finding a new role. Your friends can get you past the gatekeepers at the companies and make sure you are seen. They also may have more insight into what roles are open.
At first, it may be difficult to reach out to them. You may be embarrassed about the situation you are in. Get over it.
If you have been in the same company for a long time, or don’t have a big network in your area, start attending meetups or local conferences. Meet developers at other companies. You are likely to meet a lot of other folks in your situation, this is ok. You can form a group to share tips about companies hiring, maybe they may know someone at a company with a role appropriate for you.
Stay in your safe harbor
If you have a good job, with a good salary, keep it. Build your savings because even good companies don’t always survive. If you were considering that it might be time to move on and find a new role, don’t.
Even if you have an offer in hand, when companies hit a rough patch, after freezing hiring, they start rescinding offers. A friend of mine left his steady job during the bust to join another established company. He quit his old job and then the weekend before he started his new one, his offer was rescinded. He was now unemployed. His former company didn’t want him back. They got someone more senior for his role at the same salary.
If you have been privileged to not know a time when you were worried about money, this will be scary. Hopefully, you find a new job, and this time will be brief. Many people in the world are not that lucky. They live constantly worrying about how they will pay their rent, or feed their families. When you are on the other side of this, realize how lucky you are and take your new perspective to be a better, more empathetic person. Do your best to help those in need, now that you have an understanding of what their reality is.
It will get better
Economies are cyclical. After the dot-com bust, there was exceptional growth in the tech industry. After the 2008 contraction, the tech sector went back into massive growth mode. If you lose your job and find it hard to get a new one, don’t despair. Things will get better. Until then, just focus on doing what you need to until that happens. Learn from this experience and be ready for the next contraction, because there will be one. Always.
If you don’t lose your job and you are in a position to make hiring decisions, try to be human. If you interview someone who seems to have lost all hope, try to see the person underneath who has had some bad luck. If you were lucky enough not to lose your job, don’t think you are better than those who did. Ask about their choices. Don’t assume that if someone went from being a developer to doing another job that it was a deliberate choice. Understand their story before making a decision.
These are amazingly challenging times for many people. Remember that if you lose your job during this downturn, even if things seem very bleak, you are probably still better off than 95% of the world’s population. If you want to grow your skills while looking for a job, you could build a website for a charity. That will leverage your skills, build your confidence, and do something for others too.
This article is meant to present how we organize our work at AstrumU, a startup based in the US in Seattle with remote offices for sales and some of the development team. Our development team has grown significantly over the last six months. The company is about a year old.
Our product is composed of multiple web-based front-end applications backed by a steadily increasing number of microservices.
I’m going to state some of my biases up-front. I firmly ascribe to the dictum that you don’t let your tools dictate your process. I am also a firm believer in using a physical board for a team to organize work.
In my experience, a physical board is only useful with a co-located team. I have managed distributed agile teams for many years. I have not found a way to avoid using a digital board when the team doesn’t physically sit together.
I have used many of the agile digital tools over the years. I am not a strong advocate for any particular one. When I joined AstrumU, we were already using Jira, and I saw no reason to switch to a different tool. There are some workflows that we’ve built in Jira that might be useful for other teams, so I include them here.
Jira is such a catch-all tool that its’ complexity makes it difficult for teams to adopt. I’m hoping that our workflows might show other teams some useful things that Jira can do.
Our Agile Process
At AstrumU we use a simple Kanban process, with some of the ceremonies from Scrum. Some call this “Scrumban.”
We have a daily standup at 9 am in the US. On Mondays, we follow the stand-up with a retrospective or a planning meeting (alternating weeks). The retrospectives and standup are similar to the traditional Scrum ceremonies so I won’t describe them here. Our planning meeting is different. I describe it later in a later section.
Projects and How We Use Them
Given that we build multiple products and a variety of independent supporting services, it makes sense not to have one project for all of our development or one project per team to track work. Instead, we use multiple projects that are each specific to a product or supporting service. We currently have 14 different projects in Jira covering everything from an application front-end to our cross-service security work.
We try to keep the projects clear enough that it is obvious where a work-item should go, but it can still be sometimes confusing to identify the right project for a new task. We’re working to make this more apparent.
A benefit of this structure is that we can leverage pull requests in our GitHub projects to transition stories to the “done” and “released” states automatically as the code moves from a feature branch to the develop branch to the main line.
To track the overall work of the team, we have a master AstrumU project that consolidates and tracks all the work in the other projects. The AstrumU project is the only one in Jira that has a Kanban view and is the single source of truth for prioritization and work-in-progress. This project is also where work items that span multiple projects, such as infrastructure or general documentation, are added.
Taxonomy of our work items
We have four different packages of work: Epics, Stories, Tasks, and Bugs. We use the Jira built-in types for these. Each has a different use, scope, and meaning in our workflow.
The product team works with the development and data teams to create Jira Epics in the AstrumU project to capture substantial efforts such as an MVP of a new feature. Epics are sized to be a reasonable amount of work to complete within a few weeks or less of dedicated development time (i.e., “Students Can Register On the Site” not “Build the Student App MVP”). The Epic ticket includes as much context as possible: links to user research, UI designs, and product concept documents, for example. The Epic also includes acceptance criteria (a.k.a. the definition of done).
The development and data teams break the Epics down into Stories to track the implementation and design work. We size Stories for completion within a day or two at most (i.e., “Route <url> to new service in Traefik” not “Create the Student API service”). The work for a story includes time to write tests and validate that the code works. The work also includes things like adding telemetry or monitoring as appropriate. The majority of Stories link to an Epic, but that is not a requirement. Stories without Epics tend to be one-off maintenance efforts, small incremental improvements to a feature or refactoring or other technical debt.
Stories should always contain acceptance and enough context that any developer who picks up the task has all the information needed to complete the work. The context is especially critical because of the distributed nature of the team. We have found problems when a story is missing this information because a developer cannot complete it without having to wait for standup to get the missing context. We have also had stories done incorrectly because of missing context.
If, as part of defining or working on a Story, we come across a small separable effort for someone else to take over and work on independently, we create a Task and link it to the Story.
Scoping for a Task is in the range of a few hours. If a Task is enough work to be a day or longer, it should be a Story instead. If the Task is simple one-off maintenance or a hygiene effort, we do not link it to a Story or Epic.
Bugs are indicators of something broken in existing code. Bugs are not used to specify new or incremental feature work. Refactoring code or reorganizing repositories is not bug work. A page rendering incorrectly or an API failing are examples of bugs.
We file Bugs against the most appropriate Jira project for the issue. We encourage anyone in the company to file a bug when they find it. For folks who don’t know what Project to file the bug against, they file it against the AstrumU project, and it is moved to the appropriate project later.
Organizing and Tracking the Work
We have two Kanban Boards in the AstrumU project. One tracks Epics only and the other tracks Stories, Tasks, and Bugs.
The Epic Kanban Board
Since our epics track the significant efforts in progress, the Epic board is an evident view of the work that the teams are doing and what is next. For those who do not need to follow the details of the work, this is an excellent view-at-a glance of the state of the world. If you want more detail about an Epic, you can open the Epic to see what work is complete and what work remains.
Our Kanban board for Epics has three columns: To Do, In Progress, and Done. The simplicity of the columns makes sense for an Epic workflow where the primary goal is transparency and managing the amount of work in progress. We also maintain a backlog for Epics.
The ordering of Epics in the columns denotes priority, but there is no strict enforcement of having the stories in the other board match the epic prioritization exactly. If there is a significant disparity, that in itself would signal some potential issues in our process.
An Epic moves from the To Do to the In Progress column when it is ready for work by the whole team if there are people available to work on it. There may be some Stories that we start on in Epics that are still in the To Do column. That is almost always cards for the UX or Product Managers to prepare the Epic for the rest of the team.
Epics move from In Progress to Done when all the stories, tasks, and bugs attached to the Epic are complete, and the Product, UX, and Engineers sign off on the Acceptance Criteria (this is very informal).
Epics move from the Done column off of the board after the bi-weekly planning meeting (described later) if all code elements from the Epic are now running in Production.
The Detail Board
The board that the development and UX teams most interact with is the Story/Task/Bug Kanban board. This board contains five columns: To Do, Blocked, In Progress, In Review and Done. Without context, this board can look very chaotic with all the stories from different teams, different projects and different epics. In reality, the team likes it because it shows very clearly what things are complete, what is in progress, and what is next.
Most of the time we have a single swim lane, but when we have any time-critical cards, we use a separate Expedite swim lane to track them.
The cards’ position in the column denotes priority. Developers are expected to take their next work item from as near the top of the To Do column as they can. Because each card that is part of an Epic has the title and color of that Epic on the card, it is straightforward to see if the prioritization of the cards aligns with the priorities of the Epics.
Cards move from To Do to In Progress when a developer is free. We do not let a single developer have more than one card In Progress. When a developer starts work on a card, if they realize that the scope of the work is too big for a story, they break down the card into smaller stories and tasks. They can keep moving forward on their work. We discuss the breakdown in the next day’s standup. If the team agrees on the new stories and tasks, those cards get prioritized in the To Do column.
If a Developer is working on a card and finds that a dependency on another card is blocking their work, they link the two cards and then move the blocked card to the Blocked column. A card only moves to the Blocked column if the blocking dependency is In Progress. If the dependency is the To Do column, then the developer adds comments about what they have done, they push their in-progress branch to Github and then put the card back in To Do and start on something else.
Once the work for a card is done and tested locally, the developer submits a Pull Request for their feature branch and moves the card to the In Review column. Each code change requires two other developers to review and approve the change. When the Pull Request merges into the Development branch, the card moves from In Review to the Done column automatically.
When a release is created in GitHub as part of our semantic versioning scheme and the code moves from our Development cluster to our Production cluster, there is a parallel release done in Jira, and the stories move off of the Kanban board.
I have been considering doing a separate column for UX Review on the Kanban board and may add that in future.
The Planning Meeting
Every other week after the Monday standup meeting we have our planning meeting. The agenda of the meeting is: Review the completed Epics from the last two weeks; review the Epics that are currently in progress and review any new epics that may be moved from To Do to In Progress in the next two weeks.
For the In Progress Epics, we discuss the work remaining with an eye towards making sure the remaining stories satisfy the acceptance criteria of the Epic. If not, we may need to add additional stories.
For the upcoming Epics, we discuss the product, UX and business context of the Epic so that the teams understand why we are working on this Epic next and why it is relevant to our business. We make sure the acceptance criteria is understood. For feature work, the Product Manager and UX designer discuss the rationale behind the epic and the initial UX designs. The development team then reviews the cards associated with the Epic to make sure they are correct and make suggestions of things to change to keep the work in scope.
Initially, we tried generating the stories as part of the Planning meeting, but that proved too cumbersome. The stories and tasks are now generated beforehand by the engineering leadership. Generating the initial stories in this way is a temporary solution. Ideally, the team should generate the stories and tasks themselves.
Our current process is the result of iteration and continuous improvement. There are still some challenges to resolve.
One of the biggest challenges we need to resolve has to do with the time difference between the Seattle and remote teams. While a good backlog and prioritization in any agile process require ongoing grooming, having the teams working hours off of each other means that there is a lot of daily grooming work. Especially since new cards are being added every day by developers breaking down stories, Product Managers adding incremental changes, or bugs coming in. If there are a couple of days without dedicated effort on the backlog, developers can find themselves not being sure what to work on next. Our best solution to the problem so far is to empower the lead developer, who is remote, to be able to update the board to unblock developers there as needed.
Another challenge is making sure that all teams are using the process consistently. We’ve had a few issues with the shared board contained too many stories in the In Progress or To Do columns because one team wasn’t using the same criteria as the rest of the organization. Doing some internal documentation and training has mostly addressed this problem.
Our most frequent issue is that the Done column on the detailed Kanban board can get full when we are working on a new feature or service. Since creating and deploying a new release is what moves the stories off the board, the Done column can get long at times. Having an overly-full column on the Kanban board makes it harder to understand the current state of the world (also in Jira it can mean much scrolling). We’re working to get our feature-flagging architecture going which will allow us to release new functionality before exposing it to our customers. As a side effect, this will help us clear out this column more frequently.
The last challenge is that there is still manual work to create releases for the projects that aren’t directly tied to a repository to move the cards from those projects off of the Kanban board. Since there are not usually very many of these stories, a periodic manual release for each of these projects takes care of the issue. Eventually, we will automate this process.
At AstrumU, we are using a simple Kanban agile process along with some of the Scrum ceremonies to help us organize, prioritize and track our work. We continue to iterate upon this process, but in its current state, it does a good job keeping the distributed product development and data teams informed and coordinated while making priorities, plans and completed work transparent to the rest of the company.
I want to give credit to Fedya Skitsko who developed a lot of the early Kanban process and Jira structure that is the basis of our current process and structure.
I was given a set of questions from a consultant working with a company about to begin a transformation to Agile. They asked if I record my answers for their kick-off meeting. That video is above, but I had written my thoughts down as well for clarity, so I am including that text as well.
How hard it can be to implement an agile model in a company where the old model was more hierarchical and conservative?
It can be extremely challenging if only part of the organization is interested in making the change. If the rest of the company are expecting detailed plans and delivery date commitments and the product development team is working with a more iterative approach, that will create a lot of organizational friction. For any agile transformation to be successful, the whole company has to be supportive and committed.
I don’t think that company hierarchy is necessarily an impediment to a successful agile transformation. As long as the responsibilities and expectations of leadership adapt to the new way of working and that leadership is also committed to the agile transformation. Many organizations with more traditional hierarchies build their products successfully with agile methodologies.
What would be your advice for this team to successfully implement the model? What should they be aware of? Basically, the DOs and DONTs.
Do commit to making the transformation, and understand that it won’t be easy. This will be a culture change for your company. Any culture change follows a path where the excitement of making the change is followed by a period where the individuals and teams struggle to understand how to be productive in the new model. During this time (sometimes called the valley of despair), it seems like the best idea would be to go back to the way things used to be. Push through this time and don’t give up. Bit by bit, things will improve, people will figure out how to operate in the new world and you will end up in a much better place.
One of the ways that teams make the transition to agile is to use a known structured methodology like Scrum. At first, the processes and ceremonies will feel strange and not what you understood agile was supposed to be like. Stick with it. As your teams get better at agile thinking, you can start to decide which elements make sense for you and which you may want to change or drop altogether. Each of these things has a purpose, and understanding the purpose and the value when it works well is important before you decide not to do it. Teams that abandon the parts of the process that they don’t like early on often end up with a very poor understanding of agile. They gain very few of the benefits and may be a lot less efficient.
What are the foundational measures they should follow in your opinion?
Like any organizational culture transformation, there should be some time spent by the whole organization understanding why there is a need to make the change, what the expected outcome from the change is and what the plan is. Time should be spent to make sure that all parts of the organization (especially the teams dependent on the team making the change) are committed.
If there is a smaller team that is mostly independent, that team might try to pilot the switch to agile first, to develop some expertise ahead of the rest of the organization and learn from their experience.
What should they anticipate to succeed?
Anticipate that this may be a longer process than they expected, but the effort is worth it! Anticipate that the change may too big for some people to make, and they may choose to leave or try to prevent the change from happening. Anticipate that it will get progressively easier over time.
Other relevant points you might find useful.
I have been working Agile exclusively for almost 20 years after having spent the first 8 years working in a more traditional way. The reason that I have continued to work agile is that I have seen no better way to deliver software efficiently. I am inherently pragmatic. If I saw a better way to work, I would switch immediately. I haven’t found any yet.
The hardest part of adopting agile is learning the agile mindset and understanding that it doesn’t mean abandoning quality, accountability, documentation, process, planning or tracking to deliverables. It is about finding the right amount of each of those things for the project and no more.
In the end adopting agile is adopting a culture of continuous improvement. A culture of always looking for better ways of doing what you are doing. The way we practice agile today is very different from the way that we did it five years ago. Its adaptability is part of its strengths. It’s fluidity also makes it very difficult to learn. It is absolutely worth the effort though.
Compare the Market was nice enough to invite me to speak at their tech managers’ off-site about distributed teams. This talk reflects my own experience leading distributed teams.
I was presenting to them over video. Their meeting included people in two different offices and also folks dialing in from home. Ironically, in the middle of my talk, I got disconnected from the video conference. Because I was sharing my slides full-screen and had my speaker notes on my second monitor, I didn’t notice. So I spoke to myself for about 15 minutes before I realized what happened and dialed back into the meeting. It was a bit mortifying, but the folks in the UK were extremely nice about it. I can’t think of a better example though of the challenges around working with teams who have to communicate over electronic means constantly, so it was a good illustration of the issues I raised. 🙂