When, why, and how to stop coding as your day job

By letting go of writing code, you open yourself up to excelling as a manager.

I am a computer programmer.

I was one of those people who started coding at a young age – in my case, on a TRS-80 Model 1 in my school’s library. I loved the feeling of teaching the computer to do something and then getting to enjoy the results of interacting with what I built. Since I didn’t own a computer, I would fill spiral-bound notebooks with programs that I would write at home. As soon as I could get time on the computer, I would type it in line-by-line. When I learned that I could write software as a job, I couldn’t imagine anything else that I would want to do.

After university, I got my dream job writing 3D graphics code. I was a software engineer! I defined a successful day by the amount of code I wrote, the compiler issues I resolved, and the bugs I closed. There were obvious, objective metrics that I could use to measure my work. Those metrics and my job defined me.

Today, I am a Chief Technology Officer, leading software development organizations. If I am writing code on the product, it is probably a bad thing. I now have to define my success by much fuzzier metrics: building good teams, hiring and training good people, setting multi-year technical strategy and vision for the company, collaborating with other departments, and setting and managing a budget. I may have a good day or a bad day, but I have to measure my success based on quarters or years.

My achievements are now always tied to the successes of others. Getting to this point wasn’t easy, but I wouldn’t have it any other way. It was a journey that took years, and the first challenge was understanding that coding was no longer my job.

Why is it hard to stop coding as our day-to-day work?

When I speak to engineering leads or managers working to grow into more senior engineering leadership levels, the question of ‘How much do you code?’ is very often raised. We usually have a hard time imagining that we can still be useful if we don’t code for a significant part of our time. Why is that?

We’ve been traditionally bad at hiring managers in the software engineering industry

Usually, companies choose development leads because they are the best, technically, on the team. I would guess that the reasoning behind this is that it’s assumed that the best developers are the right people to supervise their peers. This practice creates the impression that managing others is a promotion for a skilled developer when, in actuality, it is a career change away from what made them successful in the first place.

The worst managers I’ve had were very talented developers who hated having to spend time doing the boring stuff that wasn’t coding. They resented the time spent away from the keyboard and weren’t always good at hiding that fact.

Many companies now feature dual career tracks for technologists, giving them a choice to advance as an individual contributor or move into management. This choice of career is an excellent thing. It means that if you want to spend your days coding, you can do that without sacrificing your career. It also means that if you desire to find joy in leading teams and growing others’ development and skills, you can do that.

We fear becoming ‘non-technical’

We joined the technology industry to be close to technology. We fear that by moving away from coding, we will morph into the classic ‘pointy-haired boss’ – ridiculed by the people on our team and unable to understand what the developers are discussing. I won’t say this can’t happen, but it won’t happen on its own. It will only happen if you choose to avoid technology once you move into the management role.

As you take on broader leadership responsibilities, you will need to learn and understand new technologies. Moving beyond the specifics of your expertise is necessary for you to move up in management. I have managed developers coding in at least a dozen languages on the backend, frontend, mobile, operating systems, and native applications. I have also managed testers, data scientists, data engineers, DevOps, Security, designers, data analysts, program managers, product managers, corporate IT teams, and some other roles I don’t even remember anymore. It isn’t possible to be an expert in all those fields. I need to take the lessons from my time as a developer and use them to inform my understanding, help me learn new areas, and give me empathy for the people who work for me.

It isn’t that you will become non-technical. It is that you will become less narrowly technical.

As a new manager, we are often expected to continue coding

It is common to move from being a developer on a team to managing that team. As the new manager, this means you are still responsible for part of the codebase. Unless you immediately start leading a large group, your new role still requires that you spend a significant portion of your time coding. This expectation makes the transition to the new role more comfortable – but it can also be an anchor that holds you back from embracing your new role as your management responsibilities grow.

We still see ourselves as a resource that can ‘save’ a deliverable

As a manager, you are accountable for the results of your team. If the group is struggling to make a deadline, it might be tempting to jump into the weeds to try and help the team finish the project on time. While this is sometimes the right decision, it can also make the problems worse because the team loses the person who looks at the more significant issues and coordinates with other teams to get more help or prepare them for the delay.

Why do we need to stop coding eventually?

We don’t need to stop coding, ever. However, once you move into engineering leadership, it will need to become a smaller and smaller part of your job if you are working to lead larger teams or broaden your responsibilities scope.

I had led teams before I was a manager at Adobe, and I had always spent a significant part of my work week contributing code as part of the groups I was in. At Adobe, though, my team had grown to be fourteen people, with another four dotted-lined to me.

I had been the primary developer for a part of the project, and I took pride that I was still contributing important features to every release. However, my management responsibilities were starting to fill my work weeks. Between 1:1s, sync meetings with other teams, and other manager work, my feature development time was increasingly moving into my evenings and weekends. My features were often the last to be merged and usually late.

The company had two mandatory shut-down weeks. To work during this time, you needed the prior approval of a Vice President. The team was preparing for a release, and my features were still in the to-do column; I met with my VP to get his permission to work over the shut-down week. He asked me, ‘Who is the worst developer on your team?’ I hemmed and hawed – I didn’t want to call out anyone on my team, and I hadn’t even really considered the question. Seeing my uncertainty, he answered for me. ‘You are! You’re always late with your features. The rest of the team is always waiting on you. If you were a developer instead of the manager, you would be on a performance improvement plan.’ He was right. My insistence on coding was hurting the team, not helping it.

Taking on the lead role doesn’t mean you should stop coding immediately, but it does mean that your coding responsibilities should now be secondary to your leadership ones. There are other developers on your team, but there aren’t other leads. If you aren’t doing your lead job, no one else will. Similarly, your professional development’s primary focus should now be on your leadership skills, not your coding skills. You are moving into a new career, and if you don’t work to get better at it, you will find yourself stuck.

As your leadership responsibilities increase, you should transition your development responsibilities to other people on the team. This transition is good practice because delegation is an essential part of leadership.

How do you stay ‘technical’ when coding isn’t your job anymore?

As I said earlier, staying technical is a choice that you need to make. Hopefully, one of the primary reasons you chose to make a career in the technology industry was that you were interested in it, so this shouldn’t be a problem.

As I also said earlier, as you develop as a technology leader, your focus broadens as your scope widens. 

The best way that I have found to remain a credible technologist for my teams is to be interested in them and their work. To do this, talk to the people on your team and take a genuine interest in the things they are working on. If a technology comes up in a meeting or 1:1 that you don’t know, add it to a list of things to research later. Then, dedicate time in your week to go through that list and learn about the technologies well enough to have your own opinions about them. This practice allows you to have further discussions with whoever mentioned the technology to you.

If you get interested in what you learn about the new technology, you may want to keep trying to understand it better; you may read more or embark on a personal project using it to gain more practical knowledge. As I said, it isn’t that you have to stop coding, it is that, eventually, it shouldn’t be your day job anymore.

By taking an interest in the technologies your team uses in their work, you deepen your empathy for them and expand your own knowledge. You’ll be able to discuss the work, ask reasonable questions, and make connections to other things happening in the organization and your own experience. This way, the people on your team know that while you may not be able to step in for them, you understand their work and care about it.

Success is defined differently when you lead people

The feeling of accomplishment that comes from completing a cool user story, deploying a new service, or fixing a difficult bug is significant. It is a dopamine hit, and just like other dopamine-inducing behaviors, it can be hard to stop.

Having a great 1:1 or leading a productive team meeting can also feel good but in a more esoteric way. As a team leader, you need to learn to perceive the success of making others successful. Success takes longer, but the feeling is more profound and more rewarding.

Having a release resonate with your customers, being able to easily justify the promotion of a developer that you have mentored, and having someone accept a job offer for your team, are all fantastic feelings. In the day-to-day, watching stories get completed, helping resolve the issues when they aren’t, and seeing people get excited about the direction you’re setting for the team can leave you feeling satisfied at the end of the day.

Being a technical leader doesn’t mean writing code every day

As you grow in your new leadership career, you will need to devote your time to mentoring, developing, and leading your team. As you spend less time in your code editor, you will find new challenges in strategy, clearing roadblocks, fixing broken processes, and new tools like HR information systems, slides, and spreadsheets (it isn’t as bad as it sounds). You will spend less time learning all the intricacies of a specific language or toolchain and instead learn about how systems interact, understand when to build vs. buy, and learn about entirely new areas of technology. And you can still code, but make sure that you aren’t the developer holding your team back.

[This was originally posted at https://leaddev.com/skills-new-managers/when-why-and-how-stop-coding-your-day-job]

Twenty Questions for your 1:1s

You sit down for a chat with someone on your team. You get through the pleasantries, the small-talk, the status, and you run out of things to say. It happens to even the most curious and high-EQ people. It happens to me, and I have probably had 10,000 one-on-one meetings in my career.

When you hit that moment where neither of you has anything to say, it is very tempting to just say, “Ok then! Let’s talk next week.” giving you both time back into your day. If this happens occasionally, it is not a severe problem. However, if you find it happening more often than you would like, it is good to keep some prompts handy to move into deeper topics that might prompt a valuable conversation.

Here are twenty questions that you might find helpful if you get stuck at the surface level or run out of things to discuss in your one-on-ones:

  1. What is more challenging in your day-to-day work than it should be?
  2. What is the most fulfilling thing that has happened this week?
  3. Who on the team has been really impressing you lately?
  4. What are you looking forward to in the next six months? Why?
  5. What haven’t you told me that I probably should know?
  6. What is one thing that you miss from your last job/team?
  7. Who on the team are you most worried about?
  8. If there is one thing I could change about your role today, what would it be?
  9. Who on another team do you most enjoy working with?
  10. What do you wonder about?
  11. How would you change the company’s strategy?
  12. What is the product or feature that we should build next?
  13. When do you feel the most satisfied at work?
  14. What is your least favorite part of your job?
  15. What percentage of your work time do you think you are in a flow state?
  16. What is the one meeting that you would add to your calendar?
  17. What is the one meeting that you would remove from your calendar?
  18. What do you wish that I did differently?
  19. What should I keep doing?
  20. When am I the most helpful?

If you don’t like any of these questions, you can create your own list in a few minutes. You are looking for a question that gets the other person to think a bit, share a bit more with you, and hopefully give you an avenue for deeper discussion.

Have other questions that you like to use? Please add them in the comments!

Building a vernacular with your engineering team

Teams consist of people. People communicate via a common language. The base unit of most languages is words.

The impact of language

Whether written or spoken, words are essential – both the general terms we use and those specific to our work.

The terms and phrases that are specific to our jobs or our companies create a vernacular. The definition of vernacular is ‘the mode of expression of a group or class.’ Our vernacular separates software developers from lawyers, Amazon employees from Microsoft employees, and your team from the other teams in your company.

The words and phrases that we use in team discussions give us a shorthand. They save time. Instead of saying, ‘Ok, deploy this to production, let the support team know that it is going live and then let marketing know once it has gone to 50% of active users.’ Your team may just say, ‘let’s deploy-ify it.’ The larger context is defined and understood in the vernacular of your team.

One of the challenges of joining a new company or a new team is learning the vernacular. One of the significant struggles of team forming or cross-team communication is different definitions for the same words.

Consider the word ‘Agile’. To you, it may mean ‘Scrum’ because your only experience working in Agile teams was working with the Scrum framework. For me, it may mean Kanban or a set of principles not tied to any specific framework. If we are on the same team and I say that I think we should work in an Agile way, we could have very different interpretations of what that means, which may inadvertently create tension in the team.

‘Done’ is another word that often leads to problems – both for a development lead and between teams. A developer on your team says that their feature is ‘done’. Do they mean that they finished the code? That they tested the code? That they deployed it to the staging environment? That the code is running in production? That the A/B tests for the code have completed?

Having clarity on the meaning of words is critical. Companies will often create glossaries of the terms and phrases in everyday use to help onboard new employees. You should do the same for your team for the words and phrases your team uses day-to-day. As a leader, you should also deliberately cultivate your team vernacular.

Creating a team vernacular

Creating a team vernacular is a simple way to drive team unity, identity, and alignment around best practices.

A simple way to start building a team vernacular is to use a group meeting to identify and define the words and phrases used in the team. You can get the discussion started by spending a few weeks taking note of words or phrases that come up often in team discussions. Terms such as done, tested, shipped, agile, stuck, autonomy, microservice, or waiting, may have different definitions from different people on a team.

Ask the team what they think each of these words means. If there is a general agreement, add it to your team glossary. Don’t stress over creating a perfect definition for each word. You can reference a dictionary definition or definitive blog post if you want, but your goal is team consensus around the meaning, nothing more.

Once the team establishes the primary vernacular, update it as necessary. Clarify the definitions of the new terms introduced. If someone uses a word in a new way, ask, ‘What does that word mean to you?’ If you don’t recognize a term that others are using, ask the team for the definition. Add these new words and phrases to your glossary. Over time, the meanings of words will change as they understand new subtleties or gain new skills. When this happens, append or replace the existing definitions.

Using your vernacular to train the team

Creating consistency in the words you use, or introducing new words, is also a valuable way to train your team or introduce new concepts.

You may find that there is debate within the team about the constraints for a system to be called a ‘microservice’. This debate is an opportunity to find blog posts or a book for the team to read together and discuss to create the definition for the team glossary.

If you want to understand secure coding practices better, you could watch a conference talk as a team and then discuss what words and techniques you could introduce into your vernacular.

As you build your glossary, include the phrase and what it means to your team and the references your team used to arrive at that meaning. Your dictionary can then become an onboarding tool, a training tool, and a reference to share with other groups.

Vernaculars happen

Groups of friends, co-workers, teams, and families all create unique vernaculars over time. The in-jokes you have with friends, the shorthand you have with your partner, the CEO’s catchphrase. Be aware of this, be deliberate about it within your team, and use this naturally occurring phenomenon to your advantage!

[posted originally at https://leaddev.com/communication-relationships/building-vernacular-your-engineering-team]

Fail Safe, Fail Smart, Succeed! Part Five: Putting it into Practice

Fail Safe, Fail Smart, Succeed!

Putting this into practice at Avvo

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.

Fail-safe architecture

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.

We also redesigned our organization to leverage the reverse Conway Maneuver, further accelerating the new architecture.

You can build a fail-safe / fail-smart team

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.

Fail Safe, Fail Smart, Succeed!

Fail Safe, Fail Smart, Succeed! Part Four: My Biggest Failure

Fail Safe, Fail Smart, Succeed!

My Biggest Failure

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.

Part Five: Putting it into Practice

Fail Safe, Fail Smart, Succeed! Part Three: Making Failure Safer

Fail Safe, Fail Smart, Succeed!

Making Failure Safer

How do we reduce the fuel-air bomb failure into an internal combustion failure? How can we fail safely?

Minimizing the cost of failure

If you fail quickly, you are reducing the cost in time, equipment, and expenses. At Spotify, we had a framework, rooted in Lean Startup, that we used to reduce the cost of our failures. We named the framework “Think it, Build it, Ship it, Tweak it.

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.

Think It

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.

Build It

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.

Ship It

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.

Tweak It

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.

Maintain It

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.

Part Four: My Biggest Failure

Fail Safe, Fail Smart, Succeed! Part Two: Building a fail-safe culture

Fail Safe, Fail Smart, Succeed!

Building a fail-safe culture

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.

If you worry about employees taking risks without accountability, give them participation in the revenue that they bring in

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?”

“No.”

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.

For an example of project retrospectives processes, see this post from Henrik Kniberg.

The project retrospective repository

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.

Part Three: Making Failure Safer

Fail Safe, Fail Smart, Succeed! Part One: Why Focus on Failure?

This article is about failure and everything I’ve learned from 28 years of failing (and succeeding) in the technology industry. Its basis is my talk of the same name that I first gave in 2015.

I’ve broken it into five parts to make it easier to read and share:

The importance of failure in software development

How we approach failure is critical in any industry, but it is especially crucial in building software.

Why?

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.”

In his own words, Thomas Edison says that he created three thousand different theories before he found the right materials for his electric light. To invent his battery, the laboratory performed over ten thousand experiments.

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.

The Lesson of Clippy

Even if you’ve never used the Office Assistant feature of Microsoft Office, you are likely aware of it. It was a software product flop so massive that it became a part of pop culture.

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.

Part Two: Building a Fail-Safe Culture

Succession for Scale

Recently, I have been thinking about the role of the executive in a scaling startup.

As a senior leader in a growing company, you need to be scaling faster than the organization. You grow by scaling yourself and the leaders in your team more quickly than the business. This fact is well known and is covered excellently in such books as Zero to One and The Hard Thing About Hard Things.

Even if you are aware of this fundamental requirement, it is still challenging to recognize when you are starting to fall behind on that scaling. The people on your team, the people that got you to where you are today, who are working as hard as ever, should be doing better than they are. You may start seeing the signs: teams falling behind, tensions between groups or functions, team leaders beginning to struggle with their work, and increasing responsibilities.

You might not know what these scaling problems look like because you haven’t seen them before. Maybe you do recognize them, but your loyalty to your team lets them go on longer than they should. You can get away with that for a while.

Eventually, your boss (the CEO, the board) or your peers start to recognize the growing gaps in your organization between where you are and where you should be. In a company with a good culture, they will let you know. In a company with a less-open culture, your peers may notice but not feel like it is their place to say.

By the time the problems are apparent outside your team, it will be nearly too late.

When these problems first arise, you need to put together a plan. If you missed the early signs and the challenges are visible outside your team, you need to act immediately.

You need to bring in new talent who can help close that gap. It will take time to do that. If you choose to re-double your efforts to mentor the existing folks, you will only fall further behind. Either you missed your window to mentor, your leaders need more mentorship than you can provide, or they are not yet ready to take on the new responsibilities in their role even with mentorship.

Replacing people who have historically done well in their roles can seem cruel, and this is why it is hard. It feels disloyal to the people that have been loyal to your company and have helped to build it along with you. It is not their fault.

If you don’t make those hard choices, though, they will be made for you by the person whom your boss or the board hire to replace you.

It doesn’t have to be this way.

We have an assumption that in a growing company, people will remain in the roles they have had, and newer employees will come in below them. This assumption is one of the exciting incentives of joining a startup. It can be a career accelerator. Indeed, there are many stories of early employees at startups remaining in their senior leadership roles through rapid growth and past the point of going public. Very few people are capable of this kind of personal development, however.

Instead, we should be explicit about this challenge of growing a company. We should build a culture that acknowledges and celebrates this fundamental fact. Let people your hire know that you will support their growth, but be honest that if the company is scaling faster than they are, they may need to help hire the person who will help with the next phase.

Reid Hoffman talks about these ideas in his book The Alliance. I think Netflix has done well being explicit around the Tour of Duty in their culture. I do think Netflix is a bit too employer-focused in its attitude towards these ideas. This approach works for them because they favor hiring experienced developers and do not invest much in training their employees relative to other companies. That is another definitive decision of their culture.

I advocate for a more balanced and sustainable approach for companies, one that encourages employee development and business realities. Startups that are willing to hire at all levels of experience and support employee growth can hire and retain better. Even those companies face challenges at their scaling inflection points when company leadership changes by the new business reality’s necessities.

Suppose your company builds the concept of succession for scale into its culture. In that case, hiring your successor should be expressed as an opportunity for further mentorship and growth and not as a demotion or failure. Celebrate it as a rite of passage. Challenge the leaders in your team (and give them the tools) to recognize when this time has come, and praise their self-awareness.

Build succession for scale into your compensation structure and leadership career pathing. Ensure that the newly hired leaders train the people they have replaced to assume the role once again. If the position opens up in the future, the person may now have the skills to step back into it.

[This article was originally posted at https://nimbleautonomy.com/articles/succession-for-scale.html]

A resignation can be an opportunity

People leave jobs. If you are a manager, people will leave your team, just as someday you will leave your team.

When this happens it’s an opportunity, a chance to re-evaluate. While you might want to immediately pull out the job description that you used when hiring for the role last time, instead, take some time to think.

A chance to learn

When someone that works for you tells you that they are resigning, it can feel personal: ‘they don’t like working for me.’ It can hurt. You might immediately look for any reason why it isn’t your fault. You may obsess about everything that you could have done differently. It is natural to want to move on as quickly as possible.

Instead, after an employee gives you notice, take a day or two to process and get some distance. Recenter. Come back to them with an open mind. Do not look to assign blame and let them know that you are working to improve the team for the people that are still here. Ask what was not working for them and what they will miss, aiming not to assign any extra meaning to what they say. Take notes. Thank them.

Take some more time to distance, then come back again and think about the leaver’s words. Try to understand from their perspective what they experienced. If they are taking a more senior role elsewhere, was there an opportunity like that in your company that you could have helped them get? If they are joining another company to learn a different technology, was it a technology they could have explored in your organization? Was there another team in your company that they could have joined instead? 

Your goal is to understand their unmet needs. Were there signs that you missed? Were there opportunities in your company or in your team that could have addressed their needs?

Once again, the goal is not to assign blame, and the goal is not to get the employee to change their mind either. The goal is to learn from this experience.

So moving forward, how can you approach your role in a better way?

Consider this process to be a personal retrospective, and just like in an Agile team retrospective, you may want to emerge with a list of things to keep doing, a list of things to start doing, and a list of things to stop doing.

A chance to change

As teams evolve they shift and mature. If the leaver has been in the group for a long time, they may have accumulated an unusual set of responsibilities and they may have influenced the technical decisions around their strengths.

While it may seem like the obvious decision is to look for someone with the same skill set, that is just ‘role inertia’ (credit to Omosola Odetunde for introducing that phrase to me). Instead think of this as an opportunity to re-evaluate and make a change without impacting someone.

Consider your technical vision for the team and the skill sets of others in the group. Is there something missing that could help you today or in the future? Is this role still needed? Should you repurpose the position into a different one based on the team’s long-term needs?

It is critical to be thinking about long-term needs and not short-term ones. A mistake tech leads often make is that they hire someone because of near-term demand. They assume that there will be a headcount later to cover the long-term need, but too often that headcount doesn’t appear and now the team is missing a crucial skill set.

Potentially your team is out of balance, where you have too many (or too few) senior folks. This opportunity means you can now rebalance the levels within the group. Maybe this role is no longer necessary and you can give a headcount to another team that needs it more, or is there someone on the team who is looking for a new challenge and can step into the role?

If you are in a position where you manage multiple teams then this might be an opportunity to re-evaluate the team structure, especially if the leaver is a manager. A way to approach this exercise would be to imagine that the person leaving was never on the team. Your manager has given you a brand new headcount and asked you to figure out how you want to use it.

Once you have a plan, you can then write the job description and look to fill the role, as you may decide that you need to replace the person with someone who has a similar skill set. If so, you can move forward confidently knowing that you have thought it through, and if you have also taken the time to learn you will hopefully retain your new hire for a long time. 

[This article was originally posted at https://leaddev.com/hiring-onboarding-retention/resignation-can-be-opportunity]