Eric Pelz

How the best product engineering teams maximize value

Also published at HackerNoon on April 16, 2018.

Reducing risk, eliminating complexity, and sequencing effectively

With perfect information, an engineering design process can be pretty straightforward: you can focus on implementing functionality while minimizing engineering complexity and risk. But this gets a lot trickier when there are a lot of product uncertainties. How do you make an engineering plan when you don’t know how it will be used or extended? In these cases, you need to have a strong dialogue between product and engineering — healthy teams don’t silo each person, but have them work together as one team. Through this, you can plan to maximize user value through incremental releases, by minimizing product and engineering risk.

The most interesting types of engineering problems

At Asana, I’ve been the tech lead of a few different teams, and have seen first-hand that each team has its own joys and challenges in the engineering design process. For example, one team involved rewriting an unperformant feature to a new framework. This meant that the team had a well-defined product with little uncertainty, as the feature was already launched. In this team, we only solved engineering problems: e.g. how to write efficient queries, differences between our old and new frameworks that could impact the product, and how to build abstractions to harness the power of the feature.

My favorite kinds of teams involve much more product collaboration. When building powerful new features, there are many types1 of risk at play — product and engineering are two of the most common — and you need to think of them all when planning a product engineering project. Product risk can be very dangerous: if you build the wrong feature, you might need to re-build the right feature afterwards. At best, this means wasting some time. At worst, this means building and maintaining two separate systems instead of one. This means the scope of problem solving changes completely, as an engineer needs to be a partner in the product process and think through both engineering and product tradeoffs.

In other words, be involved in all stages of the product process. Do this from the start, even before identifying the problem statement and initial hypotheses. You will be better equipped to think through product tradeoffs and be a good partner to your product manager. Similarly, a product manager should be a partner in the engineering process. They should understand where complexities or legacy technologies live, intuit which solutions are simpler, and so on. A successful collaboration results in a better product delivered with higher velocity.

How to approach these problems

Unfortunately, many engineers focus on an extreme when they see multiple types of risk. If there is large product risk, they’ll prototype to deliver value as fast as possible, which could harm their future viability. On the other side, they might over-abstract and focus too much on the long-term feature-set, risking added complexity and delaying when they deliver value. The ideal path forward is to be somewhere in the middle, and balance the product and engineering concerns.

If you’re at a product company, your ultimate goal is likely to deliver user value. There’s a lot to consider: what user benefit will you deliver, what risk will that have, when will the release occur, what engineering debt will you add, and so on. The danger is when these factors aren’t considered when planning a project. That likely happens when it’s assumed that someone else will consider the factors (i.e. the PM will think about product risk, and you, the engineer, only need to care about engineering planning).

Brief interlude: concretely modeling this

Bear with me for a moment, we’ll return in a minute — but it’s helpful to think of this from the perspective of economics. Suppose you have a project which you are developing an engineering plan for. You can deliver it all at once, or through a series of incremental releases. We want to maximize the expected value for hypothetical releases. If you squint a bit, we can model this the same way as risk-adjusted net present value in economics. We have a series of “releases” at different points of time, each with a different risk profile and net value. Since we prefer value as soon as possible, we “discount” the value based on when it will occur.


  • Ui represents the net value from release i. This combines a few factors: Ui=Vi-Di-Ci

    • Note: assume these are all the same unit — usually dollars, which is a fairly generic unit that we can translate to.
    • How much value (Vi) do users get from a release? For example, they can do a new workflow that they previously couldn’t.
    • How much disutility (Di) do users get from a release? For example, if you delivered a half-baked release which degrades user trust.
    • How much cost (Ci) did your team incur from release? For example, 5 engineers spent 2 weeks on features.
  • ri represents the probability a release can happen to yield the value Ui. This models the risk of a release.

    • Note: assumes that a release is all or nothing. In practice, a release would be delayed or its adoption would be lower than expected. This could be modeled with many more “potential releases” at different probabilities.
    • For example, this might be: product risk (build the wrong thing), engineering risk, and so on.
    • For example, you can de-risk a project by having an early release beta program to receive feedback.
  • 1/(1+d)^Δti represents the “discount factor

    • d is the discount rate — the value that can be earned per unit of time on a different investment of similar risk.
    • Δti is how long has occurred between release i and i-1.

With this, we can model expected user value from a series of releases. While I’ve never calculated this explicitly, and this model is highly simplified, it lets us compare how each factor relates to one another and the overall plan.

Best practices to maximize value, with inspiration from the model

Through the teams and launches I’ve been a part of, I’ve gathered some learnings about maximizing value. These also are reflected in the simplistic model above.

On thinking through a release plan

  • You should aggressive split a feature into small releases. If it adds no risk or cost, then splitting a feature into multiple releases is a great way to increase user value. Earlier and more frequent delivery means parts of the feature can get in the hands of users sooner. In our model, this is reflected by the discount factor yielding lower net value.
  • There are many considerations to a release plan — feature readiness, market conditions, product and engineering risk — and coming up with a plan should be a collaboration of all interested parties. After the initial planning for a project, consider whether incremental or early releases would be useful.

On thinking about engineering design

  • Adding engineering debt or complexity is dangerous. It can complicate all future development or add risk to all future releases. As such, optimizing for the short-term through engineering complexity can have dramatic consequences to long-term user value. This often manifests by building “partial” features that ultimately introduce debt, rather than building the smallest complete unit of user value.
  • Balance is important — while you should always think of future engineering design, you shouldn’t over-engineer such that you deliver something to users much much later. If there is product uncertainty with the future (for example, it’s not clear how a feature will be extended or generalized), then you risk building the wrong abstraction which can slow future development.
  • All engineering complexity will result in additional cost or risk, so it is almost always better to circumvent complexity when possible. Being a partner in the product process will give you context to think through non-engineering solutions to complexity, thereby increasing expected value.

On mitigating risk

  • It’s incredibly helpful to sequence work such that you mitigate risk for large releases. Suppose you are planning a big release in 6 months and the product risk is currently high. You might have the opportunity to halve product risk with a small earlier release to validate hypotheses. This will ultimately double the final release’s expected user value. You can also apply this technique when using a new technology with large technical risk. If you de-risk a project early with a small release to validate an engineering plan, you will improve the project’s expected value.
  • Different types of risk compound one another, so it’s dangerous to combine multiple types of risk in a single release. Consider all types of risk, and strive to focus a release to a single type of risk (e.g. couple riskier engineering with less risky product work).
  • It’s helpful to think about this from the perspective of “minimizing the worst outcome”. In other words, think about what kind of risk is biggest, and what contingencies you have in place. For example, is your engineering design resilient to portions of your product changing as a result of beta testing? If not, consider doing that for areas with high product risk, or deferring engineering design until you have higher confidence on the product direction.


  1. Some of the most common types of risk I’ve seen (see here for more):

    • Engineering risk — are there a lot of unknowns in the technical plan? Does this use new technologies that aren’t often used? Does this touch a lot of old code or systems that the team doesn’t know or that may need updating/refactoring? Will this cause stability fallout?
    • Product risk — are we confident in the product plan? Will we need to iterate on the feature for it to be useful? Could it completely flop and need to be re-done, after showing to users?
    • Team risk — how engaged are team members? If a milestone slips, will that make the team feel demotivated? If added uncertainty or roadblocks come up, will team members move as quickly? How confident are team members in the team’s leadership?

Hello! I work at Asana, where I'm the tech lead across our AI initiatives.