Impact and effort

For a given project, we often assume that there is a linear relationship between the amount of effort (or time) we put in and the amount of impact we get out. “The more time we spend on this feature, the more impactful it will be,” we might tell ourselves.

But this is rarely—if ever— the case. Many projects follow the Pareto principle: 80% of the impact comes from 20% of the effort. This is often the case in the earlier stages of product maturity: just having a feature is the most impactful thing you can do; polishing and tuning that feature will only have small incremental effects.

But there is another case: the inverse Pareto principle, where there is an exponential relationship between impact and effort. Some examples where this can happen are:

• When an initial feature simply doesn’t work on its own because it is meant to work in concert with other, not yet developed features;
• When you’re in an all-or-nothing situation. One of these all-or-nothing scenarios is fixing data quality issues: there is no value in your data analytics ecosystem if the data is only accurate some of the time.

If you are exclusively focused on speed, projects that have this type of curve can be dangerous. If you only build features that are easy to implement, you might be skipping the most impactful work.

Thinking about these curves is also important when scoping a project. You certainly don’t want to end up building an MVP that simply isn’t impactful.