Safe to fail experiments are about education.

Safe to fail experiments are about learning

In IT, ‘safe to fail’ describes activities that are used to generate knowledge. Additional terms that might be used are probes, spikes, R&D and prototypes. The outcome of a safe to fail experiment can be innovation, the information necessary to make decisions or to discover alternatives.  All these scenarios are appropriate for a safe to fail experiment, however sometimes safe to fail experiment are not safe nor are they appropriate.

Safe to fail experiments are appropriate in situations where learning is required to deliver a project. You might need an experiment or a bit of prototyping to learn whether something is possible or if there is a more effective way to perform a repetitive task. For example, earlier in my career my team converted credit card portfolios from one provider to another. We used a standard process for most of the hard work. Yet, we were always looking for a way to make the process more efficient using mock conversions as experiments. The goal was to find better processes and techniques.  These experiments did not always yield process changes, but they always added to our knowledge of what would and wouldn’t work. Another opportunity for safe to fail experiments is in situations where several possible alternatives exist. You need an experiment to determine which (if any) of the alternatives make sense. A test or an experiment is considered successful if it proves either that an alternative is a good choice OR if the results can be used to cross an alternative off the list. Organizations that judge results that can disqualify an alternative as a failure are apt sort out alternatives in the market place rather than before they go into production because their experiments will not truly be safe. Finally, safe to fail experiments are useful tools in formal decision-making frameworks. For example, frameworks built to comply with the CMMI process area, Decision Analysis and Resolution (DAR), often leverage safe to fail experiments to generate data for formal, structured decision-making.

Safe to fail experiments are not always appropriate.  Many projects exist with a both fairly well understood solution and a hard deadline. In some portion of these projects the solution may not be perfectly optimal or the coolest possible solution, but it is doable in the timeframe. Experimenting to find a more optimal solution when taking the time and effort could cause you to miss the due date is not appropriate. In the credit card example mentioned earlier, we explored new ideas and optimization between projects to avoid experiments that would put the conversion at risk or cause the team to miss more of their weekend than was already necessary. Experiments that imperil the commitments a team has made or inhibits their ability to effectively deliver are not experiments and are safe to fail. Experimenting is also not appropriate where negative results are thought of as a black mark on a career. In this case straying from the tried and true approaches are generally a bad idea. This scenario tends to occur in highly politicized or overly competitive organizations. In this type of scenario if you can’t change the environment, I would suggest experimenting with other job opportunities. A third scenario is more questionable than absolutely inappropriate. In scenarios where there is little potential upside to the experiment experimentation is probably not a good idea. In most organizations, time and attention are always in short supply. Every team needs to judge what it needs to learn and ration resources that are in short supply when there is little or no perceived upside.

Safe to fail experiments are scenarios where an organization or team truly wants to sift through possible alternatives before using them in a project or in production. Safe to fail experimentation is an application of the scientific method to generate knowledge and decisions so that teams and projects deliver more value.

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