Erica Walsh and the innovation team she leads at Bridgestone had a breakthrough idea: inspired by the realization that new Uber and Lyft drivers are particularly sensitive to unexpected maintenance issues, they thought, “What if we could create a sensor that would help new drivers predict an issue like a tire blow-out before it ever happens? Wouldn’t that be amazing?”

But how should Bridgestone determine whether to invest in turning Erica’s idea into reality?

Think about how your organization typically approaches developing such an idea. Almost always, the question becomes one of technological feasibility: how would we reliably measure and predict tire blow-outs? Erica says if she had handed the idea over to the traditional R&D organization, they probably would have come back in six months with a highly reliable assessment of how to measure tire tread down to the 8th or 9th decimal place, whether by lasers or infrared device.

But here’s the thing: the feasibility question is a really expensive question to answer. Six months of R&D time is no joke! For sure, before rolling out a service, the organization MUST be confident that it can indeed perform the function technologically; but it’s not the first question to answer.

The first question to answer is not, “Can we do xyz…” but rather, “Should we do xyz…”

Which is to say, the first question isn’t technological, but human. The immediate need is to assess desirability, not feasibility. And given a focus on desirability, the first consideration isn’t technological data, but human data.

And human data is actually pretty cheap to create through scrappy experiments!

Instead of getting the R&D lab to tackle the challenge of tire measurement, Erica and her team took to the streets to quickly determine whether human beings actually found the concept of a tread measurement service desirable. Rather than asking outright (always a dangerous thing to do!), they installed a handful of engineers to offer a manual, onsite service at a local parking garage. And in short order, they gleaned some fascinating learnings about the type of information a driver would actually need.

Far more than simply validating that the idea should go to R&D (which is in itself a meaningful value-add!), their learnings also informed the technical specifications of the actual product to be developed, dramatically increasing the confidence with which the management team deployed R&D resources to solving the tricky technological challenge.

If you want to know whether your idea is a good idea, the first kind of data you should create is desirability data. Luckily, it’s some of the quickest and cheapest data you can create.

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