IoT kills the law of averages

We live in a world governed by data. We have data-driven medicine, data-driven policy, and in business, data-driven decision-making. But in many cases, the data is used in a traditional way; companies or policymakers look primarily at averages. If a project has a 40% ROI, for example, that means that overall a project should return 40% more than what the company spent on it.

But depending on the project, it’s possible that the ROI is much smaller for some users and much higher for others. That 40% is just an average. We see the same thing in the smart home, such as when a smart thermostat maker argues that a consumer will save an average of $150 a year if they buy the $250 thermostat. While that may be true, for some consumers their savings may be much less, making the investment less worthwhile.

But thanks to IoT — or more specifically, the detailed, granular data from multiple individuals or machines combined with cheap computing power to crunch all of that data — it’s possible to stop selling averages. As a person who worked from home during the sweltering Texas summer while my husband was at work and my daughter was at camp, for example, the average energy savings that come from using a smart thermostat would never have accrued to me. As such, trying to sell me a smart thermostat based on those average savings would have made no sense.

But for another two-income household living in Texas, one where everyone leaves during the day, having a smart thermostat could save more than the average. The trick is knowing which camp you fall into. As more of our devices shed data, it’s possible to use that data to help classify the beneficiaries and losers in a system without resorting to averages. It’s actually why programs such as congestion pricing upset people — some people will end up paying more while others pay less.

Another way to think about this is that IoT makes the invisible visible. For example, it’s one thing to say a particular city has generally poor air quality on a given day. Those measurements are usually taken over a few sections of a city and extrapolated out over the entire metro area. But with more air sensors in more places around a city, such as those offered by Aclima, policymakers and citizens can actually see their air quality data at a block level at specific times during the day.

If a particular block sees a spike in poor air quality at the same time each day, it’s possible now to look at what’s happening at that block. Is there a traffic event? A factory? Construction? Policymakers can either determine the cause of the pollution and fix it, or optimize the process contributing to the pollution in order to mitigate the effect on human health.

This level of granular data means that bad actors can no longer hide the costs of their actions underneath the cover of averages. It can also make the externalities of pollution visible and allow cities to charge for it, ultimately pushing companies to invest in cleaner operations. Or at least that’s the hope.

The death of averages also comes into play in the world of business, as employers look at how employees perform. Through sensors and analysis, it’s possible to see how much time someone spends at their desk or how many widgets a warehouse worker can load on a given day. This level of detail means it’s possible to pay workers based on data that seems, to some, less biased or abstract.

But if employers take this path they must carefully assess their performance metrics and ensure they are counting the things that matter to the bottom line. The slower worker might break fewer items while the person who’s away from their desk makes more sales. When trying to dive into data, the details matter, more so as you leave the safety of averages.

Put another way, while IoT can make the invisible visible, but you have to make sure that what you’re looking at really matters. If you can do that, there is tremendous power in that data to target programs and interventions that will have an outsized effect on the problem you’re tackling — whether it’s finding the right buyers for your connected thermostat or easing pollution in a metro area.