A few years ago, while working with DJ Patil (now the Chief Data Scientist of the U.S. Office of Science and Technology Policy) on an article about data scientists, he related to me a general rule about big data that we had both observed in the field: “Big data equals small math.” My explanation for this phenomenon is that companies often have to spend so much time and effort getting big data into shape for analysis that they have little energy left for sophisticated analytics. The result is that, for many organizations, the most complex analysis they do with big data is the bar chart.
Unfortunately, the same situation is true for Internet of Things (IoT) analytics. This should not be surprising, since it’s a form of big data. The challenge with IoT data is often not the volume, but the variety of data. If you want to know what’s going on with a car, for example, there are a couple of hundred sensors creating data that require integration, much of it in manufacturers’ proprietary formats.
As a result, most of the “analytics of things” thus far have been descriptive analytics – bar (and Heaven forbid, pie) charts, means and medians, and alerts for out-of-bounds data. These “measures of central tendency” are useful for reducing the amount of data and getting some idea of what’s going on in it, but there are far more useful statistics that could be generated on IoT data.
So for the rest of this column, I’ll describe the analytics of things – both current and potential – in terms of the typology of analytics that I and others have employed widely: descriptive, diagnostic, predictive, and prescriptive.
As I mentioned above, these have been the most common form of IoT analytics thus far. But there is still progress to be made in the descriptive analytics domain. Integrated descriptive analytics about a large entity like a person’s overall health, a car, a locomotive, or a city’s traffic network are required to make sense of the performance of these entities. The city-state of Singapore, for example, has developed a dashboard of IoT traffic data to understand the overall state and patterns of traffic. It’s not the be-all and end-all of IoT analytics, but it at least gets all the important descriptive analytics in one place.
Another useful form of descriptive IoT analytics is comparative analytics, which allow a user to compare an individual’s or an organization’s performance to that of others. Activity tracker manufacturers like Fitbit and fitness data managers like RunKeeper and MyFitnessPal allow comparison with friends’ activities. The comparative descriptive analytics provide motivation and accountability for fitness activities. Similarly, the Nest thermostat offers energy reports on how users compare to their neighbors in energy usage.
I have not often used the “diagnostic analytics” classification favored by Gartner because the explanatory statistical models it involves are usually just a stepping stone to predictive or prescriptive analytics. But diagnostic analytics have some standalone value in the IoT context, particularly for qualifying alerts. One big problem for the IoT is going to be the massive number of alerts that it generates. Alerts are generally intended to get humans to pay attention, but “alert fatigue” is going to set in fast if there are too many of them – as there are already today in health care with medical devices.;