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Using Data To Improve Cleaning Efficiencies

The data collected by these smart machines and dispensers is funneled into software platforms created by each individual manufacturer. But what happens from there is the next great, relatively unanswered question in the Internet of Things world. Obviously, that data must be analyzed so that trends can be identified and actions can be taken. But who will do the analyzing? Commercial cleaning equipment manufacturers have worked hard to ensure their data-management software platforms are user-friendly and intuitive. But most admit that facility cleaning managers may need to turn to outside help in order make the most of all this data. Some manufacturers are looking into creating consulting departments to provide such services — at a cost. Others are providing that help as part of their IoT package. One manufacturer even has the expectation that analyzing IoT data is probably beyond the capability of most cleaning managers, and thus the manufacturer doesn’t even share the raw data with the customer unless requested. The approaches vary considerably. As a result, facility cleaning managers can probably expect independent cleaning industry consultants to begin offering data consulting services, as well. At the very least, a cleaning manager may need to bring the data to an information services department within their own company. Then comes the question of a timeline for return on investment. After all, data is only useful if there is a lot of it. Small sample sizes could lead to faulty assumptions about cleaning practices, which could lead to faulty corrections to cleaning practices, which could prove costly. Data takes time to accumulate, however, and that could take anywhere between several months and a year, depending on the size of the cleaning operation, says Baynum. “If you have a healthcare facility, or a facility that’s running 24/7, we think within 90 days we can have a significant amount of data to highlight very immediate trends,” he says. “If it’s a slower facility, it could take 12 months. So it’s really going to depend on the frequency and number of visitors.” But that, say manufacturers, is all the more reason to embrace IoT today — it’s imperative that cleaning departments begin collecting data right away. What’s more, IoT — or more accurately, data collection — isn’t a passing fad. “We all know technology comes and goes,” says Nissen. “But the need for data, leveraging that data and insight to drive action that helps anyone better manage business, I think, will always exist.” Right now, says Baynum, IoT technology is still only used by the group known in tech circles as “early adopters,” which usually constitutes about 13.5 percent of users. Next will come the “early majority” with 34 percent of the user base, then the “late majority” with another 34 percent. But it might only be another five years before the late majority latches on, he says. And facility cleaning managers can probably count on many building service contractors to be among the early adopters, since the technology fits the BSC model: economies of scale come into effect with larger machine fleets, and early adopting contract cleaners can and will make use of IoT to stand out when writing bid proposals. “Today, [IoT] is the order winner,” says Boscher. “Down the road, it will be the order qualifier.” If facility cleaning managers want to take advantage of an opportunity to differentiate, the time to jump aboard the IoT bandwagon is now, say manufacturers. At the rate technology evolves, IoT will soon become ubiquitous in the cleaning industry — just as it did in the consumer market for the janitor with her fitness tracker and her smart TV. Nick Bullock is associate editor for Facility Cleaning Decisions’ sister publications Contracting Profits and Sanitary Maintenance.

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