Four Lessons from IoT Early Adopters

Four lessons from IoT early adopters: To paraphrase the late Roy Scheider in one of the greatest of all summer movies, you’re gonna need a bigger router. In 2025, Machina Research predicts, the Internet of Things is going to be a $3 trillion market of 27 billion devices generating more than 2 zettabytes of data. Two zettabytes of data is something like twice the total global IP traffic we’ll generate this year, according to Cisco.

The IoT data deluge is, by the way, the first of four lessons drawn from early IoT adopters by contributing writer Howard Baldwin for his article in Computerworld. IoT initiatives at ARI Fleet Management, for instance, generate the same amount of data every two weeks as the company previously collected in two decades. “Understand where data is coming from, and determine how you’re going to analyze it,” writes Baldwin.

The second lesson is that IoT will require cross-functional collaboration. Because IoT is deployed and used in factories and fleets and products, the IT department is going to need to partner with other functions and business units. “Determine how and when to combine operations and information technologies for maximum data insight,” writes Baldwin.

Baldwin’s third lesson for early adopters is that IoT is likely to require working with and coordinating across multiple vendors. The new Kansas City streetcar line, for instance, required collaboration with Sprint, Cisco and other vendors. “In orchestrating the many moving pieces of an IoT rollout, make sure you know who plays what part,” writes Baldwin.

Fourth and finally, as applies to forays into any young, fast-emerging technology, watch out that you don’t get caught out too far on the IoT growth curve. “Some early IoT adopters have reported reliability issues with either sensors or vendors or both, and others have struggled to reconcile competing protocols,” writes Baldwin. “Be prepared for setbacks in an immature market, and try to select a protocol that has long-term industry support and a sound security footprint.”

A primer on artificial intelligence: If you’re looking for a good primer on AI, Devin Coldewey of TechCrunch has found a gem. Turns out that in late June, the Office of Science and Technology Policy at the White House issued at request for information “on overarching questions in AI, including AI research and the tools, technologies, and training that are needed to answer these questions.” And IBM decided to help get the top office in the land get up to speed with the briefing posted here.

“It’s a fundamentally optimistic look, and a bit IBM-focused, of course,” writes Coldewey of Big Blue’s response, “but beyond that it really is worth reading to get a sense of the current state, future, and risks of AI. Don’t skip the ‘see more here’ links — that’s where the good stuff is.” What he said.

Executives + Algorithms > Executives: We humans are lousy decision makers. All the studies prove it. And yet, as this article on decision making in the age of AI in the new Deloitte Review persuasively argues, executives (presuming they are experts at what they do) are not going to get replaced by AI anytime in the near future.

“Though even simple algorithms commonly outperform unaided expert judgment, they do not ‘take humans out of the loop,’ for several reasons,” declare Deloitte U.S. chief data scientist James Guszcza and Monitor Deloitte business analyst Nikhil Maddirala. The reasons human experts are needed: to create predictive models; to decide the data to feed the models; and to assess the applicability of the resulting predictions.

“Although predictive models and other AI applications can automate certain routine tasks, it is highly unlikely that human judgment will be outsourced to algorithms any time soon,” write Guszcza and Maddirala. “More realistic is to use both data science and psychological science to de-bias and improve upon human judgments. When data is plentiful and the relevant aspects of the world aren’t rapidly changing, it’s appropriate to lean on statistical methods. When little or no data is available, collective intelligence and other psychological methods can be used to get the most out of expert judgment.”

Their conclusion: “Figuratively speaking, the equation should be not ‘algorithms > experts’ but instead, ‘experts + algorithms > experts.’” That’s heartening … as long as you’re an expert at something non-routine.