Cognitive Technologies and the Future of the Internet of Things

We are in the early stages of what many, if not most, analysts believe is a fourth industrial revolution (aka Industry 4.0). Analysts from the Boston Consulting Group (BCG) explain the first three revolutions were driven by steam, electricity, and automation. They write, “Industrial production was transformed by steam power in the nineteenth century, electricity in the early twentieth century, and automation in the 1970s.”[1] The fourth revolution is being driven by data. The BCG analysts explain, “Today, another workforce transformation is on the horizon as manufacturing experiences a fourth wave of technological advancement: the rise of new digital industrial technologies that are collectively known as Industry 4.0.” Much of the data used to power Industry 4.0 will be delivered via the Internet of Things (IoT) — sometime referred to as the Industrial Internet of Things (IIoT).

 John McDonald (@jpmcdon), President and CEO of ClearObject, observes, “IoT and data are critical for today’s operations in any industry. It’s no longer feasible to ignore the benefits for efficiency, productivity and customer satisfaction that are results of using advancements in IoT and data. Each and every industry must adopt new and inventive methods like IoT and machine learning to analyze transactions and data in any form whether it’s a car that can detect driver fatigue, preventive maintenance sensors, or nanotechnology to monitor food sources.”[2] McDonald alludes to the fact that most people are talking about a larger ecosystem when they use the term “Internet of Things.” The IoT ecosystem involves sensors generating data, the IoT which transmits the data, and advanced analytics (often involving cognitive technologies) that make of the sense of data.

McDonald notes, “The trend of collecting ‘big data’ just to have it on hand is over. Businesses now have to use that data in ways that allow them to make crucial decisions that affect their bottom-line, like customer satisfaction, preventive maintenance, etc. Predictive analytics will continue to advance and give industries like manufacturing and logistics not only the data they need, but the suggested actions and steps to take as a result.”

 IoT growth makes cognitive technologies an imperative

Connecting things (i.e., exchanging data) is only valuable if you can do something with the generated data. Rachel Stinson observes connecting things is often the first step in understanding things people want to know. She explains, “Smart home hubs, thermostats, lighting systems, and even coffee makers collect data on your habits and patterns of usage. When you set up voice-controlled devices, you allow them to record what you say to them and store those recordings in the cloud, and that data is collected to help facilitate machine learning.”[3] Understanding patterns discovered using machine learning can help people leverage those patterns.

 Although Stinson focuses on the consumer side of the IoT, the IoT’s greatest benefits are found in the industrial sector. The former editorial staff at Information Management (IM) wrote, “To date, a good amount of the attention around IIoT has focused on machine learning and artificial intelligence to address things like how to reduce energy and predict maintenance problems.”[3] Sreekar Krishna, Director of Data and Analytics at KPMG, points out that big data and the IoT are not just being embraced by technology companies, but even the oldest of industries — agriculture.[4] Like McDonald, Krishna notes industry focus has shifted from collecting data to analyzing data. “While Big Data has provided boosts to the bottom line,” he writes, “the next generation of Big Data will require substantial investment into Big Insights, not just Big Data. This represents one of the biggest transformational changes yet to be seen in the IT world.” Like Stinson, Krishna notes machine learning is often the first step toward big insights. He goes on to note, “While [machine learning] represents a great opportunity for almost all industries, enabling production ready ML systems require platforms that are capable of state-of-the-art data ingestion, data transformation, and model hosting.” In other words, big insights require a cognitive computing platform, like the Enterra Cognitive Core? — a system that can Sense, Think, Act, and Learn?.

 The importance of data

 The Digital Age is being driven by data and the IoT will continue to generate incredible amounts of data. Maciej Kranz observes, “Simply connecting as many ‘things’ as possible to the IoT is a recipe for failure if you do not have a way to capture and analyze the data they generate. Yes, connected devices and sensors form the foundation of the IoT, but the value comes from the real-time or near real-time streams of data they produce. By capturing and analyzing this data, businesses can gain actionable insights and make better decisions.”[6] Raw data, however, often needs to be manipulated to make it useable. The former IM editorial staff cautions, “Along the way people have discovered if you [fail to] take care of the administrative/behind-the-scenes tasks (such as data prep) before plunging into analytics, you’re often going to end up with vague, inconsequential recommendations.” They continue:

“It’s a variant of the old garbage in/garbage out problem. Companies know their system data is valuable, and they want to use it. Unfortunately, it gets generated in massive volumes and conflicting formats; in its raw form, the data can be indecipherable to humans. It needs to be organized and synthesized first. People like to say data is the new oil. Which is accurate, but in an ironic way. Like oil, there’s far more data out there than one might think, and it is often in difficult-to-reach locations; it won’t do any good until it’s passed through a complex refining process. Not quite the analogy they meant.”

Stinson asserts, “You will have control over data, and can analyze data for many different purposes to make the best possible purchase, marketing, cross-selling and security decisions.”

 Concluding thoughts

 Kranz concludes, “IoT by itself is not truly transformational. To be sure, connecting devices and generating data can help organizations automate existing processes and achieve operational efficiencies. But the truly disruptive and transformational potential of the IoT is revealed when it converges with other up-and-coming technologies — specifically, artificial intelligence, machine learning, [edge] computing, and blockchain.” Jacques Touillon (@jacktouillon), CEO of AirBoxLab, insists companies combining IoT with cognitive technologies will prove to be longer-lived than competitors. “The nature of machine learning,” he writes, “means that, among competitors, the ones that go the furthest in machine learning get to keep their advantage for a long time.”[7]

 I would modify that statement and replace “machine learning” with “cognitive technologies.” Why? Nicole Martin, owner of NR Digital Consulting, explains, “When it comes to Big Data, these computer science terms are often used interchangeably, but they are not the same thing.”[8] She asserts machine learning is basically a self-explanatory term. “The important thing to remember with ML,” she writes, “is that it can only output what is input based on the large sets of data it is given. It can only check from what knowledge it has been ‘taught.’ If that information is not available, it cannot create an outcome on its own.” On the other hand, she explains, “AI can create outcomes on its own and do things that only a human could do. ML is a part of what helps AI by taking the data that it has been learned and then the AI takes that information along with past experiences and changes behavior accordingly.” She adds, “They are both crucial to the future of technology.” Enterprises are looking for more than pattern recognition; they want also want actionable insights. Only cognitive technologies can give them the insights they are looking for.

 Footnotes

[1] Markus Lorenz, Michael Rü?mann, Rainer Strack, Knud Lasse Lueth, and Moritz Bolle, “Man and Machine in Industry 4.0,” bcg.perspectives, 28 September 2015.

[2] John McDonald, “Behold the IoT Invasion: Eight Reasons to Plug In,” IndustryWeek, 12 March 2019.

[3] Rachel Stinson, “The Inevitable Future of the Internet of Things (IoT)!” Supply Chain Game Changer, 30 July 2019.

[4] Staff, “Growth in IoT devices is fueling demand for machine learning technology,” Information Management, 30 July 2019 (out of print).

[5] Sreekar Krishna, “Big Data IoT Revolution has Embraced Even the Oldest of Industries,” CIO Review, 9 October 2018.

[6] Maciej Kranz, “IoT fact vs. fiction: Setting the record straight for your business,” Smart Cities Dive, 19 March 2018.

[7] Jacques Touillon, “Why machine learning will decide which IoT ‘things’ survive,” Venture Beat, 8 January 2017.

[8] Nicole Martin, “Machine Learning And AI Are Not The Same: Here’s The Difference,” Forbes, 19 March 2019.