Patrick Schwerdtfeger is a business futurist specializing in technology trends including big data, the Internet of Things (IoT), machine learning, and artificial intelligence. Patrick is the author of the book Anarchy, Inc.: Profiting in a Decentralized World with Artificial Intelligence and Blockchain (2018, Authority Publishing) and a former speaker for Bloomberg TV. He has lectured at various academic institutions including Purdue and Stanford Universities. The increasing use of sensors in consumer and industrial settings is generating a tsunami of data, fueling neural networks, deep reinforcement learning, and machine learning. The macro trend from centralized to decentralized power structures is sowing the seeds of anarchy in our society and businesses need to understand these trends in order to thrive in tomorrow’s economy. Patrick’s keynote program on decentralization and anarchy will explain the tribal, individualistic and increasing self-reliance we see growing within our cultures, contributing to movements such as Brexit, Donald Trump, the Tea Party, Black Lives Matter and Occupy Wall Street.

Anarchy is coming! Will you be ready?

Profiting in the new economy will take vision, boldness, and knowledge which is why Anarchy, Inc. is a must-read for the leaders of today … and tomorrow!

Jerry Ross

President, National Entrepreneur Center

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Excerpt from “Anarchy, Inc.” (which is being released in March 2018)

The Internet of Things (IoT)

The collapsing cost structure in the data space has fueled the entire technological revolution. Data was the first step. Processing, transmitting, and storing data became cheaper and cheaper. As a result, it made economic sense to measure and record more and more data. Businesses started adding sensors to the products they sold. As the volume of sensors increased, their cost naturally dropped, further accelerating the trend.

The term “Internet of Things” (IoT) gained traction in 2013. It refers to the explosion of Internet-connected sensors in devices we use every day. Some call it machine-to-machine (M2M) and there are estimates that we’ll have over fifty billion devices connected to the Internet by 2020.

In most cases, these sensors are incorporated into devices by their respective manufacturers. These devices include smartphones, cars, appliances, homes, and buildings, and the resulting data-driven insights are delivered (to both manufacturers and device owners) on mobile apps and other user interfaces.

Jeff Bezos, founder and CEO of Amazon, once said, “The only thing that’s disruptive is customer adoption.” If people adopt new technology, it becomes disruptive. If they don’t, disruption doesn’t happen. So, do people use the data-driven insights provided by IoT sensors? Some certainly do, but many do not.

The Industrial Internet of Things (IIoT) refers to the increasing use of sensors in industrial settings. Many industrial machines come fully equipped with sensors, and ERP (enterprise resource planning) providers are adding application programming interfaces (APIs) to allow data to flow directly into their digital systems. Whether or not these capabilities are leveraged is up to the customers. Once again, some certainly do, but many do not.

Make it a priority to exploit the capabilities you already have access to. Data-driven insights are increasingly available from the industrial machines and consumer devices we use. The easiest way to improve performance is to familiarize yourself with the capabilities already available on these devices.

It’s always interesting to analyze your industry and see who’s exploiting capabilities first. Early adopters generally have support and encouragement from owners and top executives. Map out a path that these innovations normally take in your industry. See who’s first and who’s last. Where do you sit on that list?

The IoT and IIoT have already had huge implications in manufacturing. I mentioned predictive maintenance earlier. Machines can be outfitted with sensors that measure temperature and vibration, for example, allowing algorithms to anticipate problems before they occur. That allows companies to do maintenance on their own terms and avoid unexpected work stoppages.

Rolls Royce builds the huge jet engines that propel the double-decker Airbus A380 airplane. Those “Trent” engines are outfitted with dozens of sensors, including cameras that operate at 2,000°C while the plane is still in flight, allowing maintenance crews to diagnose problems and proactively service engines before major problems arise.

According to their 2012 annual report, Rolls Royce actually earns more than half of its profits from lucrative maintenance contracts with airlines all around the world. In fact, they bundle an incredible 92% of Trent engine sales with service contracts. By monitoring engines while the planes are still flying, they can have maintenance crews ready to go when the planes touch down. They can service the engines at a lower cost than the airlines while still earning a healthy profit.

The opportunities for optimization are endless. The city where I live recently installed garbage cans that monitor how much garbage is inside. They only get emptied when they’re full, not according to some arbitrary schedule, where some are still empty while others are overflowing. The result is a more efficient and effective garbage removal system.

Any task that’s completed according to an arbitrary schedule is an opportunity for optimization. Find ways to incorporate sensors, allowing for an only-when-needed servicing schedule. It’s a quick way to save time and reduce employee wages.

What other data do you have access to? Who else could benefit from that data? Find ways to leverage the data available to you. Could you anonymize the data and sell it in non-competitive markets? Could you start a new business model and leverage the data yourself?

INRIX is a mobile traffic app. When analyzing traffic data, the developers realized that the data could be used to measure economic activity, particularly around shopping malls. They now sell their INRIX Global Traffic Scorecard for over 1,000 cities around the world, helping hedge fund managers and business analysts predict economic activity and quarterly earnings figures for publicly traded companies.

What data is inherent in your business but easy to overlook because it doesn’t seem relevant? Two of the lessons in big data: First, there is data in nearly everything. And second, in sufficient quantities, even apparently silly or innocuous data can suddenly become incredibly insightful. It all starts by asking the right questions.

As mentioned earlier, we’ve seen big data implications in the distribution sector. Delivery routes are being optimized and processes streamlined. We’ve seen significant applications in the mining sector. Oil and gas exploration is being redefined by thousands of sensors in the ground, with algorithms building sophisticated models of underground geology. Refineries are using sensors to become more efficient, and the trend is occurring in renewables including solar and wind energy as well.

This is important because fossil fuels are a classically “mature” industry. Insiders have a saying: “We’ve already gotten the easy oil.” Without the combination of IoT sensors (mounted directly on drill bits, among other places), new fracking technologies (extracting high-quality and previously inaccessible shale oil), and sophisticated data-driven geological modeling, the industry would simply run dry. It’s these emergent tools that are allowing producers to drill with unprecedented precision and continued productivity.

Another fascinating application is in facilities management. Many commercial office buildings have literally thousands of sensors measuring temperature, pressure, humidity, and light, among other things. “Smart buildings” and building information modeling (BIM) systems are optimizing energy usage, water, HVAC, and security requirements.

The Hudson Yards residential real estate development in New York City is a truly smart neighborhood. The buildings are full of sensors monitoring everything from energy and water usage to air quality and noise levels. New York University’s Center for Urban Science and Progress (CUSP) is analyzing the data, looking for new efficiencies in sustainable living. The United Arab Emirates (UAE) is building Masdar City in Abu Dhabi, another ambitious sustainable-living community, and there are many similar projects around the world.

Data is also being aggregated into the smart grid. “Smart cities” are collecting data in countless areas, allowing them to optimize resources across a broad spectrum of services. Already today, Singapore uses their Electronic Road Pricing system to streamline traffic and citizens’ smartphones to sense potholes, allowing authorities to schedule road maintenance. Other cities including London, Tokyo, Berlin, and Toronto are equally creative. We’re optimizing our planet, and the IoT is an enabling technology. Sensors collect the data that makes optimization possible.