Category Archives: Thought Leadership

Daring Greatly in 2021

A small group of innovators spent 2021 in the Arena addressing Agriculture 4.0 issues including a rapidly growing population, the scarcity of natural resources, climate change, and food waste using 5G and Industrial Internet of Things technologies.

Let’s start by looking at the problems:

With Earth’s population nearing 10 billion by 2050, growers must double food production to keep up. In other words, growers must produce more food than has been produced since the beginning of farming.

It turns out there are a few headwinds that make this seemingly impossible task even more daunting.

Climate change takes credit for an unprecedented heat wave during the summer of 2021 that wiped out or severely diminished many varieties of fruits and vegetables. Cherries, raspberries, blueberries, and blackberries shriveled-up or burned, putting family farms at risk. Apples and wine grapes stopped growing leading to a smaller harvest. Internal cellular damage to certain fruits and vegetables will carry over into next year’s growing season leading to additional small harvests. Unrelenting floods drove damaging fungal growth. Warmer winters confuse budding plants, while late spring frost can kill those that started growing too early.

Extreme heat and flooding leads to less food.

Most of the Western United States is experiencing extreme drought due to the lack of rainfall combined with evaporative heat, as the Colorado river struggles to deliver enough water to the 40 million people that depend on it. America’s two biggest reservoirs, Lake Mead and Powell, are now at historically low water levels. Since agriculture uses 70% of all freshwater, we find ourselves in a desperate situation.

Reduced water leads to less food.

The Earth is losing almost 30 million acres of arable land each year due to a phenomenon known as desertification. In fact, We’ve lost 33% of all arable land over the last 40 years. Primary culprits include urbanization and deforestation along with farming and ranching that lead to overcultivation, overcropping, and overgrazing. Soil is eroding and turning into lifeless dirt due to drought and poor farming practices such as tilling which depletes soil nutrients.

Less land leads to less food.

Exceptionally dry forests combined with a variety of ignition sources has led to widespread fires. Sadly, the term, “Fire Season” is now a thing. Smoke threatens people and livestock, while fire threatens agricultural lands.

Burned land leads to less food.

A workforce is required for planting, maintaining, and harvesting fruits and vegetables as well as operating and maintaining farm equipment. The current labor shortage is at a crisis level with growers losing crops and income as fresh produce is left rotting in the field because there aren’t enough workers for harvest.

A reduced workforce leads to less food.

Energy usage accounts for roughly 15% of total farm expenditures and comes from operating farm machinery, trucks, processing warehouses, tractors, irrigation pumps, HVAC, ATVs, crop dryers, packing houses, and cold storage. As costs for electricity, gasoline, propane, and diesel increase, grower operating margins decrease making it harder to stay in business.

High energy costs lead to less food.

Between 30 and 40 percent of food, about 1.3 billion tons, is wasted after harvest every year due to disruptions in the supply chain. This disruption creates gaps between production and distribution leading to the loss of perishable items like eggs, milk, and produce in the face of unprecedented need at food banks. Improper temperatures at different parts of the cold chain lead to reduced quality or complete food loss. Lean, just-in-time supply chains, based on buyer behavior leave no room for error.

Supply chain disruptions lead to less food.

Let’s look at the solution we worked on in 2021:

While many of you know about Industrial IoT, Digital Twins, and Industry 4.0, you may not have heard about Agriculture 4.0. I spent 2021 with partners Courtney Latta and Doug Boling building technology code named “Thunderstruck” that implemented the same kind of IIoT capabilities used in a Smart Factory to facilitate sustainable Agriculture.

As a refresher, the Internet of Things uses devices, sensors, and connectivity to allow you to remotely know the state, performance, or health of an object in real-time. In our case, we needed apples, hops, soil, air, equipment, and water to talk to us and lets us know how they’re doing. With that data and a little bit of analytics, we could help growers make more informed farming decisions to drive desired outcomes such as:

  • Reduced water usage (save money and environment)
  • Increased crop quality (make money)
  • Reduced energy usage (save money and environment)
  • Reduced fertilizer usage (save money and environment)
  • Increased crop yields (make money)
  • Reduced pesticide usage (save money and environment)
  • Reduced labor costs (save money)
  • Improved food traceability (reduce food waste)
  • Increased crop protection from frost, heat, or disease (make money)
  • Increased worker safety (employee well being)
  • Reduced herbicide usage (save money and environment)
  • Increased automation (save money)
  • Increased equipment uptime (save money and make money)

What did we do during the growing season?

We combined battery-powered devices and sensors measuring soil moisture, temperature, air quality, humidity, location, and others with 5G cellular connectivity and our portable Software as a Service (SaaS) platform that can run in the Cloud or at the Edge. Our goal was to validate the MVP of our product. During pilots and trials on large farms in Eastern Washington, we deployed devices and sensors throughout acres of hops and apples. At regular intervals, those devices wirelessly transmitted their telemetry data to our platform. Sometimes we analyzed the captured data with our analytics, and other times our platform routed the data to the grower’s analytics systems to derive insights.

What was the outcome?

In the end, Courtney, Doug, and I found our product/market fit and validated Thunderstruck with happy customers who willingly signed letters of intent. Heck, we even won the “IoT Innovation of the Year for Agriculture” award from Compass Intelligence. I got to wear a cowboy hat and boots and spent the summer of 2021 amongst trees, crops, and soil. Obviously, we barely scratched the surface in the value the technology we built can provide. We also have no illusions that the precision agriculture we delivered can solve all the problems faced by growers and society at large. Battling climate change, drought, fires, desertification of soil, floods, and food waste is an “all-hands-on-deck” calling for everyone. While we are only part of the solution, the satisfaction and fulfillment we felt this year is immeasurable, and we’re happy to play our part.

Keep in mind that sustainably optimizing operations out in the fields and orchards is only half the battle. After harvest, crops are processed in giant warehouses and buildings in a fashion that looks remarkably like process manufacturing. Sensors, cameras, actuators, and automation have a big role to play there to ensure the availability of machines and the proper temperatures for food storage. While we’ve focused on smart manufacturing to deliver private 5G, there is an even larger opportunity to equip the world’s food processing facilities with reliable wireless to connect and monitor machines. Throughout the farm-to-market supply chain, anywhere from 30-40% of food is lost and/or wasted. Using digital twins, a long digital thread will be created to trace food conditions and source nutrients from farm to table. No one should go hungry, and we aim to make a positive impact in this area.

In the video below, Courtney Latta discusses the value the Thunderstruck brings to farming with Dan Maycock from Loftus Ranches. 

I think we’ve barely scratched the surface of the value Thunderstruck will deliver globally to large megafarms, vertical farming, the food supply chain, greenhouses, and the small, mom and pop farms that form the backbone of our society. I’m super-grateful to my partners Courtney Latta and Doug Boling for making this a reality.

The Future of Agriculture is the Future of Humanity

Digital Twin Prototypes and Models

The digital twin prototype is created before the physical twin and the digital twin model is used to define a type of entity or process.

What is a Prototype?

The digital twin prototype is typically comprised of engineering designs, processes, relevant analysis, and a visual representation to create the physical product or process. As you might imagine, it’s faster and cheaper to create and test a digital product than a physical one. Digital mistakes and iterations are definitely less costly or painful.

Models let you Define Once and Reuse Many

The digital twin model allows you define all aspects of a type of entity just once, rather than defining it repeatedly for each individual entity in your IoT platform. The model includes baseline information including a name, description, a variety of properties, one or more pictures or CAD models, and a version number because the model may evolve over its lifetime. If you’re on object-oriented software developer, you can think of this as a base class comprised of one or more properties. Unique, individual entities are referred to as digital twin instances which I’ll cover in a later article. Each instance of a digital twin derived from a digital twin model will inherit its properties. You can best think of a digital twin model as a data definition, structured in a database or file. A composable digital twin model is created using a visual designer in an application or through a domain-specific programming language designed to create the proper data model. An example of a digital twin model might be a 2022 Ford F-150 with a specific set of features as properties. Thousands of actual 2022 Ford F-150s on the road that inherit the specific feature set from the model would be the digital twin instances.

More to Come

Follow along with me as I take you on a deep dive of all the elements that come together to make a digital twin. Click links below to catch up with other articles in the series:

  • Digital Twins Defined
  • Telemetry Properties of a Digital Twin Model
  • Static Properties of a Digital Twin Model

Digital Twins Defined

What are digital twins and where did they come from?

What are they?

A digital twin is a digital representation of a physical object, process, place, person, or system that sits at the intersection of connectivity, data and analytics. At a high level, the digital twin concept is comprised of three parts. The physical space, the digital space, and the connection between the two. Digital twins are used in both design, simulation, and operational phases of a product or system lifecycle. Design is done digitally before physical creation occurs, which saves an organization money because they avoid costly, physical mistakes. Digital simulations are performed by feeding test data to see how digital twins react. Think of this as a digital version of a wind tunnel to test the aerodynamics of car. Operationally, data populates the digital twin with the physical counterpart’s current state and behavior. This allows you to observe both the current, and historical state of the physical twin. Further context is derived when additional data from a variety of sources are blended with the digital twin. Software agents compare incoming data from the physical twin with expected state values and KPIs of the digital twin to trigger further analysis and actions. Oftentimes you’ll see hear that digital twins are a 3D CAD model or what you see when wearing AR or VR goggles. Keep in mind that those are actually a view of the digital twin’s data model combined with live data from the physical twin. The view of a digital twin could just as easily be an Excel spreadsheet or exploration through the metaverse.

Where did they come from?

If you remember watching the movie Apollo 13, you saw how Tom Hanks, Kevin Bacon, and Bill Paxton were struggling to get home from the moon in their disabled spacecraft after an oxygen tank exploded. Yes, this happened in real life. You might also remember Gary Sinise troubleshooting the problem in a physical twin of the spacecraft at mission control in Houston. Luckily, through ingenuity and perseverance, the astronauts safely returned to Earth. The learnings from this event gave rise to the idea of creating a high-fidelity, digital replica of spacecraft to make it easier to troubleshoot problems in the future. In the early 1990s, the concept of digital twins were anticipated by David Gelernter’s book, “Mirror Worlds.” In 2002, Dr. Michael Grieves introduced the idea of a “Doubleganger” as part of product lifecycle management (PLM) while he was at the University of Michigan. Dr. Grieves went on to say, “Industry 4.0 is only possible with the digital twin.” In 2010, the name “digital twin” finally stuck when John Vickers of NASA referred to the name in an official roadmap report. Throughout the 2000s, I witnessed the slow rise of digital twins in manufacturing showcased by companies like GE and at events like Hannover Messe.

Asset Avatars

In 2016, while serving as CTO at Hitachi Insight Group, it occurred to me that digital twin technology should be at the very heart of the industrial IoT platform we were creating. Collaborating with members of the product management and engineering teams, we created an industrial digital twin technology called “Asset Avatars” running within the Lumada platform. The Asset Avatars could model machines, subsystems, assembly lines, and entire factories. We literally brought machines and processes to life in a virtual world to enhance operational efficiency, provide early warning of problems, an ensured uptime of Hitachi assets such as bullet trains and wind turbines. Since then, I’ve been a huge proponent of this powerful technology.

More to Come

Follow along with me as I take you on a deep dive of all the elements that come together to make a digital twin. Click links below to catch up with other articles in the series:

  • Digital Twin Prototypes and Models
  • Telemetry Properties of a Digital Twin Model
  • Static Properties of a Digital Twin Model

Looking Back at 2020: A Year of Digital Resiliency

When I think about the things that held the planet together in 2020, it was digital experiences, delivered over wireless connectivity, that made remote things local.

While heroes like doctors, nurses, first responders, teachers, and other essential personnel bore the brunt of the COVID-19 response, billions of people around the world found themselves cut off from society. In order to keep people safe, we were physically isolated from each other. Far beyond the six feet of social distancing, most of humanity weathered the storm from their homes.

And then little by little, old things we took for granted, combined with new things many had never heard of, pulled the world together. Let’s take a look at the technologies and trends that made the biggest impact in 2020 and where they’re headed in 2021:

The Internet

The global Internet infrastructure from which everything else is built is an undeniable hero of the pandemic. This highly-distributed network designed to withstand a nuclear attack performed admirably as usage by people, machines, critical infrastructure, hospitals, and businesses skyrocketed. Like the air we breathe, this primary facilitator of connected, digital experiences is indispensable to our modern society. Unfortunately, the Internet is also home to a growing cyberwar and security will be the biggest concern as we move into 2021 and beyond. It goes without saying that the Internet is one of the world’s most critical utilities along with water, electricity, and the farm-to-table supply chain of food.

Wireless Connectivity

People are mobile and they stay connected through their smartphones, tablets, in cars and airplanes, on laptops, and other devices. Just like the Internet, the cellular infrastructure has remained exceptionally resilient to enable communications and digital experiences delivered via native apps and the web. Indoor wireless connectivity continues to be dominated by WiFi at home and all those empty offices. Moving into 2021, the continued rollout of 5G around the world will give cellular endpoints dramatic increases in data capacity and WiFi-like speeds. Additionally, private 5G networks will challenge WiFi as a formidable indoor option, but WiFi 6E with increased capacity and speed won’t give up without a fight. All of these developments are good for consumers who need to stay connected from anywhere like never before.

Web Conferencing

With many people stuck at home in 2020, web conferencing technology took the place of traveling to other locations to meet people or receive education. This technology isn’t new and includes familiar players like GoToMeeting, Skype, WebEx, Google Hangouts/Meet, BlueJeans, FaceTime, and others. Before COVID, these platforms enjoyed success, but most people preferred to fly on airplanes to meet customers and attend conferences while students hopped on the bus to go to school. In 2020, “necessity is the mother of invention” took hold and the use of Zoom and Teams skyrocketed as airplanes sat on the ground while business offices and schools remained empty. These two platforms further increased their stickiness by increasing the number of visible people and adding features like breakout rooms to meet the demands of businesses, virtual conference organizers, and school teachers. Despite the rollout of the vaccine, COVID won’t be extinguished overnight and these platforms will remain strong through the first half of 2021 as organizations rethink where and when people work and learn. There’s way too many players in this space so look for some consolidation.


“Stay at home” orders and closed businesses gave e-commerce platforms a dramatic boost in 2020 as they took the place of shopping at stores or going to malls. Amazon soared to even higher heights, Walmart upped their game, Etsy brought the artsy, and thousands of Shopify sites delivered the goods. Speaking of delivery, the empty city streets became home to fleets FedEx, Amazon, UPS, and DHL trucks bringing packages to your front doorstep. Many retail employees traded-in working at customer-facing stores for working in a distribution centers as long as they could outperform robots. Even though people are looking forward to hanging out at malls in 2021, the e-commerce, distribution center, delivery truck trinity is here to stay. This ball was already in motion and got a rocket boost from COVID. This market will stay hot in the first half of 2021 and then cool a bit in the second half.

Ghost Kitchens

The COVID pandemic really took a toll on restaurants in the 2020, with many of them going out of business permanently. Those that survived had to pivot to digital and other ways of doing business. High-end steakhouses started making burgers on grills in the parking lot, while takeout pizzerias discovered they finally had the best business model. Having a drive-thru lane was definitely one of the keys to success in a world without waiters, busboys, and hosts. “Front of house” was shut down, but the “back of house” still had a pulse. Adding mobile web and native apps that allowed customers to easily order from operating “ghost kitchens” and pay with credit cards or Apple/Google/Samsung Pay enabled many restaurants to survive. A combination of curbside pickup and delivery from the likes of DoorDash, Uber Eats, Postmates, Instacart and Grubhub made this business model work. A surge in digital marketing also took place where many restaurants learned the importance of maintaining a relationship with their loyal customers via connected mobile devices. For the most part, 2021 has restauranteurs hoping for 100% in-person dining, but a new business model that looks a lot like catering + digital + physical delivery is something that has legs.

The Internet of Things

At its very essence, IoT is all about remotely knowing the state of a device or environmental system along with being able to remotely control some of those machines. COVID forced people to work, learn, and meet remotely and this same trend applied to the industrial world. The need to remotely operate industrial equipment or an entire “lights out” factory became an urgent imperative in order to keep workers safe. This is yet another case where the pandemic dramatically accelerated digital transformation. Connecting everything via APIs, modeling entities as digital twins, and having software bots bring everything to life with analytics has become an ROI game-changer for companies trying to survive in a free-falling economy. Despite massive employee layoffs and furloughs, jobs and tasks still have to be accomplished, and business leaders will look to IoT-fueled automation to keep their companies running and drive economic gains in 2021.

Streaming Entertainment

Closed movie theaters, football stadiums, bowling alleys, and other sources of entertainment left most people sitting at home watching TV in 2020. This turned into a dream come true for streaming entertainment companies like Netflix, Apple TV+, Disney+, HBO Max, Hulu, Amazon Prime Video, Youtube TV, and others. That said, Quibi and Facebook Watch didn’t make it. The idea of binge-watching shows during the weekend turned into binge-watching every season of every show almost every day. Delivering all these streams over the Internet via apps has made it easy to get hooked. Multiplayer video games fall in this category as well and represent an even larger market than the film industry. Gamers socially distanced as they played each other from their locked-down homes. The rise of cloud gaming combined with the rollout of low-latency 5G and Edge computing will give gamers true mobility in 2021. On the other hand, the video streaming market has too many players and looks ripe for consolidation in 2021 as people escape the living room once the vaccine is broadly deployed.


With doctors and nurses working around the clock as hospitals and clinics were stretched to the limit, it became increasingly difficult for non-COVID patients to receive the healthcare they needed. This unfortunate situation gave tele-medicine the shot in the arm (no pun intended) it needed. The combination of healthcare professionals delivering healthcare digitally over widespread connectivity helped those in need. This was especially important in rural areas that lacked the healthcare capacity of cities. Concurrently, the Internet of Things is making deeper inroads into delivering the health of a person to healthcare professionals via wearable technology. Connected healthcare has a bright future that will accelerate in 2021 as high-bandwidth 5G provides coverage to more of the population to facilitate virtual visits to the doctor from anywhere.

Working and Living

As companies and governments told their employees to work from home, it gave people time to rethink their living and working situation. Lots of people living in previously hip, urban, high-rise buildings found themselves residing in not-so-cool, hollowed-out ghost towns comprised of boarded-up windows and closed bars and cafés. Others began to question why they were living in areas with expensive real estate and high taxes when they not longer had to be close to the office. This led to a 2020 COVID exodus out of pricey apartments/condos downtown to cheaper homes in distant suburbs as well as the move from pricey areas like Silicon Valley to cheaper destinations like Texas. Since you were stuck in your home, having a larger house with a home office, fast broadband, and a back yard became the most important thing. Looking ahead to 2021, a hybrid model of work-from-home plus occasionally going into the office is here to stay as employees will no longer tolerate sitting in traffic two hours a day just to sit in a cubicle in a skyscraper. The digital transformation of how and where we work has truly accelerated.

Data and Advanced Analytics

Data has shown itself to be one of the world’s most important assets during the time of COVID. Petabytes of data has continuously streamed-in from all over the world letting us know the number of cases, the growth or decline of infections, hospitalizations, contact-tracing, free ICU beds, temperature checks, deaths, and hotspots of infection. Some of this data has been reported manually while lots of other sources are fully automated from machines. Capturing, storing, organizing, modeling and analyzing this big data has elevated the importance of cloud and edge computing, global-scale databases, advanced analytics software, and the growing importance of machine learning. This is a trend that was already taking place in business and now has a giant spotlight on it due to its global importance. There’s no stopping the data + advanced analytics juggernaut in 2021 and beyond.


2020 was one of the worst years in human history and the loss of life was just heartbreaking. People, businesses, and our education system had to become resourceful to survive. This resourcefulness amplified the importance of delivering connected, digital experiences to make previously remote things into local ones. Cheers to 2021 and the hope for a brighter day for all of humanity.

Teddy Roosevelt

The Man in the Arena

On April 23, 1910, Rough Rider President Theodore Roosevelt gave one of the most powerful speeches of his life at the Sorbonne in Paris.

I first learned about this great man when I was growing up and visited the Menger Hotel in San Antonio where Roosevelt recruited the Rough Riders. I certainly feel gratitude for this conservationist and naturalist every time I visit one of America’s national parks for forests. The speech he gave in Paris after his presidency has made a lasting impact on me and many others.

The poorest way to face life is to face it with a sneer,” Roosevelt said as he railed against cynics who looked down at men who were trying to make the world a better place. “A cynical habit of thought and speech, a readiness to criticize work which the critic himself never tries to perform, an intellectual aloofness which will not accept contact with life’s realities—all these are marks, not … of superiority but of weakness.”

“It is not the critic who counts; not the man who points out how the strong man stumbles, or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.”

Your Next Employee Might be a Digital Twin Powered by an AI Bot

A company is a collection of roles filled by people who are assisted by machines, networks, and software to accomplish tasks needed to achieve the goals of the organization. Can these roles be represented by Digital Twins and can AI Bots carry out their activities?

Back in the Summer of 2019, I wrote a provocative article on Digital Transformation where I put forward the concept of using digital twins to represent employees and the use of APIs to illustrate interactions between them. While it’s normal to represent company assets and business processes as digital twins, employees were something new. Building on my recent series of articles discussing digital twins, I will explore this concept further and show you how to make it a reality.

Companies don’t just hire employees. Instead, one or more tasks to be accomplished are identified and potentially encapsulated within something called a role. The tasks may represent a one-time event or may be something that’s done repeatedly. Similar to the owner’s manual of a new car, a collection of repeating tasks that make sense to be performed by a particular role is often illustrated by something called a job description. This helps with the matchmaking process of connecting interested people possessing the right skills with the role containing the tasks to be accomplished.

Depending on the goals of the company, roles could include things like product development, sales, delivery driver, people manager, marketing, product planner, secretary, executive management, accounts payable, web developer, waiter, and thousands more.

To go beyond a simple job description, how do you codify what it means to be an employee in one of these roles? What are the baseline attributes of a role that apply to all employees serving in that same role? What intelligence or skill set does an employee taking on such a role need to possess? What is the list of one-time or ongoing tasks that the role requires an employee to accomplish? What do the process steps of those tasks look like? Who are the people, systems, and organizations this role must interact with? What kinds of information should the employee serving in this role expect to receive? Similarly, what kinds of information should this same employee expect to send? Based on tasks to be accomplished and interactions with others, what results or outcomes are expected to be derived? What are the unique attributes of a distinct employee serving in a given role? What can be learned from one employee serving in a particular role that can be applied to other employees serving in the same role? How is a role related to larger grouping concepts like teams, divisions, business units, and geographies within an organization? For that matter, what about relationships with external organizations and the employees serving in their roles? Last but not least, how are all the actions, outcomes, and learning from an employee serving in a role during a period of service or throughout an entire career captured in order to improve that role?

Luckily, the questions asked above can be answered by digital twins and the function of human resources within a company can be revolutionized. Let’s walk step-by-step through the questions posed above and bring their answers to life via different aspects of digital twins.

Q: How do you define a role within an organization? Let’s use the role of “waiter” for example, since it’s familiar to most people.

A: A role is defined in an organization through the creation of a digital twin model. This concept and structure define the high-level type or class of a role rather than the individual instances of the role. In other words, it defines what a waiter at a particular restaurant is supposed to be, as opposed to the unique characteristics of each individual waiter. Using a car as an example in the digital world of the Internet of Things, a digital twin model might represent a 2015 Volvo XC 60.

Q: How are the attributes of this role codified?

A: A digital twin model can have one or more static properties that help to define the characteristics of the role. They’re basically the list of attributes which can be long or short and contain everything you need to digitize the role’s job description. The waiter wears a particular uniform. The waiter must be courteous. The waiter must be able to memorize orders. The waiter has to carry a tray full of heavy plates. You get the idea. In the digital world of the Internet of Things, the Volvo XC60 would have static properties like the length of the car.

Q: How would the attributes of a specific waiter be captured?

A: The individual waiter would be defined via a digital twin instance that inherits attributes from the digital twin model representing the waiter role. In this case, static properties are assigned specifically to the digital twin of the specific waiter. The waiter’s name is Jane Doe. This waiter is unavailable to work on Tuesdays and Thursday due to college classes. Our Jane Doe waiter would inherit the need to memorize food and drink orders from the same digital twin model static property to applies to all waiters. In the digital world of the Internet of Things, an individual instance of the Volvo XC60 would have static properties like the color of the car.

Q: Who are the people, systems, and organizations this role must interact with?

A: Using static properties, the digital twin model representing the waiter role would list things like host/hostess, customers, kitchen, manager, bartender and others as appropriate.

Q: How do you define the kind of information this role should expect to receive?

A: In the world of digital communications, the data received from another entity is often referred to as telemetry. An example would be NASA mission control receiving heart rate telemetry from an astronaut. In the case of the waiter, one or more telemetry properties would be used to define the wide variety of information the waiter role should expect to receive. The waiter is told to seat customers at a particular table by the host. The customer orders food and beverages. The kitchen lets the waiter know that food is ready. While digital systems would also need to know things like data types and units of measure for incoming data, a person serving in this role is parsing strings they see or hear using their mind to determine meaning. In the digital world of the Internet of Things, the Volvo XC60 might have a telemetry property like left front tire pressure whose current value we would know because of a pressure sensor. Don’t worry, I’m not trying to turn people serving in a given role into robots. This is just a growing list of things a person should expect to see and hear while doing their job.

Q: How do you define information this role is expected to send?

A: The opposite of receiving telemetry in the world of digital communications is sending a command. Using a NASA example again, mission control would send a command to a deep space probe telling it to change its course. One or more command properties would be used to define the types of requests and information the waiter roles should expect to send. The waiter tells the customers the specials of the day. The waiter asks the customers what they would like to order. The waiter asks the bartender for a particular drink. As with telemetry properties, this is a growing list of things the person should ask for while doing their job. In the digital world of the Internet of Things, the Volvo XC60 might be able to receive a command to remotely turn on the car.

Q: How are required tasks this role must accomplish enumerated?

A: Tasks are listed in the digital twin model using process properties. They represent a process to follow in order to complete the specified task. For instance, the waiter takes food and drink orders from customers. The waiter gives the food orders to the kitchen and drink orders to the bar. The waiter brings out food and drinks and knows where on the table to place each item. The waiter uses the point-of-sale machine to charge a customer’s credit card for the meal. Any task or interaction requiring multiple steps to complete will be defined by an associated process property.

Q: How are these processes further described to ensure success in completing a task?

A: Each process property in a digital twin model is linked to one or more process steps. They enumerate a list of steps taken in a linear or sometimes a branching, non-linear sequence in order to achieve a desired outcome. For instance, these properties would list the steps required to capture a food and drink order from customers and enter that order in a point of sale terminal for electronic delivery to the kitchen and bar. Every step in a process needed to complete the given task will contain as much information as necessary. In the digital world of the Internet of Things, the process for irrigating a farm might include testing soil moisture with a sensor and calling a weather API to see when it’s expected to rain next.

Q: How can all the interactions and activities from an employee serving in a role during a period of service be captured in order to improve that role or help other employees serving in the same role?

A: The historical record of what happens to an instance of a digital twin throughout its entire lifecycle, is represented by something called a digital thread. In the digital world of the Internet of Things, the data needed to create the digital thread over time would automatically come from sensors and microcontrollers sending telemetry along with manually added events. In this case, we’re talking about the waiter so things will be a little different. As you might imagine, the restaurant’s point of sale system will record every instance of the waiter placing orders and getting paid and so these activities will be captured digitally and associated with telemetry and command properties. The waiter can manually capture unusual or extraordinary events that took place while on the job on a weekly basis. Likewise, the waiter’s manager can manually capture observations of the waiter’s performance. These manual entries are referred to as digital thread events. Either way, all this data will be captured to provide a current and historical view for both real-time and batch analysis. Through this analysis, ways to change or improve the way the role works will reveal itself not only for this waiter, but potentially all the other waiters at the restaurant.

Q: How is a role related to larger grouping concepts like teams, divisions, business units, and geographies within an organization?

A: Physical entities like people and machines don’t live in a vacuum, they operate in larger systems of systems with relationships and interactions with other entities. The digital twin representing the waiter role belongs to a larger digital twin called “Front of House” which represents a collection of waiter and host digital twins. It looks like a group with superpowers where there’s a parent/child relationship between itself and the waiters. The “Front of House” digital twin belongs to a larger digital twin called “Restaurant.” While simple groups are a great organizational tool, a digital twin that looks like a group will have rich properties and capabilities that make it more valuable. In the digital world of the Internet of Things, an assembly line represented by a digital twin would contain a collection of industrial robots represented by their own digital twins, thus providing a richer “group view” for analysis.

Digital twins have proven their value over the years in manufacturing and for organizations like NASA. As you can see from the human example above, digital twins will not only be invaluable to a company and its human resources department, they’ll be a constant companion to each employee serving in a role that’s described and managed by a digital twin. Everything an employee needs to know, everyone she needs to interact with, and every process she needs to follow to accomplish her tasks are digitally by her side via her role’s digital twin. Every experience she has and everything she learns on the job is captured in a digital thread. All this is easily accessible from mobile devices, the web, or via APIs.

So where do AI Bots come in the picture?

Depending on the job description, it’s quite possible that a role defined by a digital twin model could be brought to life by a bot instead of a human. The more digital the tasks to be accomplished are, the more likely a bot could pull it off.

What is a bot? It’s a software agent with varying degrees of intelligence that can run autonomously and interact with people as well as other bots and software systems. No, I’m not going to dive into chatbots from your favorite chat app or robotic process automation (RPA) trying to automate Win32 apps without APIs on your desktop. A bot is software running on PCs, servers, in the cloud, and on smartphones, tablets, and even Raspberry Pis.

I’m talking about a combination of three things:

  • Pure software or cyber physical systems that accomplish tasks important to the organization and can be manipulated through open APIs. Clearly, software systems running on computers are prime opportunities for this type of automation. Thanks to the Internet of Things, a much larger world of machines and environmental systems are also open to automation from bots.
  • Resilient bots with local and remote network access to the APIs of these systems. A bot needs to be able to see and manipulate APIs to perform digital interactions. Keep in mind that you can grow your list of legacy apps and systems to be automated via bots by wrapping them in RESTful APIs. I’m talking decades-old COBOL on mainframes and all the Windows/Windows Server apps built in the 1990s and 2000s.
  • A digital twin model that uses its static, telemetry, command, virtual, and process properties to provide an instruction manual for bots to follow. In the same way that the digital twin model told the waiter how to perform her job, a bot will do the same thing in the digital or cyber physical world.

The bot becomes a digital twin instance as well as an employee of the company. Yes, the bot shows up as an employee with a list of skills by the Human Resources department. Yes, the bot is a User in Active Directory and part of Groups consisting of people.

Remember what I said at the beginning of this article. Companies don’t just hire employees. They encapsulate one or more tasks to be accomplished within something called a role. More times than not, people will fill those roles. But sometimes, a bot brings a digital twin to life and can accomplish enough tasks to fill a growing number of roles in your organization.

It’s time for the Human Resources department to dramatically expand its scope and change its name to the Human + Digital Resources department. The digital transformation of your company actually belongs to this group, not IT.

Think about it.

IoT Smoke Detector

For most people, the home smoke detector is the first Internet of Things IoT sensor/device they’ve ever come in contact with.

It may or may not CONNECT to a remote monitoring service over the Internet.


It ANALYZES the air looking for smoke using either an ionization chamber or photo-detector.

It ACTS on the insights derived from this analysis and sounds an alarm if smoke is detected. The battery on this device typically lasts a year before the annoying beeping sound begins.

Amazingly, this self-contained IoT device and analytics platform doesn’t require mythical AI powers to deliver value to the customer.

Don’t overthink it.

Stirring the Pot with my First Principles

Today I thought I’d cause a little controversy by comparing my First Principles (things I know to be true) related to building high performance, scalable systems (software, compute, storage and networking) with current conventional wisdom.

Among most architects today, current conventional wisdom states that your architecture must follow a microservice software pattern, use containers like Docker, capture data in an event streaming platform like Kafka, persist data in a NoSQL database, be managed by something like Kubernetes or Mesosphere and run on Linux if you want to have a high performance, scalable system.

So what is something I know to be true from the dot com era?

Back then I was fortunate enough to be part of a team that built an energy trading platform that allowed multiple counterparties to buy and sell financial instruments (think NASDAQ). This platform was built on 32-bit Windows Server 2000 with some kind of Intel Pentium CPU. Classic Active Server Pages would send and receive XML fragments over HTTPS to and from brokers from every major energy company + NYMEX and the Intercontinental Exchange (ICE) following a RESTful API pattern. Incoming data was queued in MSMQ and free-threaded objects pooled inside Microsoft Transaction Server (MTS) moved that data in and out of 32-bit SQL Server 2000. The databases were clustered and Windows Server got 1 GB of RAM whereas SQL Server got most of the remaining 3 GB of RAM. The Internet Information Servers running ASP 3.0 used the built-in network load balancing service (NLB). When we needed to update the software, we just took the servers out of the cluster one by one to make the update. No biggie.

So what’s the takeaway from all this? As someone who has played a prominent role in the Internet of Things space over the years, I struggle to find modern systems that have to deal with the transactional and analytical load that I witnessed with the system I helped build 20 years ago. In spite of all the promise and talk, I’ve yet to see any IoT system that deals with a fraction of the load the world’s financial systems handle effortlessly with their antiquated architectures and relational databases.

Why were we able to do so much more with so much less back then?

Remember folks, we’re just flowing current through a gate to establish a high or low voltage at a particular point in the circuit. The farther away you abstract yourself from this, the more resources your system will require and the slower the system gets. Don’t be impressed by architectural diagrams with hundreds of lines, boxes, arrows and triangles going every which way. Complexity kills. New programming languages, frameworks, and architectural patterns come along all the time. Use you best judgement and fall back to your own first principles before deciding to jump on the next bandwagon.


Digital Twin Models and Process Properties

A process is a series of actions, tasks or steps taken in a linear or sometimes a branching, non-linear sequence in order to achieve a desired outcome.

These process steps could be manual activities undertaken by a person, purely digital steps taken by software across computers, electromechanical actions between digital messages and mechanical actuators as well as advanced cyber-physical tasks performed by industrial robots.

Let’s walk through a few examples:

  • A farmer checks the weather forecast everyday then drives their truck to appropriate orchards or fields to perform irrigation for a certain amount of time based on previous rainfall totals and the needs of the crops.
  • A digital scheduling system for doctor appointments retrieves appointment time and location preferences of the patient and combines it with their insurance information and the doctor’s current schedule to deliver a range of appointment times.
  • An electromechanical system uses motion sensors to notice a person has entered a meeting room and carries out steps to turn on lights, adjust room temperature, and turn on the projector to show a presentation.
  • Cyber-physical industrial robots perform individual tasks but also have awareness of the current state of other robots on the assembly line to better work together in building a car.

Rather than just using digital twins to provide visibility to ongoing operations or to improve future product development via simulation, why not use digital twins to orchestrate processes?

Let’s dive into the simple electromechanical scenario shown above:

Imagine the familiar IoT process where you need to turn on the lights, adjust room temperature and turn on the projector when a person enters a meeting room. You’ve got a Digital Twin that represents the meeting room which acts like a group or container for a collection of digital twins that represent motion sensors, HVAC, the lighting system and the projector.

All you need now is an process automated by bots to bring this to life.

The motion sensor detects a person walking-in which triggers a software bot on the associated microcontroller to send a message to an IoT platform or building management system. Upon receiving the message, a bot identifies the particular meeting room and sends a command to activate the overhead lights. Concurrently, a bot sends a command to the HVAC system to adjust the room to a comfortable temperature. Last but not least, a bot sends a command to turn on the overhead projector so the person can deliver a presentation.

How do you orchestrate this process?

Since this is a simple process, you could probably do this with a series of rules via the event processor in your IoT platform. Knowing that processes can become more complex with many variables and different forks in the road, you could instead choose to define the orchestration in a Digital Twin Model. With it’s available process properties, this twin defines the steps needed to guide software bots in taking the actions needed to achieve the desired outcome. Each step is represented by a process property. Each process property defines the digital twins involved, the APIs needed to connect, security requirements for calling those APIs, data to be sent, return values to expect and other details needed to successfully complete the step and move on to the next one.

This doesn’t have to be a 100% digital process.

In another scenario, the prescriptive analytics from an IoT platform might alert a technician to fix a broken component. The orchestrating digital twin model would still have its process properties except this time, each step would guide a person instead of a bot to complete the required activities. In this form of guided repair, the process properties would use descriptive text to tell the technician what tools to bring, where to go, and step by step instructions to fix the component. These text based steps could be displayed on a mobile app or take a more digital form via augmented reality (AR) glasses. Once the repair was completed, a tap of a button on a mobile app might make the API call needed to let the orchestrating twin know the job is done.

Assembly Line

Digital Twins and Groups

It’s easy to think of Digital Twins as representing discrete systems & subsystems, like an industrial robot that builds a car in a factory. The reality is that physical entities like humans, machines & environmental systems don’t live in a vacuum.

They often operate in larger systems of systems with relationships & interactions with other entities. If I were to collect a number of industrial robots that work together, I might create a “group” called “assembly line.” Unfortunately, using a simple group construct would do this collection of robots a disservice. The assembly line group should actually be a digital twin itself where its telemetry, virtual, static, and command properties have defined causal relationships with all the robots that comprise this assembly line.

There’s a parent/child relationship between the assembly line twin and all the robot twins. Furthermore, there are peer relationships between all the child robots. This literally brings the assembly line to life allowing you to monitor it via your IoT platform and analytics.

Collections of assembly line digital twins can then come together to create a “composite digital twin” called a factory.


Digital Twins & Subsystems

If you’ve ever worked with an IoT platform, you might have noticed it typically has you define a simple schema or Digital Twin Model for an entire person, machine or environmental system.

If the system you intend to monitor is simple enough, then a single digital twin instance may suffice. On the other hand, if what you’re monitoring is comprised of multiple, complex subsystems, you may have to go a bit further.

For instance, an automobile is actually a system made up of many subsystems including the engine, braking, transmission, electrical, & fuel subsystems just to name a few. Depending on complexity, it stands to reason that some of those subsystems deserve to be digital twins with telemetry, virtual, static & command properties of their own. Not only would these subsystem digital twins have a parent/child relationship with the overall car, they would have causal relationships with each other. If the engine doesn’t run when you start your car, the cause could be the battery, starter or alternator in the electrical subsystem.

Defined causal relationships between the engine & properties of the electrical subsystem would alert you to the correct cause. This helps you get prescriptive analytics.

Thread Spools

The Digital Thread

To create an historical record of what happens to an instance of a Digital Twin throughout its entire lifecycle, you represent this with something called a Digital Thread.

Beyond the IoT telemetry the digital twin captures from the physical entity, other significant events are captured via an ever-growing digital thread.

I’ll use an automobile to illustrate how this works. While it’s critical to have a car’s current and historical telemetry data captured & analyzed, there are other events that occur throughout the car’s life that result in a digital thread adding those events to the twin’s permanent record. Taking a car to the shop for an oil change, an accident report and repair, and performing routine service on the car all represent events that are manually added to the digital thread. You could also add pictures, 3D CAD models and other important information via this mechanism. Just imagine capturing a digital thread event where a particular type of car suffered a water pump failure at 60,000 miles. Sharing this information with all other cars of the same type would be invaluable.

In the end, this is how we tell the birth-to-retirement story of the physical entity represented by it’s digital twin.

Twin Buildings

The Digital Twin Instance

It’s time to create a Digital Twin Instance of a physical entity that is derived from a Digital Twin Model.

If you’ve worked with any of the Internet of Things platforms, you probably registered an IoT endpoint or device to make its identity known to the system. In the smallest way possible, this is what it means to create an instance of your digital twin that is entangled with a physical entity.

Like most things in the digital world, you start with Identity. You give your digital twin a name & perhaps a brief description. The IoT platform you’re working with will assign a unique identifier used to access & identify the digital twin and its physical counterpart throughout its life cycle. Next, some type of security token or X.509 certificate will be bound to the unique identifier of the digital twin in order to facilitate authentication & authorization. It’s possible that you might assign a date in the future when the security token or certificate is no longer valid. You should also have the option to enable or disable the twin if you need to blacklist incoming data from a compromised physical entity. Lastly, you bind it to the digital twin model that it’s derived from.

Twin Buildings

Digital Twin Models and Rules

As part of my series on Digital Twins, I’ve discussed the creation of a Digital Twin Model which defines a type, or class of physical entity from which instances of digital twins are derived.

Once you’ve added telemetry, virtual, static, or command properties, it’s time to make use of this #metadata with rules.

The first steps in deriving value from streaming telemetry data revolves around pattern matching, key performance indicators (KPIs), & filtering. You therefore need to specify one or more rules to be associated with each “telemetry property” you’ve defined in your digital twin model. This is accomplished through the use of simple operators such as equals, not equals, greater than, greater than or equal to, less than, & less than or equal to.

Let’s say you’ve defined a “telemetry property” for the “left front tire pressure” of a car digital twin model with an “integer” data type and “PSI” unit of measure. To create a “green” KPI between 30 & 35 PSI, you would define a rule looking for values that are >= 30 & <= 35. Using simple IFTTT algorithms, the event processing engine of your Edge or Core IoT platform would apply those rules to incoming data & trigger an action for values outside that range.