Remote industrial operators looking to scale business value and improve decision-making through edge data, take note: the 2024 Olympics used AI to improve future performance of their event.

According to an article published by the International Olympic Committee (IOC), AI and digital twinning are being used as an efficiency booster for future Olympic games – from energy consumption to identifying the best places to place cameras and power sources. 

One thing AI demands is historical data. This is why we are so passionate about industrial operators owning their data. Without data ownership, it’s a false start when using large language models to train and leverage AI algorithms.

In the article entitled “AI and Tech Innovations at Paris 2024: A Game Changer in Sport,” Ilario Corna, the IOC’s chief technology officer said, “We started gathering various operational data as far back as 2020, to look at how we can make the management of the Olympic Games more efficient.” 

AI has evolved over the past decade. One issue that has surfaced is data ownership. Companies that don’t give customers rights to their data are putting them at a disadvantage. In contrast, those who own their data will eventually win because they can use it while others will start from zero. Catching up over time is like being a lap behind in the 100 meter freestyle. With every passing second, it becomes increasingly difficult to gain ground as competitors advance. 

Companies that secure data ownership today are the ones who will lead tomorrow or, in Olympic terms, will earn gold in the AI race.
That’s one reason FreeWave drafted the IIoT Bill of Rights (data governance is amendment one) – as a way for remote industrial operators to claim their data for future growth and opportunities.

Importance of Data Ownership in the Age of AI

Owning your data is not just a competitive advantage; it's a necessity. 

After all, there are a lot of AI solution providers. The expectation for a lot of people is I’ll just AI it. It’s like a magic wand. Look into the future, though, and ask: What do vendors like? Reliance on their systems.

Unfortunately, there is no one “mega-solution” out there to solve every problem, but, if there’s one thing industrial operators know it’s that data ownership means leveraging your data for operational and business performance. How? 

• Using predictive maintenance to prevent machines from going down 
• Reducing fuel and time waste from manually checking on equipment
• Increasing safety by not putting people in treacherous conditions or terrain
• Maintaining continuous connectivity through alerts, alarms, and data insights

Just as the IOC is using AI to create more efficiency in the future, understanding data over time enables industrial operators to continuously improve.

While still under development, FreeWave is exploring AI solutions around our data platform that pulls in SCADA system data and edge data from sensors. A technician, for example, can resolve the issue of a wobbly rotating asset, like a bearing, after seeing the vibration and temperature data on a single pane of glass or a dashboard connected to the data platform. 

Another example of using historical industrial internet of things (IIoT) data comes from when I lived in Michigan. I walked into an automotive manufacturer’s plant that made instrument panels, a “tier one supplier” in the industry. The company didn’t have the budget to climate control the entire building. In hot, humid weather, the adhesive failed. Since variables change over time, IIoT solved the problem. 

Humidity and temperature data from sensors showed the numbers going up and down. Historical data indicated when to change the adhesive or turn up the AC or ramp up the climate control when needed. The manufacturer and its solution providers knew what knobs to turn by identifying trends in a specific period. That’s why data ownership is so important. Without data, AI becomes impossible.

Solve – and Simplify – Operational Problems

AI is hard to get your arms around. It has appeared on the business landscape like a tsunami. We’re all trying to figure out how to embed AI into daily operations so that those operations can work better. But it’s not easy or even cost-effective to go from pilot to full deployment. We’re working on solutions that reduce the tech stack and use AI to address specific problems like predictive maintenance and trends analysis of sensor data. When I’m thinking data – and the AI that can help it become more robust – I picture a huge Excel spreadsheet.

What’s on the spreadsheet? I see real-time monitoring of temperature, vibration, or water levels, for example, as important variables for industrial leaders to know: oil and gas producers with fields in remote or rural areas, large-scale agricultural operations with water pump stations dotting thousands of acres, or public and municipal providers with aging infrastructure in need of robust, remote network strength. These are places that pose a risk for people to perform daily or weekly monitoring. 

My hometown of Austin, Texas, while known for barbecue and Tex-Mex, is equally known for its unreliable energy grid, especially during hurricane season or super hot summer months. The panels on the grid can overheat. Alerts notify engineers to replace components before problems start. Rather than react, alerts and predictive maintenance reduce downtime. 

Another example is California where water is a precious commodity. Knowing humidity levels, soil moisture, and water consumption allows agriculturalists to invest minimal resources for better outcomes. Now, imagine AI overlaid onto those industrial internet of things (IIoT). AI optimizes sensor data locally, at the edge, by providing analyses crucial to decision-makers.

Failure-Proofing Connectivity Keeps Data in Play

Connectivity is changing, and it’s largely due to satellite becoming smaller and more affordable. Oftentimes, industrial operators don’t have ready access to cellular or Wi-Fi, however, satellite is an optimal solution for areas with questionable coverage and those wanting a strong back-up connection. 

We’re combining satellite gateway expertise, industrial sensor technology, and cloud computing with algorithms to detect problems. A robust data platform, like we’ve developed, enables remote companies to gather sensor data, make sense of that data for local intelligence through a gateway, and send the insight through the cloud to a single pane of glass, where that data can be stored or acted on depending on the need. 

Own Your Data, Own Your AI Future

Thinking of data only in solving today’s problem doesn’t stand up to winning long-term. Those who don’t own where or how their data is managed might be left behind. 

Think of it this way. The data is your IP. When you give your data to a solution provider, you give the solution provider a better solution, but forgo the opportunity to improve your performance using AI down the road. 

After all, AI is just a fancy math problem. If there’s no data to input, you can’t build your IP.

Just as athletes leverage insight and experience to outperform predecessors, remote industry operators can use their data to improve response, results, productivity, and stay competitive. Elite athletes analyze, tweak and get back out there. Remote industrial operators can do the same. In today's fast-paced AI-driven world, data is the new gold. 

Ryan Treece is the Global Business Development Manager – Data Platforms & AI Solutions at FreeWave. He believes that IoT isn't just about technology - it's about making a real impact. In the industrial space, he's driven by the goal of helping manufacturers eliminate downtime, reduce waste, and enhance efficiency. By leveraging IoT, he believes we can create workplaces where productivity thrives and job satisfaction soars.