As industrial business leaders increasingly recognize the future potential of the Industrial Internet of Things (IIOT), global spending on IIOT platforms is expected to surge from $1.7 billion in 2018 up to $12.4 billion in 2024. What’s behind this 629% increase over the course of just six years?
In a recent episode of the Thomas Industry Update Podcast, host Tony Uphoff spoke with Shawn Davison, software architect and CEO of custom software engineering firm DevIQ, about his perspectives on how IIOT will transform the industrial space and what the future holds for savvy industrial business leaders who adopt technological innovation in the near-term.
Meeting Software Demand in the Industrial Space
After years spent as a serial entrepreneur launching numerous businesses in telecommunications, retail, healthcare, and e-commerce — some of which were later acquired by notable public companies —Davison felt the time was right to move into the industrial space.
"Our experience, my experience, and my team’s experience in building great software and user experience is now very much in demand in the industrial IoT and [broader] industry environments,” he says. "There’s a significant convergence of software and the physical world. That’s where it gets really exciting, in that that blend of what was simply on a computer before is now all around us in the industrial environment.”
Solving for the User by Shifting to Service-based Models
Davison’s firm, DevIQ, designs IoT ecosystems in an effort to redefine how people work and change what users think is possible through continued technological innovation. This specific focus on solutions architecture that prioritizes user experience is key when developing new IoT platforms and cloud applications.
"User experience, or what we call that end-to-end ecosystem, is [about] creating an experience that spans all the devices in the systems and makes things work across an entire ecosystem. Today, it’s not just about speed and efficiency,” Davison explains. "It’s about creating an experience that works, [in which] users can be very productive. And it creates value for the business.”
This focus on the user also applies to production at a manufacturing business. Industrial business leaders, Davison suggests, should consider shifting from a "product as a widget” to a "product as a service” approach; This shift has been a substantial contributor to the growth of Industry 4.0 and advancements across the sector.
"If you think about it,” Davison explains, "any manufacturer that produces a complex, serviceable product today has the potential benefits of moving from what I call a widget-maker to a product-as-a-service model – and the benefits to the manufacturer as well as the end client are significant. You get better service, the user experience improves, [and so do] the margins and long-term contracts.”
While he acknowledges that the "product-as-a-service” model may be hard to conceptualize initially, "the value is so significant,” he explains. "It allows you to get more information about what your product is doing, and it allows you to improve the product” on an accelerated timeline. By connecting their product, he says, industrial business leaders can collect the data they need to identify areas for improvement or find opportunities to introduce new products to fill gaps.
"What AI requires, in particular, is lots of data,” Davison explains. "So you have to create an ecosystem where you can connect to your products or your industrial environment or your factory components and collect the data from those machines and devices.”
"AI Is Going to Eat a Lot of Software and Data”
Back in 2011, Mark Andreesen said that "software is eating the world.” Now, nine years later, Davison predicts that "AI is going to eat a lot of software and data.”
"Basically, we’re seeing glimpses of the future right here in terms of automation, robotics, machine learning, and AI. And in industry in particular, an application of that is predictive maintenance,” he notes. While some industrial businesses are already reaping the benefits of predictive maintenance, many small- to mid-sized companies still have a long way to go to get on board.
"There’s a tremendous amount of opportunity” to seize the data available today to improve industrial businesses, he explains. To do that, "you have to start [by] essentially creating an ecosystem where you can connect to your products, or your industrial environment, or your factory components, and collect the data from those machines and devices.
"And you’ve got to be able to then ingest that [data] into… a cloud environment […where that] data can then be processed via using machine learning tools and capabilities,” Davison explains. This synthesis of data allows business leaders to "start creating prediction algorithms and learning models” on which to base future process, product, or other business decisions.
"What I see happening is that becoming much more mainstream and easy for a manufacturer that’s not very focused on software today,” he says. "And that’s where we help in a big way” at DevIQ.
Hear More on the Thomas Industry Update Podcast
For more actionable advice for industrial business leaders interested in incorporating IIoT and AI advancements into their processes, this episode of the Thomas Industry Update podcast is streaming on the following platforms:
- Apple Podcasts
- Spotify
- Soundcloud
- Stitcher
