Combining the Internet of Things (IoT) with automation is essential if you want to stay competitive while continuing to streamline your processes.
Indeed, without the data provided by the IoT, you can miss out on the critical information you need to stay competitive. And without automation, you’re stuck sifting through a growing pile of data that can leave you worse off.
The solution is for companies to integrate IoT development platforms with low-code workflows.
How much data can the IoT collect?
In 2019, the estimated volume of data in zettabytes (one trillion gigabytes) from the IoT was 13.6. In 2025, it is estimated at 79.4.
The average data companies handle, however, can vary. Usually, it is between 47.81 terabytes (TB) for the average small business and 347.56 TB for the average business.
In short, the IoT can provide a business with much more data, regardless of the size of your business. This opens the door to deeper and more accurate customer and business insights. (Also read: 6 no-code AI platforms accessible to SMBs.)
However, the sudden increase in all this extra data also presents a major challenge.
The challenge of implementing IoT networks
The IoT enables organizations to increase productivity, streamline workflows, and redefine how a business operates. The data streams it provides can move across a range of computing infrastructures. Innovation is essentially constant, with new apps and features being added daily.
As you begin to connect more and more devices to the IoT, you face a larger and larger lake of data with streams constantly flowing through it. So the challenge quickly shifts from data capture to data management. And that can create a major bottleneck for growing businesses.
When people try to explain the benefits of the IoT, they often use the logistics metaphor: sensors in refrigeration containers can track temperatures to ensure perishables stay within set parameters.
This is a great example; but what if you want to analyze more than the temperature of storage containers? What if, for example, you want to track the efficiency of your suppliers by measuring a range of data across your entire supply chain? All of a sudden you are facing a data overload. (Also read: IIoT vs. IoT: The Biggest Risks of the Industrial Internet of Things.)
Collecting data from various sources and combining that information into a clear picture can be difficult, especially if you try to do it manually.
More data, more problems?
Often one of the biggest hurdles to connecting more systems to the IoT is whether someone has the time to actually analyze that data.
Depending on how you approach IoT implementation, it can feel like lighting a data firehose. And if you don’t have the right systems, that data ends up where most data dies: endless spreadsheets.
When data is siled in spreadsheets (or other platforms), it becomes increasingly difficult to manage. Reports cannot occur in real time. Manual data entry errors are costly and put your business at increased risk. Moving data requires either delegating it to a team member (who needs to find the time to do it) or outsourcing it. Both involve more costs. (Also read: Break down silos with integrated data analytics platforms.)
Either way, it can be a risky investment for growing businesses with limited IT resources.
You can also create custom applications that connect these systems to a central database. But it also creates problems: building custom apps is expensive, time-consuming, and potentially risky from an ROI perspective.
And if you’re trying to transform your business processes to stay competitive, your budget and your IT department are likely limited in time and resources.
How can IoT networks and low-code support business functions?
Low-code platforms are Software as a Service (SaaS) interfaces designed to streamline the development of applications and integrations. In short, they are an incredibly nimble way to build apps. Rather than building complex custom apps from scratch, you simply drag and drop pieces of code or visual elements to create the solutions you need. (Also read: Is no-code development about to go mainstream?)
This drastically reduces the time and cost required to create custom applications. Instead of spending seven figures on custom app development and waiting months to test and go live, you can build custom software solutions in days.
Low-code platforms also have many advantages as a cost-cutting strategy. As a SaaS platform, costs scale with usage, making them very affordable solutions for businesses with a limited IT budget. Plus, they’re designed for people who don’t have coding experience. That means they’re easier to use and onboarding is much faster (and cheaper).
By using low-code, you can quickly connect various IoT technologies to your existing enterprise infrastructure. And you can consolidate your data on one platform. In short, you’ll trade silos of data for actionable insights in clear data dashboards.
Additionally, these cloud-based platforms are more secure than many solutions companies use to house data.
As an added benefit, several low-code platforms already have IoT support built into their framework. Ultimately, you can connect your technology faster. (Also read: How IoT is driving growth in the micro data center.)
How Low-Code Reduces IT Backlog
Despite the benefits that technological advancements bring to businesses, IT departments continue to struggle to achieve business goals. The IT backlog is a real problem.
Therefore, connecting applications to the IoT in a meaningful way can be a real challenge for IT teams who are already bogged down with what seems like an endless list of open tickets.
IT professionals must manage the maintenance of outdated operational technology, working within the limitations of current legacy systems and the technical debt that eats up IT budgets. Additionally, they now face increasing pressure to integrate systems and automate workflows, which requires investing time and resources in better systems.
The constraint is real and measurable.
Low-code allows IT developers to work faster and more cost-effectively. Rather than building custom integrations to connect a hodgepodge of technologies, your IT department can quickly establish connections between new technologies and your organization’s software architecture. The end result is a significant reduction in the ticket backlog and more time for other projects. (Also read: Low-code platforms: the solution to the shortage of developers?)
Challenges with IoT and Low-Code Networks
Although many common low-code applications are designed to support the IoT, there are still potential challenges.
On the one hand, the IoT is complex. And even though users can build custom apps with just some basic coding knowledge, that doesn’t mean it’s necessarily easy to do. You potentially envision a complex network of disparate systems, endpoints, and IoT platforms. Additionally, you need to know the best way to organize data streams and present them in a meaningful way. (Also read: Top 5 Ways to Organize the Data You Need.)
In addition, applications are increasingly complex: advances are happening every day. IT teams, with their experience in code, are better equipped to put in place the necessary infrastructure that companies need to learn meaningful lessons from these new technologies.
Therefore, low-code is not positioned to replace software developers. Instead, it’s a tool that can help them scale up their efforts. Other team members can write the basic business logic needed to run automations, and they can highlight relevant data points. However, they still need to work with IT to create the infrastructure needed to effectively support IoT.
Despite the challenges that come with them, low-code platforms have the power to amplify the work of developers. And ultimately, it can help them build the systems businesses need to take advantage of all the benefits of IoT and automation.
It’s important for businesses, especially those who think the IoT is out of reach due to data complexity, to realize that low-code is the scale that will help them realize its full potential. (Also read: How Low-Code Development Will Bring Data Science to the Masses.)