By Jack hayes (Data consultant, Amarji)
The Internet of Things (IoT) has been quietly revolutionising the way we live and work. Just a couple of decades ago, the idea of smart homes, connected cities, and real-time industrial monitoring seemed like something out of science fiction. Now, IoT is everywhere—tracking energy usage, preventing equipment failures, and making businesses more efficient.
I’ve spent a lot of time working with IoT data, and what fascinates me most is not just the sheer volume of information being collected but how that data, when used correctly, can drive real change.
Understanding IoT Data: The Power of Time-Series Analysis
At its core, IoT data is time-series data—a constant stream of information from sensors and devices that measure everything from temperature and humidity to power usage and machine performance. This continuous flow of data is incredibly valuable, but it also comes with challenges.
Raw IoT data is messy. It’s high-volume, often unstructured, and can be overwhelming to make sense of. That’s where data engineering comes in. Structuring and managing IoT data effectively—using tools like Python, SQL, and Microsoft Fabric—turns it from a chaotic stream into something clean, structured, and actionable.
From Raw Data to Actionable Insights
But collecting data isn’t enough. The real magic happens when we analyse it to uncover trends, inefficiencies, and opportunities for optimisation. Here are a few examples of where IoT analytics can make a difference:
1. Energy Cost Reduction
By analysing energy consumption patterns, businesses can cut unnecessary costs. I’ve seen cases where companies were using excessive energy at night simply because no one was paying attention to their usage trends. A simple shift to cheaper tariff times made a huge difference in operational costs.
2. Fault Detection in Equipment
In fast-food kitchens, IoT sensors monitor equipment like fryers and refrigerators. By tracking temperature fluctuations and power consumption, we can predict failures before they happen—avoiding costly downtime and keeping things running smoothly.
3. Predictive Maintenance & Operational Efficiency
One of the biggest advantages of IoT is predicting when machines need maintenance before they break down. This means fewer unexpected repairs, optimised servicing schedules, and ultimately, cost savings. It’s incredible how much smoother operations can run when maintenance isn’t just reactive but proactive.
Closing the Loop: Turning Insights into Action
One of the biggest pitfalls in analytics is collecting great insights but failing to act on them. I’ve worked on projects where IoT dashboards didn’t just display data—they were integrated into ticketing systems like Jira and Zendesk, ensuring that every issue identified was assigned, tracked, and resolved. This kind of workflow integration is what turns data from an interesting report into something that drives real change.
The Future of IoT: What’s Next?
IoT is only getting smarter, and the possibilities for improvement are endless. Some of the most exciting developments I’m seeing include:
- AI & Machine Learning for Predictive Analytics – Using AI to anticipate issues before they even arise.
- Automated Cost Optimisation – Dynamically adjusting energy use and operational schedules.
- Integration with Engineering Fleets – Optimising fleet operations, maintenance schedules, and asset utilisation.
Final Thoughts
The real value of IoT isn’t just in collecting data—it’s in using that data to make better decisions, reduce costs, and drive efficiency. I love working in this space because there’s always something new to learn, new insights to uncover, and new ways to make IoT data work smarter.
If you’re sitting on a pile of IoT data and not sure how to get the most out of it, you’re not alone. It’s a challenge many businesses face—but it’s also an incredible opportunity.