From Hardware to Insights: Why Universities Should Prioritise Data Analytics and AI Over IoT Hardware

A Change in Our Universities Focus Areas

Artificial Intelligence (AI) and Big Data Analytics have become the talk of the town.

Everywhere I go, someone discusses how these technologies are transforming industries and reshaping how organisations work.

But let me tell you, all of this innovation boils down to one thing: data. AI can’t learn without data, and analytics can’t deliver insights.

This realisation has enormous implications for how universities prepare students and educators for the future.

Over the years, many universities have focused on teaching students to build their Internet of Things (IoT) hardware.

Don’t get me wrong, it’s a great way to learn the technical basics, but I’ve always felt it’s not the best use of resources or time.

The truth is that the hardware is just a means to an end.

The real value is in the data these devices collect and what you do with that data. That’s where I think universities should shift their focus: data analytics and AI.

Why Data is the Real Hero in AI and Big Data Analytics

Here’s the thing about AI: it’s only as good as the data you feed.

Whether you’re predicting customer behaviour, analysing health trends, or optimising supply chains, the first step is always about collecting, cleaning, and understanding data.

And guess what generates a lot of that data? IoT devices.

From smart sensors to wearables, these devices are constantly collecting information streams. But the value isn’t in the device; it’s in the insights you can extract from the data.

Take smart cities, for example. IoT sensors might monitor traffic flow, air quality, or energy usage.

That’s impressive, but more amazing is how AI models and analytics make sense of all that data to improve city living.

Building the hardware for these sensors is straightforward; developing the AI and analytics platforms behind them is the real challenge.

Why Universities Need to Prioritise Data Analytics

1. Meeting Industry Needs

Let me be honest — the industry doesn’t need more people building IoT hardware.

They’re hungry for data analysts, AI developers, and data scientists. Companies are about insights that drive decisions, not the physical gadgets that generate the data.

Shifting the focus to data analytics would better prepare students for what’s waiting for them in the real world.

2. Endless Applications

Consider this: data analytics and AI can be applied across many industries. Whether it’s healthcare, agriculture, manufacturing, or retail, the possibilities are endless.

With skills in data analytics, students can work on anything from predicting machinery maintenance to forecasting disease outbreaks or personalising customer experiences.

Meanwhile, hardware skills are mostly limited to niche engineering roles.

3. Lowering Barriers to Entry

Let’s face it: building IoT hardware isn’t cheap. You need tools, components, and a workshop.

That’s a big ask, especially for students or universities with limited budgets.

In contrast, data analytics only requires access to software tools, cloud platforms, and datasets, which are much more accessible.

4. Using What’s Already Available

These days, you don’t even have to build IoT devices from scratch.

There are ready-made solutions, like FAVORIOT, AWS IoT, and Azure IoT.

These platforms make collecting, storing, and managing IoT data easy. So why reinvent the wheel? Use these tools and focus on creating value through analytics and application development.

How Universities Can Make the Shift

1. Rethink the Curriculum

If I were designing a university course, I’d ensure it included data analytics, AI, and IoT platforms. Students should learn how to:

• Collect and preprocess IoT data.
• Use tools like Python or MATLAB to analyse data.
• Build machine learning models and deploy them in real-world scenarios.
• Understand cloud computing and work with IoT platforms to manage data.

2. Partner with Industry

One of the smartest moves universities can make is partnering with companies. Industry collaboration gives access to real-world datasets, tools, and expertise.

Plus, internships and collaborative projects can give students the hands-on experience they need to hit the ground running.

3. Focus on Real-World Problems

When I was a student, I always enjoyed projects that felt meaningful.

Educators should design projects that challenge students to solve actual problems.

For example, they could predict energy usage patterns on campus or analyse traffic data to improve transportation systems.

4. Train the Trainers

Let’s not forget the educators.

They need to stay ahead of the curve, too.

Universities should invest in training programmes for lecturers, helping them stay updated on the latest AI and data analytics trends.

5. Provide the Right Tools

Students can’t learn data analytics without the right tools.

Universities should give them access to software, cloud-based platforms, and open-source datasets. This doesn’t have to break the bank — many affordable or even free options exist.

Imagine the Applications Students Can Build

By focusing on data analytics, students can work on exciting applications like:

• Smart Agriculture: Analysing soil and weather data to optimise irrigation and fertilisation.
• Healthcare: Using wearable data to predict health trends.
• Retail: Analysing customer behaviour to personalise shopping experiences.
• Manufacturing: Implementing predictive maintenance to cut costs and downtime.

These examples show how data analytics can drive innovation across industries.

Isn’t that more impactful than soldering circuit boards?

My Advice to Universities

The world is changing fast, and universities need to keep up.

It’s time to move away from DIY IoT hardware and focus on the bigger picture: data analytics.

By doing this, universities can prepare their students for a future where data is king.

As educators, it’s our job to help students see the actual value of IoT — not the gadgets but the insights they enable.

And for students, my advice is simple: dive into data analytics and AI. These skills will open doors and help you make a real difference.

The future is all about harnessing the power of data.

Let’s make sure our universities are ready to lead the way.