Understanding IoT edge analytics and its uses in the real world

IoT edge analytics

Since the advent of the internet, the world has changed drastically. The World Wide Web has democratised information and made the entire world as accessible as one republic. Albeit one governed by the rules of internet companies instead of a government.

Today, most of our interactions are reliant on the internet, at least to a certain extent. Everything from communication, entertainment, and security to payments. The impact of the World Wide Web is undeniable.

While the internet and the services that are based on it have taken much of the limelight, a key piece of this environment often gets overlooked; the IoT device network.

In recent years, IoT devices have exploded in popularity. A recent study found that 55 billion IoT devices are expected to power our households and businesses. IoT devices take the form of smart light bulbs to complex sensors powering planetary research rovers.

Many believe that IoT devices are the driving force behind the internet. Without these devices, the internet as we know it may not exist. These devices power the products and services we love by collecting and transmitting information to companies who operate them.

That said, the amount of data collected by these devices has created a bottleneck in data analytics. This is because analytics platforms are not powerful enough to analyse and produce insights in real life.

This has led to the development of IoT edge analytics—an analytics framework that eliminates bottlenecks by moving data analytics closer to the IoT devices.

In this post, we explore what IoT edge analytics is and its real-world applications.

What is the role of data analytics in IoT applications?

IoT devices act as the data collection tools where they are deployed. These devices stream data to centralised data processing systems which clean, process, and analyse these data sets to produce actionable insights.

Using these insights, IoT devices can help real-time decision making in IoT applications. Edge analytics is the next step in data analytics in IoT applications.

How is big data analytics important for IoT systems?

Without big data analytics systems, it would be impossible for organisations to derive any meaningful value from their IoT systems.  

IoT needs analytics platforms because they generate a large volume of data. In fact, by 2025 IoT devices will be generating 79.4 zettabytes of data in real-time. The immense volume of data generated makes it hard to analyse the data efficiently.

Big data analytics platforms can analyse data on such a large scale because they feature machine learning that can extract useful insights from the data in real-time and reframe the insights in a framework relevant to the organisation’s goals. 

However, not every analytics platform is suited for IoT systems. The analytics platform must have the right infrastructure and performance capabilities to analyse IoT data in real-time. This is where IoT edge analytics systems become essential.

What is IoT edge analytics?

IoT edge analytics refers to a data analytics framework where analytics capabilities are moved to, or closer to, the devices that make up the fringes or edges of the data analytics pipelines. 

Traditionally, data analytics involved collecting data from various sources and migrating it to a centralised data lake or data warehouse to initiate the data analytics process. This, however, has proved to be inefficient as the amount of data collected in the current environment far outweighs the analytics capabilities; which means businesses incur increasing costs for data transmission and storage.

Edge analytics eliminates this issue by conducting data analytics closer to the data source and only transmitting the final results to the warehouse, reducing data transmitting and data storage costs.

This also allows for real-time analytics, which has paved the way for some clever implementations in the real world.

Edge analytics in the real world


The transportation industry is one of the pioneers in IoT edge computing adoption. Technologies like auto parking, lane correction, and automated driving found in modern cars are powered by IoT analytics. Sensors in these cars collect and send information to the computer unit, which analyses the data and sends instructions to these driver-assist systems.

NASA also used edge analytics to execute the landing of the Perseverance Rover. Without edge analytics, NASA would not have landed the rover due to data transmission latency from Mars to Earth.


Quick decision making and tight tolerances are key to an efficient manufacturing function. By nature, manufacturing needs continuous monitoring to spot errors and optimise the production process. 

That said, achieving this level of production efficiency is a hard task using human resources or traditional analytics methods. Edge analytics can streamline this process using IoT sensors to power error identification systems and production optimisation processes. 

Machine learning

One of the more widespread uses of IoT analytics, machine learning, uses on-device processing to perform complex tasks without connecting to the cloud or the internet.

Your smartphone, for example, uses a neural processing unit to power smart assistants like Siri or Google Assistant to understand your voice commands and execute them.

IoT edge analytics is the secret weapon for the modern experience

In the current context, IoT devices help us simplify data analytics by eliminating the bottlenecks of traditional methods. It’s fair to say IoT edge analytics is the future of data analytics, and the internet experience as a whole.


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