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.
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.
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.
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.
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.
What do you think the future on earth looks like?
We all think of a future where humans and machines interact seamlessly. Our idea of the perfect future is influenced by how it looks in movies and TV shows.
Most of these fictional future worlds have one thing in common: super-smart devices.
Whether it’s the ultra-futuristic world of Blade Runner 2019 or the toned-down future of Black Mirror episodes, all of them feature smart devices that interact with the characters.
These fictional worlds, however, are not all that rooted in fiction. Some of this technology already exists in the present world, albeit with limited functionality and capabilities. These devices are called IoT devices—interconnected devices equipped with sensors and cameras that allow them to communicate with neighbouring devices over the internet.
Businesses, though, are not resting on their haunches. They are trying to create smart devices with greater functionality and capabilities, just like in the movies. Data analytics tools like SAS analytics for IoT are helping engineers create these uber-smart devices.
Early IoT devices had simple functions and limited capabilities due to the lack of data needed to improve their intelligence. As a result, they were not smart—only a little less dumb.
Early smart doorbells, for example, only relayed a video feed of the front door to occupants inside the house. While this is undoubtedly useful, it was not very smart.
Modern smart doorbells not only relay a video feed to occupants but also identify the person at the front door. In addition, they notify occupants about the arrival of guests, all through the power of analytics.
This identification function, in particular, uses cameras, AI and data analytics to identify different people. Cameras capture the image and data analytics helps identify the individual by comparing the image to information in a database.
Analytics tools like SAS analytics for IoT have been critical in improving home assistants powered by the likes of Google Nest Home and Amazon Echo.
These digital home assistants use AI-powered data analytics to study our behaviour through various sensors located. Studying our behavioural patterns helps these devices automate actions like setting the temperature through a thermostat or switching lights on and off.
IoT devices are not only making a splash in our homes but also in our factories and commercial spaces. Many organisations are now using IoT devices in their manufacturing lines, which previously required manual labour.
Early manufacturing equipment relied on human knowledge about each machine for maintenance. Unless there were trained machine operators on-site, these expensive machines were rendered useless when they broke down.
Modern manufacturing lines include IoT devices so that maintenance and troubleshooting become easier. Modern manufacturing equipment includes sensors and cameras that not only monitor the production process but also monitor the optimal operation of these machines.
These sensors stream large amounts of data to servers to deliver visual analytics insights about the manufacturing process and components inside the machines. With these insights from IoT devices, employees are able to detect faults in the machines and fix them, reducing downtime across the manufacturing process.
Modern devices not only share data but also use AI to pinpoint faults, eliminating the hassle of troubleshooting for employees.
In the future, data analytics tools for IoT devices may help us build intelligent manufacturing lines that fix themselves, making production seamless.
All of us envision a future powered by smart technology that automates our mundane tasks.
Thanks to data analytics for IoT, this reality is not too far away. The IoT analytics challenges that prevented widespread adoption is fast becoming a thing of the past as more and more smart devices are used across industries.
Soon, we might not need special effects to create the fantastical scenarios we see in movies.
When we think about agriculture, utilities, and transport—we think about IoT devices. The Internet of Things is no longer an optional benefit for a tech firm. It is an essential component of operations for almost any company in any industry. With over 27.1 billion devices to be deployed across a range of industries, like healthcare, it is important to understand how IoT devices work.
IoT devices contain petabytes of data, which could contain valuable information. Given the growing importance of IoT, adopting technology that allows organisations to analyse data from the source as soon as it is generated is important. This is where edge analytics software can be a huge boon.
IoT devices make several operations, like preventative maintenance, possible. There is one drawback to them, however, and that is time. The value promised from IoT devices only converts into tangible ROI when the right analytics software can convert the data into useful findings, in real-time. Take, for example, healthcare IoT devices.
One of the benefits of these devices is their ability to detect a person’s heart rate in real-time. This information can inform doctors when patients are undergoing a serious condition, like a heart attack.
The catch is that these readings only work when there is an analytics system that can analyse and present the findings in real-time. In such cases, the conventional data analytic pipeline will not work. It won’t be possible to have data transferred along this data pipeline, stored in a data lake, and then analysed.
This is where edge analytics software can make transformative changes to the production process. Rather than transporting data to a separate storage facility and then analysing it, edge analytics allow businesses to analyse data much closer to the source.
Instead of storing data in one location, edge analytics can analyse data closer to the point of origination. This means organisations don’t have to worry about setting up a central point of storage. Instead, they can focus on building out the comprehensive IoT network they need. This also has positive implications on data collection and analysis because most organisations can eliminate latency in data collection and processing.
How will edge analytics software work? Machine learning will play a crucial role in this function. Conventionally, the analytics model collects, stores, and prepares data for analysis. After that, an algorithm is chosen. The parameters of the algorithm and the data output, however, can vary depending on the IoT network.
This is where machine learning becomes an integral component of edge analytics. Machine learning enables organisations to find the right combination of data output and algorithm because it learns as it feeds data.
This is quite transformative because it gives organisations the option to maximise efficiency in their data collection and analysis process. The development is especially crucial when you consider that most IoT networks are deployed across the cloud. Companies in different industries, like utilities and transport, will be relying on a cloud-based IoT network for most of their operations.
Most importantly, edge analytics allows for the evolution of IoT networks. By removing some of the existing constraints, we can see IoT devices pave the way for some transformative operations. For example, IoT building technology will be able to measure a variety of factors that go beyond worker productivity.
IoT devices can measure energy efficiency, worker health and safety, along with a host of other factors that can help executives determine the overall scope and scale of their employees’ well-being, both mentally and physically. While organisations are looking into their well-being by investing in various programmes, IoT devices can measure employee well-being in ways that weren’t previously possible and get a better understanding of how effective these programmes are.
As the world’s industries become more and more dependent on IoT devices, adopting the right data analytics software can give organisations the edge they need to maximise ROI on IoT devices. Edge analytics can provide the competitive advantage that organisations need.
Visit Selerity to know more about edge analytics software and other data analysis platforms.
As organisations generate more and more data, it becomes important to be more aware of the shortcomings of using big data analysis tools for IoT data. We are seeing a trend in the industry where organisations are dealing with larger bodies of data, but need to generate insights at a faster rate than before. Yet, managing big data from IoT is not easy, and this blog will explore some of the reasons why that is.
Data visualisation is challenging
Developing a cohesive process for collecting and analysing data is one of the biggest challenges for big data analysis tools. This is because data visualisation is difficult to do though it is a huge part of the data analysis process. Put simply, data visualisation is the process of taking raw, complex data and converting it into an easily readable format, like graphs.
The objective of data visualisation is to make complex data sources easier to understand. However, it is difficult to replicate the process with data generated from IoT devices. This is because data generated from different sources, like IoT devices, are heterogeneous in nature. The data often presents itself in structured, semi-structured and unstructured formats, making it difficult to execute proper data visualisation using big data analysis tools.
Iot devices are constantly streaming data in real-time.
This places a strain on data storage capacity and management processes. Since IoT devices, like sensors, are constantly streaming data, questions arise on the best method for storing and managing it.
While the obvious solution would be to move past physical servers and into cloud-based infrastructure, there is still the challenge of managing the data so that organisations can generate useful insights in quick time. Usually, such measures would involve using edge analytics to start the data analysis process as soon as possible.
While there is no denying that IoT sensors can sense and communicate a ton of data when applied to different applications, there is a question mark over its integrity. How can we ensure that data is not being leaked? How can we guarantee that privacy concerns are addressed? Is there any guarantee that data collected meets the organisation’s objectives?
This is a challenge when it comes to using big data analysis tools on IoT data. While the tools can break down and analyse data, making sure that the findings are ethically obtained can be challenging.
IoT devices can work without stopping. While the ability to constantly generate data is a huge advantage, there are some concerns to be had. For example, how can we ensure that big data analysis tools have the necessary power to run round the clock? This is a problem organisations have to consider when implementing their analytics framework.
Device security is a huge concern for most organisations relying on IoT sensors to get work done. This is especially the case for organisations using edge analytics as part of the data collection and analysis process. Some of the challenges include, but are not limited to, networking, data storage, and computing power. To work around this problem, cybersecurity becomes a major factor.
Every IoT device generates an enormous amount of data, which may or may not lead to confidentiality concerns. It is important to ensure that data is collected and stored in a way that meets these requirements.
While there is no denying that IoT devices are a huge asset for organisations, there are some downsides to using them. To work around some of the shortcomings, it is important to work with an experienced data analyst or a team of skilled analysts.
The right team can help organisations optimise their big data analysis tools to ensure you are getting the most out of your IoT devices. The right data analytics team can help organisations optimise their data analytics infrastructure around the use of IoT devices to maximise the quality of findings while minmising the downsides.
Visit the Selerity website to learn more about optimising big data analysis tools.