IoT Data Analytics and How It Can Benefit Your Company

The rise of IoT (Internet of Things) devices over the past decade has brought valuable insights to homes, businesses, and healthcare, but it has also introduced challenges. IoT data is often messy, unstructured, and incomplete due to the nature of collecting data directly from the source. However, modern IoT data analytics, equipped with specialized tools and techniques, can extract meaningful insights from this complex data.

While early predictions in the mid-2000s foresaw a rapid boom of interconnected devices, the reality has been a gradual, steady growth. This slower pace has allowed data tools and analysis methods to evolve alongside the technology, enhancing the value of the insights generated by IoT devices. Big data companies have played a crucial role in this, developing tools and dashboards that are transforming how we interact with and understand the world.

In this post, we’ll explore the connection between IoT devices and data analytics and show you how even basic analytics principles can help you start transforming your surroundings.

What Are IoT and IIoT?

In the simplest possible terms, IoT can be thought of as a network of interconnected sensors and devices collecting, sharing, and uploading data directly from its source. Individually, one IoT device can make a small difference in improving a process or automation. When these devices and their data are combined and collated, however, a smart network can provide revolutionary change and efficiencies within a business.

The rise of IoT devices and networks in homes and businesses has led to an exponential growth in the amount of data being collected and analyzed across multiple industries.

IIoT (Industrial Internet of Things) is a specialized subset of IoT technologies that focuses on the thinking, tools, and technology that applies to industrial use cases of IoT tech. Ranging from industrial manufacturing processes to energy generation, building maintenance, and asset management amongst others—IIoT tech is a growing area of interest for many companies.

For the companies adopting IIoT technologies at an early stage, the effect the tools have had on processes and results has been truly transformational.

For example, American motorcycle manufacturer Harley-Davidson adopted IIoT into their manufacturing processes in recent years. Using efficient new methods of production, data gathering, and communication the company was able to reduce its end-to-end assembly time from 21 days down to just 6 hours.

IoT and IIoT are technologies that are often confused and correlated. Users need to understand that IIoT is a subset of IoT technologies aimed at industry and businesses. Some of the key differences between IIoT and IoT include:

• Reliability. While errors and bugs in commercially available IoT devices have minor impacts on users, IIoT failures can be highly damaging and exceptionally costly in industrial settings. IIoT devices are built with increased safeguards and reliability measures in place to reduce the chances and impacts of product failure
• Resilience. Typically, IoT devices are built to withstand domestic environments close to room temperature, humidity, and light wear and tear. In contrast, IIoT devices are built to withstand the impacts of their environment and the dust, wear and tear, and temperature spikes that might be incurred in manufacturing processes and more challenging environments
• Legacy Compatibility. Commercial IoT products are often only compatible with the latest version of consumer technology. In industrial settings, IIoT tech often has to work with equipment and machinery that is decades old, expensive, and irreplaceable. In addition, IIoT devices often have to collect broader data sets and unique information to comply with regulatory guidelines
• Device Connections. IIoT and IoT devices are built for different audiences with different goals and ideas. Many commercial IoT devices will be designed to integrate with smartphones and consumer electronics while an IIoT device is more likely to be built to interface with logic controllers, automated processes, and cloud services

How Can Data Analytics and IoT be Combined?

Some of the biggest challenges associated with IoT come from the amount and type of data that is generated by devices. By scaling the number of sensors, inputs, and responses the system is capable of handling the data challenges associated with the technology scales too. That's where cutting-edge IoT data science tools and trends come in to help.

By its very nature, IoT data is prone to being highly unstructured, gap-filled, and difficult to tame with traditional data analysis tools. These devices are often tasked with recording data from noise-rich sources such as cameras, temperature probes, and sensors attached to industrial machinery.

To work with this data effectively it first has to be cleaned and sorted to ensure it's both accurate and compatible with further analysis tools. After data is prepared it can be combined and analyzed against other data sets either recorded internally in the past or commercially purchased from suppliers. These data sets often give crucial information such as weather or production data, market information, or industry-specific analysis to allow businesses to make the best possible decision for their available assets.

Combined, these data sets provide your business with the context-filled real-time business intelligence necessary to make improvements and monitor the impacts of changes within your organization.

Impacts of IoT Data Analytics on the Industry

The most recent reports on IoT predict that the technology is expected to reward companies with over $1 million returns in industries that can fully harness its potential. With such astronomical returns, it's worth taking a look at the benefits of IoT and IIoT in your business and what definitive advantages you can expect from the technology.

The most notable benefits of IoT and IIoT tech in your business should include:

Improved resilience

One of the key advantages IoT brings to firms is increased visibility over internal and external challenges. In practical terms, this means applying maintenance improvements and machinery upgrades as they're needed to maximize the lifecycle and efficiency of industrial components. It also means responding to supply chain challenges in real-time and using consistently changing data to simplify management and resourcing through automated tools.

The key to achieving this resilience and reliability is utilizing the best available tools within your teams. Our guide to the current top data science frameworks can be a great guide to discussing the technologies and tooling you need for your organization's unique requirements.

Real-time performance insights

Where human-based monitoring and analysis can take hours, days, and weeks to report on what is going on in a business — an IIoT-connected organization can paint a picture of what's happening on a minute-by-minute basis. This kind of feedback can allow downtime and issues to be addressed instantaneously and back up the need for changes and improvements with hard data that's impossible to dispute.

Having a wealth of data on hand allows for new designs to be built and tested in real-time and the need and opportunities for new services to be easily identified in processes.

Robust security measures

A side effect of increasing the data and visibility within your firm is improving the security landscape too. By making assets, processes, and data more visible to teams these same tools make security throughout the firm easier to implement and monitor.

Having transformational connections generating vast amounts of data inside your organization makes security a key priority and core consideration of the way systems are built. Using these same devices and connections, IoT data analysis can build a picture of what events are expected and unexpected within your network and keep a close eye out for any abnormal access or readings during day-to-day operations.

Increased revenue

By far the biggest positive for manufacturers to take away from IoT technologies is the efficiencies and revenues it unlocks in existing production processes. Using data analytics powered by IoT devices, firms can increase production efficiency and quality to boost revenues practically overnight.

Using real-time reporting and analysis, business leaders have been able to strategize more effectively and meet customer demand to improve sales and provide a more customer-centric business solution.

Improving your Organisation with IoT Data Analytics

The key to accessing the positive impacts of IoT within your firm is hiring the right teams to analyze the data and services relevant to your industry. To do this you can hire data science freelancers or employ an outsourced team of data scientists to investigate the requirements of your organization and build the systems that can make an impact.

Alternatively, you could hire and manage an in-house team of data scientists to consistently build and improve your IoT systems.

Whichever route you take, combining IoT and IIoT technologies with data analytics capabilities is guaranteed to be a transformational change for your organization. Like many revolutionary new things, it's one that you'll quickly forget how things were done in the past and one that will quickly provide benefits and advantages you won't want to part with.

FAQs

Q1. Are IoT and data analytics related?

IoT and data analytics are two distinct topics that are very closely linked to each other. The two almost invariably go hand in hand in the industry. IoT is tasked with gathering, collecting, and combining data from various areas of the network and pieces of machinery while data analytics acts on that data to produce actionable reports and intelligence for businesses to use.

Data analytics, in summary, is a tool to tidy up and make sense of the information gathered by IoT devices and sensors. While IoT equipment often collects noisy data, filled with gaps, and littered with small errors (temperature spikes, vibrations, and bad readings for example) big data uses advanced data science IDEs, tools and techniques to smooth out these data points.

High-quality data analytics specifically designed for IoT technologies is essential to making these devices work well for companies. Individually, neither of these two things is especially valuable. Combined, IoT and data analytics can unlock exceptional potential and enable entirely new kinds of businesses.

Q2. How is big data analytics used in IoT?

Big data analytics is used to tidy up, make sense of, and gather the data collected by IoT devices. While IoT sensors often collect noisy, error-filled, and gap-prone data the tools and techniques applied by data scientists can transform this information into accurate actionable insights and reports.t

The insights that big data analytics can provide using data gathered by IoT devices can be truly groundbreaking. Some of these insights available to firms include:

• Descriptive analytics. A tool to provide insights into how systems and devices are performing in real-time
• Diagnostic analytics. An insight that describes how and why a system is providing the performance seen
• Predictive analytics. A system that uses collected data and insights to produce the most likely actions and outputs a system will provide in the future

Using effective data analytics organizations can anticipate the future needs of systems, respond to ever-changing conditions, and efficiently use resources to improve internal processes.

Q3. How does data analytics affect IoT?

Data analytics can provide a range of actionable insights and outputs using IoT devices. The two, seemingly disparate, fields of computing are intrinsically linked by the ability of IoT devices to provide vast amounts of data and data analytics ability to organize, streamline, and report on that data.

Data analytics can take real-time data from IoT sensors and devices and provide up-to-the-minute reports and data dashboards for businesses to act on. Data tools and technologies can provide descriptive, diagnostic, and predictive analytics to firms—enabling a broad and current visibility of what is happening within an organization.

While IoT has been widely hyped as a groundbreaking tech of the future, the reality is that it's a technology that can do very little on its own and almost nothing without a good data analytics team behind it. Combining these tools well and making the most of their advantages is a key competitive edge that organizations are rapidly adopting.