What is IoT Analytics? Everything You Need to Know

Key takeaways

• • IoT analytics is an evolving market, expected to reach USD 143.62 billion by 2030.
  • It’s a process of gathering, transmitting, storing, and analyzing Internet of Things data.
  • This technology can be used to gain insights into user behavior and streamline decision-making and operational costs.
  • It’s already being used in healthcare, smart infrastructure, supply chain management, agriculture, retail, and other industries.

What is IoT analytics?

IoT analytics refers to gathering, transmitting, and analyzing data collected by IoT (Internet of Things) devices. They combine hardware (e.g., sensors) and software connected to the Internet to gather valuable information in real time and transmit it to IoT data analytics platforms for analysis.

An example of an IoT device is a smartwatch. It syncs with a mobile app to monitor metrics such as heartbeat, blood pressure, steps, calories, and pace. Then, it gets transferred to the app, where the IoT data is processed, analyzed, and presented to the users. Or, simply, where the Internet of Things analytics process takes place. The main goal is to provide actionable insights and help users make informed decisions about their well-being.

Why is IoT data analytics important?

The same principle can be used for a business. Let’s take a closer look at how businesses harness the power of IoT analytics.

1. Recognizing trends and patterns

By using IoT data analytics, companies can collect information (e.g., user behavior, operational metrics, or product performance). However, IoT data can only be useful if properly interpreted. The real value of IoT analytics is that it helps identify patterns and trends hidden within the data.

By analyzing that data, the business can find ways to perfect its operations. For example, it can help optimize supply chain logistics, improve customer satisfaction, or refine its marketing strategy.

2. Making real-time decisions

IoT devices are constantly gathering data, providing businesses with up-to-date information. This instant access to data is invaluable, especially in time-sensitive situations. For example, issues like security breaches have to be addressed immediately, and having IoT sensors all over your factory will help you do just that.

This technology also helps anticipate maintenance needs before they reach a boiling point and destroy an expensive piece of equipment. By using predictive IoT data analytics, you can make decisions that reduce those risks and prolong the life cycle of your equipment.

3. Enhancing customer experience

With the help of IoT data analytics, you gain access to info on customer behavior, preferences, and interactions with your product. Based on this information, you can personalize your product and create experiences that will resonate with individual customers. You can also anticipate their needs by identifying patterns in their purchasing habits and feedback.

IoT analytics allows you to identify and address issues that could impact customer loyalty. Real-time insights will help make your services more efficient, relevant, and personalized.

4. Accommodating your growth

As your business expands, so does the volume of data you receive from IoT devices. Managing this growth is one of the most critical challenges for many startups. With IoT systems, you don’t have to worry about crashing databases or overwhelmed servers.

The IoT ecosystem matches your business’ growth, offering flexible and dynamic solutions that can handle datasets of any size. Whether you’re adding new devices, developing new products, or exploring new markets — IoT data analytics platforms will accommodate your needs.

How does IoT analytics work?

But how exactly does IoT analytics do its thing? Let’s talk about the mechanics of this process in more detail.

1. Data collection

The first step in IoT analytics is collecting data. IoT devices receive information from the outside world through sensors. They can measure a variety of data points, depending on the purpose of the device. For example, a smart irrigation system detects changes in humidity, temperature, and other environmental data.

In smart homes, data is collected through sensors located in devices like lighting bolts, remotes, smart plugs, etc.

2. Data transmission

Once data is collected, it is transmitted over a network to a centralized server, cloud platform, or local edge device. This transmission may occur in real time or at intervals. IoT devices use Wi-Fi, cellular, or other networks to send data to a server or another device.

3. Data storage

Once the data reaches the server or a cloud platform, it is stored in a database. IoT-specific databases must be well-suited for handling high-volume unstructured data. A lot of cloud platforms offer instruments specifically designed to store IoT data.

4. Data preprocessing

The data transmitted to storage moments ago isn’t ready for analysis yet. Before it can be interpreted, it needs to go through several stages of cleaning. This is when all irrelevant data is filtered out, and information is standardized. Missing data points might be either filled in or removed during this process.

5. Data analysis

Finally, it’s time to extract some meaningful insights from the data. Different types of analysis can be used depending on the situation. For example, predictive analytics helps forecast any issues that might arise in the future. Prescriptive analysis is aimed at giving actionable recommendations. These kinds of analyses are usually used alongside each other to provide an all-round understanding of the situation.

6. Visualization and reporting

Finally, the results of the data analysis need to be presented in a way that is easy to understand. The target audience of data reports might be you or your users, depending on the kind of data.

⭐Our experience   We once had to develop a desktop app for a stun gun manufacturer. The software records the intensity with which a stun gun was used. In case there are some problems, this information can be used as evidence during an incident investigation. We made sure that the software displayed data such as the setting used, time of discharge, and trigger pull time in a way that was easy to understand. To ensure clarity and create contrast, we made three conceptual blocks for three different types of information. The manufacturer produces three stun gun brands. We made two versions: a blue one for Magen and a black one for Gard and Lumos

IoT analytics tools

Which IoT analytics tools does a data analyst need to collect, transmit, store, and analyze huge chunks of IoT data? While technologies will vary from project to project, there are three main types of instruments that you’ll most likely need.

Cloud-based computing solutions

A cloud-based IoT analytics platform collects data from IoT devices and stores it. It then analyzes the data using machine learning and artificial intelligence (AI). Cloud-based IoT solutions let you connect millions of IoT devices and manage them remotely. For example, if you need to pass an update, you can do that automatically for all devices. Cloud-based platforms offer over-the-air update technology to make that as easy as possible.

Azure IoT Hub is an IoT analytics platform developed by Microsoft that provides a cloud-based IoT device and software management service

Edge computing solutions

Edge computing is the direct opposite of cloud-based computing. Cloud-based computing is all about an IoT device transferring data to a far-away storage. When it comes to edge computing, the data usually doesn’t travel too far. In fact, the data collection happens in the same location as the computer that stores and analyzes it.

This technology is often used in healthcare. Since medical data is highly sensitive, it might be risky to store it in a cloud server. Performing the data parsing and analysis right there and then is much safer and quicker.

Edge computing solutions help healthcare professionals ensure the security and safety of their patients’ private information. This approach also provides access to up-to-date data, which can be crucial in the case of a medical emergency.

Visualization tools

As we already said, the way the data is presented has a direct influence on its value. The information gathered by the IoT device must be visualized in an intuitive format. There are software solutions that can turn any standardized data into clear and concise dashboards.

⭐Our experience   We developed BaseballCloud — data visualization software that lets coaches and scouts view game stats and player information across the US. This service also helps athletes and coaches effectively track and compare results. Since large amounts of information can be overwhelming and confusing, we made sure that BaseballCloud visualized data straightforwardly. The software tells you the meaning of every single metric, highlighting the areas in which athletes need to improve. BaseballCloud displays information like the athlete’s age, body measurements, and velocity in the form of concise dashboards

IoT data analytics industry use cases

IoT analytics are used across various sectors to optimize processes and improve efficiency. Here are some examples of industries that already rely heavily on IoT analytics.

1. Medicine

Since wearable devices collect data non-stop, physicians can monitor patients remotely. This allows them to detect medical issues simultaneously, prevent complications, and adjust the treatment plan accordingly. The data collected by wearables can be used to create personalized treatment plans and improve outcomes.

2. Smart cities and homes

In smart city infrastructure and smart homes, IoT devices help make the lives of people more comfortable and safe. Some examples include security cameras, automatic lighting systems, and voice assistants. IoT data analytics help optimize resources, reduce costs, and improve life quality.

⭐Our experience   EnerGO is a power bank rental company that lets people borrow portable chargers for a fee. We developed a mobile app that allows people to locate the closest power bank station and pay for the service in-app. We also wrote the software for the power bank stations. The EnerGO app shows you the closest spot to pick up a power bank or return one and pay a fee when you’re done using it

3. Supply chain management

IoT analytics provides real-time tracking and monitoring of goods from production to delivery. Sensors and GPS make inventory management easier by reducing delays, theft, and spoilage. In supply chain management, predictive analytics help businesses forecast demand, optimize stock levels, and improve logistics issues.

4. Agriculture

In agriculture, IoT analytics monitor crop health, soil moisture, and weather patterns. That way, farmers can optimize their resources and better handle unforeseen circumstances. IoT-powered drones and machinery automate planting, watering, and harvesting. This helps increase productivity and lower labor costs.

5. Retail

IoT analytics enhances the shopping experience by collecting data on consumer behavior. It also helps manage inventory and staff more efficiently.

⭐Our experience   Purrweb developed an IoT service for smart vending machines. The client wanted to put smart fridges in offices and hotels so that people could have access to freshly cooked meals. We created a mobile app that communicates with the machines to automatically tally up whatever they take out of the fridges, and it lets customers pay for food and drinks using their phones. The IoT app development process took around six weeks.

Conclusions

IoT analytics is not just a buzzword. It’s a tool that helps businesses across industries reach their full potential. If you are looking for a trustworthy service provider to help you — look no further than Purrweb.

Purrweb has been developing IoT software in various niches for years. We have the expertise you need to use the power of IoT analytics to its full potential.

➡️Check out our portfolio for more examples of our work. If you like what you see, contact us, and we’ll get back to you in 24 hours to discuss your project.