What is IoT analytics? Layers, use cases & challenges

IoT analytics is the process of extracting actionable insights from data generated by connected devices. As IoT deployments scale globally, this discipline has become essential across every sector. Organizations use IoT analytics to collect pertinent data, analyze patterns, and guide decision-making.

Key takeaways

• IoT analytics extracts actionable insights from connected device data across three primary layers: device data for predictive maintenance, environmental data from surroundings, and big data for machine learning.
• Organizations apply IoT analytics across industrial manufacturing, smart cities, agriculture, consumer product, and more to monitor equipment, manage traffic, optimize crop care, and analyze product usage patterns.
• Security vulnerabilities, data volume management, and device errors represent the primary challenges that add complexity to extracting reliable insights from IoT deployments.
• Cloud computing addresses data storage and processing limitations by providing virtually unlimited capacity for maintaining uninterrupted IoT data records over time.

Layers of IoT analytics

IoT analytics operates across three primary layers: device data for predictive maintenance , targeted environmental data, and big data for machine learning. The layer you focus on depends on your use case and deployment goals. Here's how each layer functions:

• Device data: Sensors monitor equipment performance to predict maintenance needs
• Environmental data: Sensors gather information about surroundings like temperature, weather, security footage
• Big data: Massive datasets fuel machine learning and advanced analytics

Device data for predictive maintenance

Many businesses use IoT sensors for remote monitoring of equipment in the field or on a factory floor. The sensors send data back to an IoT platform, where it's fed into an algorithm. Managers can create triggers that automatically send alerts when sensors detect anomalies indicating the need for service.

Similarly, organizations can use device data to monitor connectivity and address any issues that arise. For example, a micromobility vendor might be managing a thousand connected scooters within an urban area. They can use a Connectivity Management Platform (CMP) with a dashboard to look at connectivity data and receive alerts if there are interruptions or anomalies.

Targeted environmental data

Many IoT use cases focus on collecting and analyzing environmental data. Sensors are designed to gather useful information about what's around them. Temperature sensors, weather monitors, security surveillance cameras, and telehealth devices all fall into this broad category. As sensors gather data, they typically send it to a cloud-based IoT platform, which may then connect to third-party analytics software or other enterprise systems.

Big data to enable machine learning

Big data refers to the massive datasets collected in the internet age whether consumer data gathered online or agricultural data captured by field sensors. With IoT, organizations can collect far more data than was possible in past decades. Two approaches dominate:

• Pre-sifting: Looking only for specific cues that indicate problems
• Full transmission: Sending all available data to fuel machine learning and advanced analytics

For example, a healthcare technology company might choose to receive a constant stream of data from all of their telehealth monitors. After making the data anonymous to comply with healthcare privacy laws, they feed it all into a powerful AI-enabled software tool that can extract insights about device usage, patient outcomes, and more.

IoT analytics use cases

IoT analytics drives value across industrial manufacturing, smart cities, agriculture, and consumer products. Each industry applies analytics differently based on their data needs and operational goals.

IIoT: predictive maintenance for machines

The Industrial Internet of Things (IIoT) leverages the power of IoT to provide monitoring for large-scale production sites and other industries such as mining, construction, and fleet management .

A factory manager installs sensors on machines along the assembly line at a car factory. One of the machines starts to overheat, tripping an alert trigger in the management platform that warns the manager of an impending breakdown. The manager can then pause operations and repair the machine before the problem gets worse.

Smart cities: traffic and parking management

In smart cities, IoT sensors are placed in key places such as on traffic lights, in public transportation stations and vehicles, and on utility meters. City managers can then analyze the data those sensors generate to isolate problems and drive better decision-making.

City managers install sensors in an urban parking garage. The sensors count the number of cars that enter and exit and display the number of available spaces on digital signage outside, making it easy for drivers to see if the garage is full. But all of that data the sensors are collecting is time-stamped, meaning that the city managers can analyze it to detect patterns and predict when the garage is most likely to fill up. This analysis can be used to make decisions, such as freeing up additional parking in the area during peak times or building another garage to accommodate more vehicles.

Smart agriculture: crop care decisions

Agriculture applications of IoT typically involve small, low-power sensors installed in remote areas. These sensors may only report back to the platform periodically, but over time the data yielded can paint a helpful picture that aids farmers in managing their crops and livestock.

For example, an agriculture enterprise installs IoT sensors throughout their fields that detect soil moisture levels. By monitoring these levels, not only can they make decisions in real-time (if moisture falls below a certain level, it triggers the irrigation system to turn on), they can utilize predictive analytics to foresee how much water they will need to nurture a particular crop from seedling to harvest.

Smart home: consumer product usage analysis

Companies that produce internet-connected products can monitor how consumers are using them, creating a helpful swath of data that can guide future decisions about product improvements and eliminations.

For example, an appliance company monitors the usage of its line of coffee makers using IoT analytics. They're able to get a clear picture not only of how many customers are buying the products but also of how often they're using them to brew a cup of joe. They can then layer that data with other information, such as mentions of their product on social media channels, to paint a fuller picture of how their coffee machine is faring.

IoT analytics challenges

The primary challenges of IoT analytics are security vulnerabilities, data volume management, and device errors. Each adds complexity to extracting reliable insights from connected devices.

Security

Device security is critical to IoT analytics. If a device is hacked and data manipulated, the consequences include:

• Flawed decision-making based on corrupted data
• Production halts
• Supply chain disruptions

IoT analytics architectures must follow IoT security best practices to defend against outside attackers while remaining cost-effective and maintaining data integrity.

Data volume

While collecting and analyzing large amounts of data is the goal of IoT analytics, sometimes the tremendous amount of data collected can cause problems for IoT architectures, both in terms of storage and processing. Cloud computing can solve these problems with its virtually unlimited storage capacity, and tremendous processing power, allowing companies to maintain uninterrupted records of IoT data over time.

Recommended Reading: The important relationship between IoT and cloud computing in bringing scale to businesses

Device errors

The data from a device can only be analyzed if it exists. If a device breaks down in the field or loses connectivity it can result in incorrect calculations and a less successful IoT deployment. Using a connectivity partner that offers network redundancy will prevent any loss of data due to loss of connectivity.

Simplifying connectivity analytics

While some use cases require third-party analytics applications, many IoT platforms provide built-in connectivity analytics. The Hologram dashboard offers:

• Proactive monitoring: Track usage patterns, device health, and location from afar
• Instant alerts: Get notified when devices are potentially tampered with or usage spikes above normal
• Centralized management: Evaluate and resolve incidents directly in the Action Items menu
• Flexible routing: Send alerts to Slack or through the Hologram API