What is IoT Data Analytics? A Detailed Guide for Beginners

We’re living in a world where physical devices can “talk” with each other and exchange data like humans do. These devices, from smartwatches to factory machinery, can produce a huge flow of information, known as IoT (Internet of Things) data. Like other types of data collected from websites or apps, IoT data also brings us transformative benefits if it’s properly exploited. In doing so, many businesses now are conducting IoT data analytics to take advantage of every pixel of this data. 

So if you listen to the term “IoT data analytics” at least once, you may wonder what it is exactly. Here in this article, you’ll find the answer to this question. Further, you’ll understand why your business should invest in IoT data analytics, which types it covers, and how to implement analytics effectively. Let’s dive in!

What is IoT Data Analytics?

Today, “smart” devices, like fitness trackers or intelligent thermostats, are connected to the Internet and exchange IoT data. However, this data – be it structured, unstructured, or semi-structured – is raw and useless. For example, a sensor from industrial equipment records a range of temperature data. This data itself, however, doesn’t tell you much about the equipment’s status. That’s why you need analytics. 

Briefly, IoT data analytics refers to converting this raw data into something useful for your business. Take the above example. After processing and analyzing temperature data, you understand that the equipment is overheating. This requires you to take proactive solutions before anything worse (like breakdowns) happens. 

For IoT data analytics to work well, you need to understand the three components behind it: 

• Sensors: Detect changes (e.g., about temperature) in equipment or environments, turn these physical changes into digital signals, and collect them for IoT analytics platforms.

• Gateways: Function as bridges between sensors and cloud platforms. They gather data from different sensors, process it locally at the edge of the network, and transfer the most critical data to the cloud for deeper analysis. This reduces data workloads and thus latency.

• Cloud Platforms: Provide computing and analytical tools to store and analyze massive IoT data volumes.

4 Types of IoT Data Analytics

There are four types of IoT data analytics:

Descriptive Analytics

This is the most basic form of IoT data analytics. It summarizes historical data to answer questions like “What already happened?” or “What is now happening?” With this capability, this analytics type can identify patterns and trends within datasets over time. 

For instance, descriptive analytics allows you to understand how much energy a specific factory machine consumes over time or how many kilometers an individual runs over a 7-day period. 

Diagnostic Analytics

This helps your business analyze IoT data to answer the question “Why did this event happen?” In other words, diagnostic analytics focuses on explaining the root causes behind a problem by using techniques like correlation or drip-down analysis. 

For example, this analytical approach digs deeper into sensor data to identify why a certain production part consumes energy most (i.e., due to malfunctioning components or a recent change in the manufacturing schedule). 

Predictive Analytics

As the name states, predictive analytics refers to predicting future outcomes instead of focusing on past and prevent events. It leverages statistical models, historical data, and even machine learning algorithms to answer the question “What will happen?” 

For example, you can implement predictive analytics to forecast when equipment is likely to break down or when a patient can be re-admitted to a hospital. With this capability, a demand for predictive analytics becomes much higher, leading to an over 28% annual growth during 2025-2030. However, one visible downside of this analytical approach is its accuracy, which relies heavily on data quality and the complexity of predictive models. 

Prescriptive Analytics

This is the most advanced type of IoT data analytics. Besides forecasting what may happen in the future, prescriptive analytics uses optimization algorithms and simulated scenarios to suggest the best action for your problem. In other words, it helps answer the question “What should be done?”

For example, prescriptive analytics allows machinery to automatically modify its heating and cooling systems to optimize energy usage. This automates the manufacturing process while ensuring operational efficiency. But like predictive analytics, incorrect data input and too complicated models can affect the accuracy of prescriptive analytics.

Why Should Your Business Conduct IoT Data Analytics?

The demand for IoT data analytics has increased so significantly that its relevant tools become more popular accordingly, with over $47.5 billion estimated in 2025. So, why is that? Let’s take a look at some reasons why analyzing IoT data is increasingly crucial and why you should do it:

Cost Reduction & Operational Efficiency

Imagine this scenario: your equipment is breaking down and it takes a long downtime for repair or maintenance. This can delay your production, significantly affecting the supply chain and making your clients dissatisfied due to longer delivery times. 

But now, by analyzing data from connected devices and sensors, your business can anticipate when and why the machinery is likely to fail. This allows you to take proactive action to deal with this problem, consequently minimizing downtime and optimizing production. With this capability, IoT data analytics proves useful in different sectors, from manufacturing and logistics to healthcare. 

Improved Customer Experiences

When analyzing IoT data, your company can better understand customer behavior and interests. This allows you to personalize customer services and offer better experiences. 

In retail, for example, Sephora uses IoT data to track a shopper’s journey and improve services accordingly. Or such brands as Home Depot and Auchan analyze data from cameras to instantly monitor product availability on shelves and inform shop assistants about out-of-stock products. Without analyzing data, these companies may fail to meet their customers’ real-time demands, missing valuable opportunities to improve services and increase customer satisfaction. 

Innovation & New Revenue Streams

Analyzing IoT raw data thoroughly helps your business recognize emerging trends that reflect unmet customer demands. This allows you to develop new products and services to meet those needs and create new revenue streams. 

GE (General Electric) is a typical example for this case. By leveraging IoT, GE provides its clients with value-added services like predictive analytics and real-time machinery inspection. This not only gives GE new revenue streams but also improves customer satisfaction. 

Enhanced Risk Reduction & Decision-Making

IoT data analytics provides your business with actionable insights to make informed decisions. 

For instance, a transportation company can analyze IoT data to identify traffic patterns and possible weather conditions, looking for optimal routes to prevent accidents or delays. Meanwhile, a manufacturer can conduct real-time analytics to inspect defects instantly during production (e.g., surface cracking). 

Thanks to IoT analytics, these companies can minimize risks incurred by, for example, machinery breakdown, poor quality, or supply chain disruptions. Additionally, they can make immediate decisions to prevent these problems before they happen.

FURTHER READING:

1. 10 Best Data Analytics Software in 2025: A Complete Guide

2. 10 Leading Data Analytics Companies in 2025

3. No-Code Data Analytics: A Comprehensive Guide

5 Real-Life Applications of IoT Data Analytics 

These immense benefits have turned IoT data analytics into something integral in companies. It has a wide application across industries, from manufacturing and smart city to agriculture and healthcare. Let’s discover its top 5 real-life use cases in this section:

1. Smart Manufacturing and Industrial IoT (IIoT)

Various manufacturers like Tesla and Johnson & Johnson DePuy Synthes have built smart factories to automate their production processes and improve efficiency while ensuring good product quality and minimizing downtime. Such companies have adopted IoT with embedded sensors to inspect equipment health (e.g., temperature or vibration) in real-time and identify anomalies, allowing for immediate maintenance and avoiding costly repairs.

They also analyze IoT data to optimize production flow, material usage, and energy consumption. IoT systems can automatically adjust manufacturing parameters to control throughput and minimize waste. Further, they spot defects in products and automatically remove defective products by tracking product quality. 

These capabilities, coupled with a growing adoption of smart factory techs, have fuelled the growth of industrial IoT (IIoT) in recent years. By 2025, its global value is estimated to reach nearly $276 billion.

2. Smart Cities and Infrastructure

Many places around the world have invested heavily in IoT and other advanced technologies to build smart cities and infrastructure. Particularly, they use sensors to manage traffic flow, track energy consumption in public spaces, and monitor waste.

For example, Singapore – a pioneer in smart city initiatives – developed the Electronic Road Pricing (ERP) system to monitor congestion by charging motorists who drive past an ERP gantry during certain times. The nation also introduced the Open Digital Platform to monitor many district functions like security, district cooling, or car park management. Meanwhile, some Australian cities like Sydney or Melbourne use smart bins equipped with sensors to track fill levels and reduce waste volume. 

3. Healthcare and Remote Patient Monitoring

Many hospitals like Mayo Clinic have embraced IoT-based healthcare systems to monitor patients remotely and enhance care delivery. Accordingly, they use IoT data (like heart rate or activity levels) collected from wearable devices to identify anomalies and send immediate alerts. This allows for remote yet prompt diagnosis and minimizes the need for in-person visits. 

Further, IoT sensors can gather data about the location of medical equipment and environmental conditions in hospitals (e.g., humidity or temperature). This data helps medical staff determine whether a certain machine is available and ensure patient safety. 

4. Logistics and Supply Chain Management

Real-time IoT data analytics help companies better manage their logistics and supply chain by tracking assets, optimizing delivery routes, and forecasting delays effectively. Accordingly, sensors can inspect the location and condition of products in transit and at rest in real-time, improving asset usage and minimizing losses. 

Further, IoT data tells you much about traffic conditions, weather patterns, and shipment progress to calculate the best delivery schedules and routes. This not only enhances delivery times and reduces fuel consumption but avoids unexpected delays.

5. Agriculture and Precision Farming

Analyzing IoT data brings huge benefits to agriculture. Particularly, sensors can track variables like temperature, soil moisture, and nutrient levels in real-time to optimize irrigation, pest control, and fertilization. One typical example is the wide application of smart irrigation in California’s vineyards. Many places like Napa’s To Kalon Vineyard use IoT technologies and sensors to calculate factors that can affect the vines’ yields and send the data to a weather station. This station then automatically sends a winemaker precise amounts of water for soil irrigation. 

Further, IoT data analytics allows farmers to manage livestock effectively. By tracking livestock health and location through smart collars, farmers can identify livestock behavior, check health regularly, and track locations. This allows farmers to predict potential illnesses, optimize feeding, and secure livestock from rural crime.

FURTHER READING:

1. 10 Benefits of IoT in Healthcare in 2025

2. What is IoT Cloud Computing? A Comprehensive Guide

3. Future of IoT in Healthcare: 5 Key Predictions for the Next Decade

Key Considerations Before Analyzing IoT Data

IoT data analytics sounds easy but in fact, it doesn’t. Rushing into analysis without careful planning or preparation can result in wasted resources (e.g., time or money) and incorrect outcomes. So, before going to each step of IoT data analytics, you should consider the following key factors:

1. Define Clear Business Goals

Connected devices can generate tons of data each day, but you can’t channel all efforts into analyzing it all. This not only makes you overwhelmed with massive volumes of IoT data but also addresses none of your existing problems. 

So, before analysis, you have to clearly identify your business goals by asking yourself: “What do I want to achieve with IoT analytics?” Is that about reducing equipment downtime by 20% this month? Or do you want to personalize shopping experiences in your store? Setting up clear goals helps you:

• focus analytics efforts on what your business wants to resolve most at present;
• measure how successful your analytics are through relevant metrics (e.g., customer satisfaction)
• assign appropriate resources, expertise, and tools for analytics to avoid waste. 

2. Ensure the Quality, Integrity, and Relevance of Data Input

Remember this motto when implementing IoT data analytics: “Garbage in, garbage out.” It means if your data is incorrect, incomplete, inconsistent, and irrelevant to your goals, you hardly derive a reliable insight that helps solve your business problem. 

To ensure the quality and integrity of your data input, you should choose the right IoT devices and sensors to collect it. Here are some of our tips:

• Select suitable devices that deliver the right data to address your issue;
• Ensure the sensors and devices are correctly calibrated and frequently maintained;
• Assess whether the sensors and devices are resilient to harsh environmental conditions (like temperature) to ensure their accuracy and durability;
• Ensure the security of the sensors and devices;
• Consider the type and amount of IoT data the devices can collect;
• Evaluate whether the devices can integrate with your current systems.

3. Select the Right IoT Analytics Platforms

There are hundreds of IoT analytics platforms in the market. Each comes with different features, presenting pros and cons. Choosing the wrong tool can make your business fail to handle and analyze data effectively. Different companies have different requirements for analytical platforms. But here are some common factors to consider:

• Must-have functionalities like analytical capabilities, data visualization, and reporting;
• Security measures;
• Ability to process data in real-time;
• Scalability (i.e., the ability to process the increasing volume of IoT data over time);
• Integration (i.e., the ability to incorporate your data sources and current systems);
• Deployment methods (i.e., cloud-based or on-premises);
• Cost and licensing models.

5 Steps to Implement IoT Data Analytics

Now, when you already have good preparation, it’s time to embrace your IoT data analytics. Normally, the analytics process often covers the following four steps. These steps may vary across companies depending on their analytical purpose.

Step 1: Data Collection and Integration

The first step is collecting data from different IoT devices through embedded sensors. Normally, these sensors can detect changes in temperature, pressure, etc. and send the data to IoT analytics tools. By integrating data from various IoT sources, you’ll have a unified view of all the data needed for further analysis. 

However, there are some challenges of gathering data from these IoT devices. As we know, IoT devices can vary in shapes and functions, from factory equipment to smartwatches. They may use different communication protocols, let alone their different data formats and structures. This makes it hard to collect them from multiple devices. Further, vast volumes of data and unreliable network connections, especially in remote environments, may result in data loss and delays. 

To resolve these problems, we need APIs. Accordingly, your business can customize APIs that allow IoT devices to communicate data seamlessly. Besides, many IoT analytics tools like AWS IoT Analytics or Google Cloud IoT Core integrate APIs to facilitate data ingestion by standardizing communication between devices, translating data formats, and enabling secure data exchange. 

Step 2: Data Processing and Storage

Once your data is gathered, it’s time to process it effectively and store it securely. This step often occurs in gateways or cloud platforms. Accordingly, data processing often involves removing errors (like duplicates or outliers) and converting data into appropriate formats. This ensures your data will be consistent and accurate for effective analyses. 

Upon data cleaning and processing, you need to store it in a secure place. As the amount of IoT data can be vast and changeable, it’s crucial to have scalable storage solutions. Cloud-based platforms are a good option in this case. They provide built-in object storage services (data lakes) to keep raw, unprocessed, and long-term data. Besides, they can integrate external databases (Relational and NoSQL) to store data used for real-time analysis. 

Step 3: Data Analysis

Upon data processing, you’ll embrace IoT data analytics by leveraging built-in analytical features within platforms and analytical techniques we mentioned above (i.e., descriptive). The primary aim of this step is to recognize patterns, anomalies, and correlations within IoT data. 

Today, many IoT analytics platforms can automate parts of data analysis. They provide pre-built analytical models for common applications, like trend analysis, anomaly detection, and predictive maintenance. Besides, they can automatically compare data input with defined thresholds and send alerts when these thresholds are reached. This functionality is extremely useful for equipment monitoring and supply chain management. 

Also, some platforms (e.g., Microsoft Azure IoT Hub) integrate AutoML capabilities (e.g., Azure Machine Learning) to optimize your analytical efforts. AutoML enables even non-technical users to build customized machine learning models that automate some analysis tasks for specific needs. 

Step 4: Data Visualization and Actionable Reporting

This step is about visualizing your analytical results to make them understandable to your audience. Data visualizations like interactive dashboards or graphs emphasize key insights and ease informed decision-making. Today, IoT analytics platforms allow you to automatically create these visualizations within seconds. 

However, building data visualizations isn’t enough. As a data analyst, you may grasp every piece of your analytical finding, but it doesn’t mean your audience can. Therefore, you need data evaluation skills, or in other words, how to interpret your findings so that the audience can use them to take appropriate action. 

In some sectors like manufacturing, automated reporting and notifications are sent in real-time to notify users of anomalies or crucial incidents (e.g., equipment overheating). 

Step 5: Feedback Loop

Your analysis is not always accurate. That’s why you need a feedback loop that allows everyone involved to offer feedback on visualizations and insights. This feedback is used to assess your analysis’s success by seeing whether it brings valuable and relevant insights to address your initial problem effectively. Then, you can make improvements in your analytical approach to ensure it’ll meet your business needs afterwards. 

Conclusion: Embracing the Data-Driven Future with IoT Analytics

IoT data analytics is an indispensable part of many sectors, be it manufacturing, agriculture, or healthcare. Understanding the four types of analytics, having careful preparation, and following detailed steps help you analyze this data efficiently and bring valuable results for your company. 

If you’re struggling with IoT analytics, Designveloper is here to help! With over a decade working for 100+ projects across industries, we’ve helped our clients transform raw data into actionable insights that support their decision-making and improve operational efficiency. Our customized IoT solutions, especially in healthcare, enable clients to track and visualize real-time data to identify hidden patterns, trends, and even abnormal events that may affect their operations.

Do you want to make full use of data to benefit your business activities with IoT analytics? Contact us now and discuss your idea further!