IoT Analytics: Challenges, Applications, and Innovations

(Last update: 21 October 2022)

If you’re trying to get a handle on large volume of IoT streaming data, you’re likely to run into some challenges when designing your data pipelines, storage, and analytics stack. Check out our ultimate guide to streaming data architecture to discover the best practices and tools you need to know in order to operationalize IoT data at scale.

IoT analytics is a new and challenging field. It takes in huge volumes of heterogeneous data from IoT devices, with the objective of processing, storing, and extracting business value from them. This requires a combination of tools, from data lakes to stream processing frameworks and analytics tools. 

This article will help you understand how IoT analytics is used in different industries, what are the key challenges involved in extracting value from IoT data, and how to solve them with a state of the art IoT analytics infrastructure.

What is IoT Analytics?

The objective of IoT analytics is to gain value from large volumes of data generated by devices connected via the Internet of Things (IoT).

IoT analytics is typically connected to the Industrial IoT (IIoT). Organizations use IIoT to collect and analyze data from pipelines, weather stations, sensors on manufacturing equipment, smart meters, delivery trucks, and other machinery. IoT analytics is also used in retail, data center management, healthcare.

IoT data is a subset of big data, and is constantly growing in volume, variety and velocity (the 3Vs model). It consists of heterogeneous streams that need to be transformed and combined to produce current, comprehensive and accurate information for business analysis and reporting. Many IoT devices were not developed for compatibility with other IoT devices and systems. IoT data integration is thus complex, as is the analytics that relies on it.

Types of IoT Analytics

Descriptive analytics on IoT data

Focuses on what’s happening, by monitoring the status of IoT devices, machines, products and assets. Determines if things are going as planned, and notifies if anomalies occur. Descriptive analytics is generally implemented as dashboards that show current and historical sensor data, key performance indicators (KPIs), statistics and alerts.

Addresses questions such as:

• Are there any anomalies that demand attention?
• What’s the utilization and throughput of this machine?
• How are consumers using our products?
• Where do my assets reside?
• How many components are we creating with this tool?
• How much energy is this machine using?

Diagnostic analytics on IoT data

Answers the question: why is something happening? Analyzes IoT data to identify core problems and to fix or improve a service, product or process.

Diagnostic capabilities are typically extensions to dashboards that permit users to drill into data, compare it, and visualize correlations and trends in an ad-hoc manner. Many organizations employ domain experts knowledgeable about a specific process, machine, device or product, rather than data scientists, to perform diagnostics on data.

Addresses questions such as:

• Why is this machine producing more defective parts than other machines?
• Why is this machine consuming excessive energy?
• Why aren’t we producing enough parts with this tool?
• Why are we getting a lot of product returns from American customers?

Predictive analytics on IoT data

Raises the question: what will happen? Assesses the likelihood that something will happen within a specific timeframe, according to historical data. The aim is to proactively take corrective action before an undesired outcome occurs, to mitigate risk, or to isolate opportunities.

Typically implemented via machine learning models that are trained with historical data, and stationed on the cloud so that they can be accessed by end-user applications.

Addresses questions such as:

• What’s the likelihood of this machine failing in the next 24 hours?
• What is the anticipated useful life of this tool?
• When should I service this machine?
• What will be the demand for this feature or product?

Prescriptive analytics on IoT data

Poses the question: what action should I take? Suggests actions based on the result of a prediction or diagnosis, or provides some visibility to the rationale behind a prediction or diagnostic. Recommendations tend to be about how to optimize or fix something.

Addresses questions such as:

• This machine is 80 percent likely to fail in the next 12 hours. How should I prevent this?
• The overall equipment effectiveness (OEE) of this machine is low. How can I improve it?
• This machine is creating too many defective components. How can I avoid this?
• This design is resulting in too many manufacturing issues. How can I improve it?

Use Cases of IoT Analytics

Optimizing marketing and sales

IoT analytics can help optimize marketing and sales for companies selling large quantities of physical items:

• Forecasting customer needs—helps analyze customer trends and needs based on product reviews and usage, anticipate future purchases, and assists with the development of consumable resupply models.
• Helps deliver new services—aggregates data from primary sources to perform analysis and make predictions.
• Flexible pricing and billing—captures pertinent data from sources, helps create outcome-based subscription and pricing models.

Real-time data analysis for manufacturing  

Manufacturers in industries including automotive, electronics, durable goods, and chemicals, have invested in IoT analytics to improve production efficiency. They use manufacturing equipment with intelligent sensors to help with smart manufacturing. This aids in cost containment and revenue generation, for example, by saving on energy costs.

Monitoring of healthcare devices and patients

The development of health apps and connected medical devices has lead to patient-centered analytics. The apps or devices are programmed to automatically provide alerts and initiate a response from a healthcare professional when a health problem is detected. For example, an inhaler with sensors to monitor environmental conditions that can affect asthmatic patients.

Sensors are now embedded in diagnostic equipment, personal health and fitness equipment, surgical robots, drug dispensing systems, and implantable devices. These sensors enable real-time monitoring of patients, and also monitoring equipment to minimize downtime and avoid failures.

Predictive maintenance

IoT analytics can be applied to a predictive maintenance model, where sensors keep track of the condition of infrastructure and equipment. For example, sensors embedded in roads or train tracks can relay ultrasonic and vibrational data in real time, allowing maintenance teams to repair vulnerable sections of the road or track before they are damaged.

3 IoT Analytics Challenges  

One way to view IoT analytics challenges is to consider a possible IoT deployment. Let’s take the following scenario. A huge industrial food storage warehouse and distribution center uses Internet-connected devices to maintain the temperature of specific zones, such as a refrigeration area for items that need ongoing, non-freezing cooling, and a freezer area for items that need to be consistently frozen.

1. Too much data

The total amount of data being collected may be so large that it may not be possible to move it over the network to a central location. Take, for example, a single outside temperature sensor in the warehouse. To fulfill its role it transmits data, including temperature, humidity, battery level, software versions, hardware versions, and motion/position changes.

Sensors could transmit this information every 30 seconds, and there could be several hundred of these sensors across the warehouse. This may be only one of dozens of sensor types, requiring a robust data ingestion pipeline.

2. Security

It is essential for connected devices to work together for most IoT use cases, but this approach raises security issues.

The overall security profile is only as effective as the weakest device. If the security on a specific vendor’s outdoor sensor is weak, and the sensor is connected to other devices, the likelihood of ‘indirect’ critical impact is high. Attackers can compromise the sensor and modify its data or exploit the connection to other devices to cause damage.

For example, a breached sensor could provide the system with an incorrect outdoor temperature reading to the system. The system could adjust a zone temperature in a way that destroys the food in that area.

3. Misbehaving devices

These are devices or sensors that go bad and begin sending false readings to the system. For example, a low battery, a software bug, or a hardware failure, could cause such readings. This could ruin the inventory of the warehouse.

Data Infrastructure for IoT

IoT analytics requires three key components to operate: storage, stream processing software, and an analytics engine.

IoT Analytics Storage

In an IoT architecture, there are thousands of sensors collecting huge volumes of unstructured data, from clickstream data to video footage. Modern data streaming architectures use data lakes like Amazon S3 to store this raw data. The benefits of data lakes are that they can grow indefinitely, integrate with many processing and analytics tools, and provide a relatively low cost of storage.  You can read more about the advantages of data lakes.

To enable analytics on IoT data, organizations need to plan their storage carefully. Just dumping data into a data lake with no prior treatment can create a data swamp. Upsolver SQLake  is a a declarative data pipeline platform for streaming and batch data.  It can save IoT data to a data lake in a format that enables SQL-based analysis by traditional analytics tools. (Learn more about avoiding the data swamp)

Stream Processing

Stream processing allows you to analyze continuous data flows in memory, with only state changes transported to a database or file system. This process, called Change Data Capture (CDC), is useful in an IoT setting as it permits a system to recognize relevant information while removing less useful data points.

An event stream processor, like Kafka, lets you write logic for each actor, representing a type of IoT device which is transmitting data, wire the actors up, and connect them to data sources. Connecting the stream processor to large numbers of data sources in an IoT environment, and managing storage effectively, is a major challenge and requires data engineering expertise.

Analytics Engine

Several vendors provide purpose-built analytics engines designed to work with IoT data. You can use one of these solutions, or analyze IoT data directly with standard analytics tools, like you would any type of big data.

AWS IoT analytics

AWS IoT analytics transforms, filters and enriches IoT data prior to storing it in a time-series data store for analysis. It collects data from your devices, transforms it into a usable form, enriches the data with device-specific metadata, and stores the processed data.

You can then analyze data by initiating ad-hoc or scheduled queries using the built-in SQL query engine, or run machine learning algorithms on the data. AWS IoT analytics includes pre-built models for common IoT use cases like predictive maintenance and smart agriculture.

Azure IoT analytics

Azure Stream Analytics integrates with open source cloud platforms to provide real time analytics on data from IoT applications and devices.

Azure IoT analytics allows you to:

• Develop massively parallel Complex Event Processing (CEP) pipelines
• Scale instantly
• Build real-time dashboards
• Guarantee high availability for IoT data
• Create compliance audits

End-to-End IoT Analytics Solution: Upsolver for IoT analytics

Upsolver SQLake is a declarative data pipeline platform for streaming and batch data. SQLake enables you to transform IoT streaming data into workable data, enhancing time-to-value and increasing the success rate of streaming data projects.  Using only SQL, you can easily develop, test, and deploy pipelines that extract, transform, and load data in the data lake and data warehouse in minutes instead of weeks.  SQLake simplifies pipeline operations by automating tasks like job orchestration and scheduling, file system optimization, data retention, and the scaling of compute resources.

Upsolver’s platform enables a variety of high-value analytics use cases such as remote device monitoring, user behavior, and log analytics.

Learn more about solutions for any IoT streaming data challenge:

• Try SQLake for free for 30 days. SQLake is Upsolver’s newest offering. It lets you build and run reliable data pipelines on streaming and batch data via an all-SQL experience. Try it for free. No credit card required.
• Visit our SQLake Builders Hub, where you can read learn more about how SQLake works, read technical blogs that delve into details and techniques, and access additional resources.
• If you have any questions, or wish to discuss this integration or explore other use cases, start the conversation in our Upsolver Community Slack channel.
• Read about the challenges (and solution) of using a database for your IoT streaming data.
• Speak with one of our data experts about scaling your IoT streaming data.