IoT Analytics: Challenges, Applications, and Innovations

IoT analytics has emerged as a new and rather challenging field. It works by taking in a large amount of heterogeneous data from the Internet of Things (IoT) devices, with the aim of storing, processing, and extracting useful business insight from them. This, therefore, requires a combination of tools, such as stream processing frameworks, data lakes, and analytics tools.

The essence of this article is to help you understand how different industries are using data infrastructure for IoT analytics, what the core challenges involved in value extraction from IoT data are, as well as how to handle them with an innovative IoT analytics system.

What is IoT Analytics?

The goal of IoT analytics is to obtain insight from huge data volumes generated by devices that are connected through the Internet of Things (IoT).

Basically, IoT analytics has a connection to the Industrial IoT (IIoT). Businesses utilize IIoT for data collection and analysis from weather stations, pipelines, sensors on manufacturing equipment, delivery trucks, smart meters, as well as other machinery. Other sectors such as retail, data center management, healthcare, etc. also make use of IoT analytics.

IoT data can be described as a subset of large data and is continually increasing in velocity, variety, and volume (known as the 3Vs model). It comprises heterogeneous streams that must be transformed and integrated to achieve current, accurate, and comprehensive information for effective business analysis and reporting.

Types of IoT Analytics

• Descriptive analytics on IoT data

  This type of IoT analytics deals with what’s happening, by tracking the status of IoT devices, assets, products, and machines. It also determines if all is going as planned and sends notifications in the occurrence of any anomalies. Generally, descriptive analytics is implemented in the form of dashboards that display past and current statistics, key performance indicators (KPIs), alerts, and sensor data.

  Descriptive analytics focuses on such questions as:

  • Is there any anomaly that should be attended to?
  • What exactly is the use and production rate of a given machine?
  • How do consumers use our products?
  • Where do our assets reside?
  • What is the quantity of components we are creating with our tool?
  • What is the energy consumption of this machine?

• Diagnostic analytics on IoT data

  It addresses the question: why is this thing happening? Diagnostic analytics analyzes IoT data so as to identify key issues and to solve or even improve a process, product, or service.

  Diagnostic functionalities are usually extensions to dashboards that allow users to access data, make comparisons, and visualize trends and correlations in an ad-hoc method. Several businesses hire domain experts with knowledge about a particular device, machine, product, or process, instead of data scientists, to carry out diagnostics on data.

  Diagnostic analytics addresses such questions as:

  • What makes this machine produce more defective components than others?
  • Why does this machine consume so much energy?
  • Why is the company not producing sufficient components with this machine?
  • Why do we get plenty of product returns from Asian customers?

• Predictive analytics on IoT data

  The essence of predictive analytics is to raise the question: what is going to happen? Hence, it assesses the possibility that an event is going to occur within a certain timeframe, as observed from historical data. The goal is to proactively execute corrective actions prior to the occurrence of an undesired result, isolate opportunities, or to mitigate risk.

  It is often implemented through ML models that have been trained with historical data and positioned on the cloud so that end-user applications can access them.

  It addresses such questions as:

  • What are the odds of this machine breaking down within the next 24 hours?
  • What is the expected useful tenure of this device?
  • When are we supposed to service this machine?
  • What will be the demand for this product or feature?

• Prescriptive analytics on IoT data

  Prescriptive analytics poses the question: what actions are we to take? Hence, it suggests actions according to the outcome of a diagnosis or prediction or offers some visibility to the rationale behind a diagnosis or prediction. Recommendations are usually about how you can fix or optimize something.

  Prescriptive analytics addresses such questions as:

  • This machine has an 80% chance of failing in the next 14 hours. How do I prevent this?
  • This machine has low overall equipment effectiveness (OEE). How should we improve it?
  • This machine has been producing excessive defective parts. How do we stop this?
  • This design is causing a lot of manufacturing problems. How can we improve this?

Use Cases of IoT Analytics

1. Optimizing marketing and sales

   IoT analytics can assist in the optimization of marketing and sales for enterprises selling large amounts of physical products:

   Forecasting customer needs — assists in analyzing customer needs and trends according to product usage and reviews, expect future purchases and helps in developing consumable resupply models.

   Aids delivery of new services—aggregates information from original sources to carry out analysis and make forecasts.

   Flexible billing and pricing—captures relevant data from sources, assists in the creation of result-based pricing and subscription models.

2. Real-time data analysis for manufacturing

   In order to improve production efficiency, manufacturers in industries such as durable goods, automotive, chemicals, and electronics, have made investments in IoT analytics. They utilize manufacturing equipment that has intelligent sensors to assist in smart manufacturing. By implication, this brings about revenue generation and cost containment, for instance, by saving on energy expenses.

3. Monitoring of healthcare devices and patients

   The development of connected medical devices and health apps has resulted in patient-centered analytics. The devices or apps are programmed to automatically send notifications and prompt a response from healthcare workers when any health issue is detected. A great example would be an inhaler that features sensors to track environmental factors that may cause harm to asthmatic patients.

   Sensors are now being incorporated into surgical robots, diagnostic equipment, drug dispensing systems, implantable devices, and personal health and fitness equipment. These sensors allow real-time supervision of patients, as well as also tracking equipment to reduce downtime and prevent malfunctions

4. Predictive maintenance

   IoT analytics can also be applied to a predictive maintenance model, where sensors monitor the status of equipment and infrastructure. For instance, sensors that have been embedded in train tracks or roads can send vibrational and ultrasonic data in real time, enabling maintenance departments to fix vulnerable areas of the track or road before serious damage occurs.

IoT Analytics Challenges

• Excess data

  The overall volume of data that is collected may be so huge that organizations may find it impossible to transfer it over the network and onto a central system. Let us take, for instance, a single external temperature sensor in a warehouse. To achieve its purpose, it relays data, including battery level, temperature, software versions, humidity, hardware versions, and position/motion changes.

  Sensors could relay this information every minute, and we could have tons of these sensors all over the warehouse. This might be just one of many other sensor types.

• Security

  It is important for connected systems to work together for several IoT use cases. But this then raises security concerns, which is one of the prevailing IoT analytics challenges.

  The general security profile is just as effective as the weakest system. If a specific vendor has weak security on their outdoor sensor, and this sensor is connected to other systems, there is a high chance of ‘indirect’ critical impact. The sensor can be compromised by attackers, who will alter its data or take advantage of the connection to other systems to wreck damage.

  For instance, when a sensor is breached, it could provide an inaccurate external temperature reading to the system. In response, the system could modify a zone temperature such that it leads to the destruction of the food in that area.

• Devices misbehaving

  These refer to sensors or devices that go bad and start to send wrong readings to the system. An example is a low battery, a hardware failure, or a software bug, which could relay such readings. Consequently, the inventory of the warehouse could be ruined.

Data Infrastructure for IoT

For IoT analytics to operate, 3 core components are needed: storage, stream processing software, and an analytics engine.

1. IoT Analytics Storage

   In a typical IoT infrastructure, there are a plethora of sensors gathering large amounts of unstructured data, including video footage and clickstream data. Contemporary data streaming infrastructures utilize data lakes such as Amazon S3 for storing this raw data. The advantages of data lakes are that they integrate with many analytics and processing tools, can improve indefinitely, and deliver a relatively low storage cost.

   To implement analytics on IoT data, it is necessary for your to carefully plan your storage. Simply dumping data into a data lake without prior treatment can result in a data swamp.

2. Stream Processing

   Stream processing enables businesses to analyze continuous data flows in memory, with just state modifications transported to a file or database system. This process, known as Change Data Capture (CDC), is of great use in an IoT architecture as it enables a system to identify useful information while filtering less relevant data points.

   With an event stream processor, such as Kafka, you can write the logic for individual actors, representing a form of IoT device that is relaying data, link the actors up, and connect them to data sources. Connecting the stream processor to huge amounts of data sources in an IoT setting, and effectively managing storage, is a great challenge and demands data engineering expertise.

3. Analytics Engine

   Numerous vendors offer purpose-designed analytics engines built to operate with IoT data. Companies can use one of these solutions, or directly analyze IoT data with standard analytics tools, just as they would any kind of huge data.

4. AWS IoT analytics

   AWS IoT analytics enriches, filters, and transforms IoT data before storing it in a time-series data storage for analysis. It gathers data from devices, transforms it into a usable format, enhances the data with device-specific metadata, and then stores the processed data.

   Your business can then carry out data analysis by initiating scheduled or ad-hoc queries utilizing the built-in SQL query engine or execute machine learning (ML) algorithms on the data. AWS IoT analytics comprises pre-built models for typical IoT use cases such as smart agriculture and predictive maintenance.

5. Azure IoT analytics

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

   Azure IoT analytics enables you to:

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

Final Thoughts

As proven by early adopters, industrial organizations and interconnected factories are enjoying the benefits of increased capacity, swifter communication, visibility into operations, and many more.

Indeed, the IoT has had a tremendous impact on several industries (particularly manufacturing), and this is just the starting point. There is a lot we can achieve with IoT analytics. We have explained the different popular use cases above, but again, the opportunities are endless! It’s high time you also started reaping the benefits of IoT analytics in your business.