IoT Analytics: Benefits, Challenges & Use Cases

Many businesses today are using IoT analytics to make strategic decisions. A decade ago, such insights were very difficult to obtain quickly. IoT analytics’ ability to transform business strategy making is one of the key factors behind the recent boom in IoT adoption worldwide.

What is IoT analytics?

IoT analytics refers to data analysis tools that analyze the large quantities of data generated by thousands of IoT devices.

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In many ways, IoT analytics is related to big data. Firstly, IoT analytics uses analytical techniques used in big data. Secondly, the volume of data analyzed in IoT analytics is comparable to the volumes analyzed in big data analytics. Even though IoT and big data are distinct concepts, the emerging field of IoT analytics has formed a bridge between the two.

How does IoT analytics work?

IoT analytics work in the following manner:

1. Unprocessed raw data recorded by IoT sensors is collected from the entire IoT network. The data is usually in multiple formats.
2. The data collected in step 1 is processed to clean it up for analysis.
3. The cleaned up, processed data is stored in time-series format.
4. IoT analytics tools use various techniques to analyze the time series data and present insights via user-friendly dashboards.
5. Businesses act on these insights to improve their operations.

Types of IoT analytics

IoT analytics use several big data analytic techniques, such as:

1. Descriptive analytics: These analytics indicate whether business operations are going as planned and alert when disruptions occur.
2. Diagnostic analytics: These analytics deal with the identification of problems in business operations.
3. Predictive analytics: As the name suggests, predictive analytics uses machine learning capabilities and historical data to provide predictions on the performance of business operations and assets.
4. Prescriptive analytics: Prescriptive analytics deals with providing actionable steps to take to fix problems in business operations.

Benefits of IoT analytics

IoT analytics offer several benefits for businesses that use them:

1. Visibility on the entire IoT network – IoT analytics enables businesses to oversee the performance of their IoT network in real-time.
2. Fast identification and resolution of problems in business operations – Businesses can use diagnostic analytic capabilities to quickly identify performance problems and use prescriptive analytics to fix such problems.
3. Better asset utilization – Businesses can use IoT analytics to monitor the performance of their assets, such as machinery, and tweak their utilization to ensure the long term health of assets.
4. Cost optimization – IoT analytics help identify areas of cost reduction and steps to implement to achieve such cost reduction.
5. Expansion into new markets – IoT analytics offer valuable insights on operations and consumer behavior to ease expansion into new markets.
6. Improved product development – Businesses can study historical trends in product usage by consumers to identify areas of improvement for future versions of their products.
7. Better customer experience – IoT analytics helps businesses identify customer problems in real-time and act quickly to fix those problems, thus enhancing customer experience and delight.

IoT Analytics: Challenges

Businesses may face challenges in using IoT analytics tools. Some of these challenges are –

1. Excessive data generation and storage requirements –The aggregate data generated by thousands of IoT sensors are usually very large, thus making it expensive to manage and store such data.
2. The complexity of data – Data from multiple IoT devices consists of different types, formats and sizes, thus making it very complex and difficult to process and clean.
3. Security – Businesses, especially those dealing with consumer data, have to take various security measures to protect the stored IoT network data against hacking attempts and leaks.
4. Inaccurate data – Faulty IoT devices lead to inaccurate measurements, thereby messing up the analysis of such data. When you have faulty IoT devices at large, the insights offered by IoT analytics tools become unreliable.
5. Building a competent data analysis team – Businesses need to hire data scientists and analysts to run analytical techniques on the IoT data and derive actionable insights.

IoT Analytics: Use Cases

IoT analytics are useful in many ways, such as:

1. Predictive maintenance of machines – IoT analytics can predict when machines will break down. Businesses can use such predictions to conduct maintenance activities before such breakdowns actually occur.
2. Facilitating updates to consumer product software – IoT analytics can alert businesses when consumer products are malfunctioning. Businesses can react quickly and update the consumer product software via on the air updates.
3. Tracking inventory – IoT analytics help businesses track shelf inventory and avoid situations, such as stockouts.

How to select an IoT analytics tool?

Clearly, IoT analytics is empowering businesses to become agile and respond faster to rapidly changing business environments. Given the challenges involved in using IoT analytics tools, businesses need to be careful when trying to select an IoT analytics tool. The following factors can help them make a sensible decision –

1. Integration with Enterprise apps – IoT analytics tools need to have the capability of integrating with enterprise apps used by the business. This enables businesses to manage their data across multiple apps seamlessly.
2. Security – IoT analytics tools need to have built-in security features. For example, the Airtel IoT hub provides telco-grade security for IoT data with a dedicated private network.
3. Cloud-based – Cloud-based analytics tools are much cheaper to use as the data is stored on cloud servers.
4. Customization – IoT analytics tools should give users the option to run custom analytic techniques as well as create custom dashboards for data management.

By keeping these factors in mind, businesses can extract the full potential of IoT analytics and mitigate the challenges in implementation.