IoT devices are all around us with the promise of making our lives easier and more efficient. In manufacturing plants, IoT sensors monitor machine performance, leading to more streamlined workflow and well-maintained machines. In retail, wearable IoT devices give employees quick access to inventory information and the ability to complete customer transactions at any spot in the store, leading to improved customer service.

Thanks to the advent of IoT, this is all possible by connecting previously unconnected devices to the internet and exchanging data. The benefits of IoT data have driven consumer and business adoption, resulting in even more IoT devices coming to market over the past several years. Billions of these gadgets have been purchased already, and the market growth of IoT devices is expected to accelerate over the next few years.

IoT data analytics

Enterprises are also beginning to utilize IoT data to improve business operations in various ways. More specifically, insights from analyzing this data can be used to make more informed business decisions (often referred to IoT analytics or IoT-A). The same data that automates consumers lives can also be used by businesses to gain insights into customer experiences and behavior and drive tangible product improvements and make other business decisions based on real-time information. The challenge to IoT data, though, is that it is constantly streaming and high volume. Capturing, storing and analyzing IoT data are not simple tasks.

Incorporating IoT analytics into business strategy

The main challenge for companies seeking to incorporate IoT data analytics as part of their business strategy is supporting the computing load associated with IoT analytics. An IT solution must be robust enough to handle increased and constant data load, which has a direct impact on an enterprise’s IT infrastructure, data management strategy, analytics tools, and security approaches.


The overall IT infrastructure must be expanded to allow for the influx of IoT data. Companies need to identify which data storage solutions and BI tools will be used and how they will work collaboratively and in a streamlined fashion. For example, some companies prefer Hadoop frameworks for storage because of their enormous processing power and vast capacity for parallel computing. In combination with a system such as NoSQL, a business could be on their way to creating a powerhouse solution for capturing, processing, and analyzing IoT data. There are many other tools and solutions available, so it is possible to create a perfect, customized IT infrastructure to support IoT analytics.

Data management strategy

IoT devices provide an immense amount of data, but not all this data is necessarily essential or usable for improving business insights. Companies must define a data management strategy that includes IoT data, identifying which data should be captured, how it is filtered and where it is stored. To be useful, it is also important that data is consistent across datasets and compatible with analytics tools. In some cases, it may be beneficial to integrate IoT data with other enterprise data to enhance data visualization and/or predictive analysis. This should all be detailed in the overall data management strategy.


Analytics and predictions

The power of IoT data for enterprises comes in the way of analytics. With the constant stream and large quantity of IoT data, the analysis of this data must be automated in order to be valuable. The system may also need to be configured to pull and analyze a combination of real-time and historical data. Machine learning algorithms can be utilized to perform predictive analysis to improve business forecasting abilities.


It is important to consider security when planning for integration of IoT data. This data is streaming from an array of connected devices all over the world. To guarantee the IoT data is trustworthy, set up checkpoints to confirm valid security certificates before allowing IoT data into the enterprise IT solution.

Opportunities of IoT data analytics

Setting up or expanding enterprise IT systems to handle the enormous computing capacity of IoT data analytics require thoughtful planning and implementation, but the rewards outweigh the challenges. There are endless opportunities where IoT data analytics can be leveraged to boost insights and improve business decisions, including:

1. Sustainable agriculture

Modern agriculture has gone digital, and IoT devices can be found in everything from crop and soil sensors to livestock monitors. The data from these devices is captured and analyzed to provide real-time monitoring of farms, but the predictive analytics of this data is also moving agriculture into a more sustainable world. Problems can now be predicted and corrected, leading to efficient harvest and better livestock health


2. Improved healthcare

IoT devices are becoming popular in the healthcare industry. From connected blood pressure monitors and thermometers to wearable and chipped heart monitors, patient health can be monitored in real-time. Analysis of this patient data gives medical professionals better insights into not only individual patient health, but also trends across large populations of patients. This is leading to better diagnostics and preventive care, and more efficient and targeted care by medical professionals.


3. Personalized customer experience

Customers have come to expect personalized service when interacting with digital services and products. IoT data and the analysis of this data can enhance business insights and the ability to create the best experience for customers. IoT data reveals a wealth of customer behaviors and preferences. Over time, the cumulative IoT data can be analyzed and paired with machine learning algorithms, to build a more customized experience for each customer and can even begin to predict customer needs. For example, if a customer asks their virtual assistants to order take out dinner and play jazz music several evenings per week, that data can be used to improve that customer’s experiences and give personalized recommendation across other products on the same platform.


4. Product improvement and innovation

IoT data tracks product performance metrics for all kinds of connected devices, such as automobiles and smart home gadgets. IoT analytics can use these metrics to improve the next generation of product and even predict possible product glitches or failures. The result of IoT data analytics can also be used to identify future consumer needs and leveraged to spark innovation of new products.

5. Energy sector and smart meters 

The energy sector is beginning to embrace IoT technology with the use of smart meters. These web-connected devices eliminate the need for meter readers by transmitting accurate energy usage data directly back to the data center. IoT data, coupled with analytics, will improve real-time monitoring, diagnostics into system irregularities, and allow energy companies to predict and forecast potential energy spikes and future outages.

The popularity of IoT devices over the past few years has brought with it an abundance of data. Data is continuously streaming from smartphones, smart home devices, and connected automobiles, to name just a few. For enterprises seeking to capture and capitalize on IoT data, it is important to plan and build a robust IT solution that can integrate this high velocity of data and analyze it. It is IoT analytics that unlocks the value of IoT data.

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