The Internet of Things (IoT) is a fast-growing industry that deals with the connection between physical objects and their exchange of data over the Internet. With our increasing dependence on interconnected technology, it's important to have data analytics in place to help us understand our usage habits and needs. With IoT, the amount of data collected can be overwhelming without proper analysis tools in place. Not to worry – there’s help. In this article, we'll discuss how analytics is used in IoT and why it's so valuable for businesses.

Tools

One of the first tasks that a business must do to implement an IoT system is to gather as much data as possible, which includes both quantitative and qualitative information. The next step is to analyze the data for insights and produce actionable results. Using traditional data management and analytics tools for analysis can take several hours, just to manually go through the data to uncover insights.

Thankfully, there’s a better way. Reputable companies have already brought several cloud-based tools to market, offering a variety of solutions to ease the analysis process and help decision-makers swiftly uncover insights. These include:

• Microsoft Azure IoT Suite
• AWS IoT Analytics
• Google Cloud

Microsoft's Azure IoT suite provides a cloud-based analytics solution that not only automates analysis and insights but also makes it easier to deploy the right tools to get this job done. With Azure IoT Hub as the central hub for data management and processing, Azure Stream Analytics for real-time event handling, and Azure Machine Learning which can be used for predictive analysis, Azure IoT Suite builds a unified analytics service that can be used to optimize business outcomes.

Amazon Web Services (AWS) and Google Cloud likewise offer analytics services for their IoT solutions.  AWS offers AWS IoT Analytics as a service that provides the ability to ingest and process data at scale, analyze it in real time, and create predictive models using machine learning. Google Cloud's AI and machine learning products make it feasible for developers to train their own machine learning models and find public datasets that they can download and use.

Strategy and Business Value

The right tools are a great place to start, but they’re only part of the solution. A solid analytics strategy is key to the success of IoT initiatives.

Analytics keep a pulse on the business and support reaching targeted business objectives. Quality analytics are also necessary for the continual improvement of IoT initiatives through feedback loops which drive improvements in customer satisfaction, cost savings, or any other desired outcome. Analytics can help organizations quickly identify problems with their data infrastructure, as well as optimize how they collect and store data so that processes run smoothly with minimal wasted time or resources.

Real-world Applications

One example where analytics and IoT have been used together is in data centers. Industrial IoT is often employed to augment data center functions, automating tasks that are prone to error. Industrial IoT has been successfully deployed for remote monitoring and diagnostics across logistics facilities, managing inventories more efficiently through automation, and limiting human intervention where possible so that staff can focus on other tasks.

Streaming analytics software can also be used in conjunction with predictive analysis to optimize efficiency and reduce errors by predicting when equipment will need maintenance or replacement before it breaks down.

Data centers benefit from the ability to better manage their energy usage and costs by monitoring what is going on in real-time thanks to analytics, which can also help them make more informed decisions about when they should deploy hardware or software upgrades as well as potential changes that may need to be made with cooling systems.

Another type of business that has benefitted from utilizing IoT and analytics is Third-party Logistics (3PL). Several 3PL firms have been able to utilize IoT sensors to monitor cargo, improve route efficiency, and reduce fuel consumption – among other things. An example of this is the use of IoT sensors to monitor refrigerated cargo, which can help managers determine when it's time for a shipment to be delivered or brought into storage before spoilage becomes an issue.

Customers in the B2B and B2C spaces benefit from analytics and IoT in many ways as well. One example is that customers are now better informed about how their goods were shipped thanks to tracking information. This information, automatically integrated by the software such as the tools discussed above, is

No matter where your company or analytics department is in the IoT adoption journey, one thing is certain – as organizations mature their use of IoT, it's essential to have a robust data collection and analysis system in place for the organization to continue to grow and thrive. As IoT technologies like wearables or drones are increasingly incorporated into operations, you won't be flying blind--you'll know exactly what lies ahead.

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