The Internet of Things (IoT) is a factor in almost all industries today, from manufacturing to energy to retail. And as the IoT grows, so does the amount of data captured from these connected devices. IoT sensors and devices collect and generate huge amounts of real-time data. These high-speed data streams are often difficult to process and analyze, especially manually. The sheer amount of data can be hard to manage, and often loses value when analytics are not in real time. By combining real-time analytics with IoT data, you can discover new actionable insights and benefit your company without bringing in any new data.
As more companies implement IoT devices and sensors, the amount of data received continues to expand at exponential rates. These massive, unstructured data sets can be an obstacle to analytics, especially once they are combined with data from other sources, such as social media, streaming media, billing systems or CRMs. However, the same characteristics that make IoT data unsuited for traditional analytics make this data invaluable to artificial intelligence and machine learning. IoT data fulfills the 4 V’s of data perfectly – high-speed, massive, heterogeneous data that is coming straight from the source. This makes IoT data sets perfect for training AI and ML applications quickly and efficiently. These applications can predict customer churn, gauge employee satisfaction, automate processes, and more in real time.
However, these applications of data are only possible with powerful, real-time processing. To extract the most value from IoT data, it must be collected, aggregated, and visualized in real-time. This will enable insights that would otherwise go unseen, and enable decision makers to make more informed, data-driven choices quickly. As illustrated by the massive amounts of IoT data, the world today is constantly changing, and businesses need to be capable of measuring and managing this never-ending stream of data. This is where real-time analytics come in. Generally, real-time means that the analytics are completed within seconds of the arrival of new data, not minutes or hours. Continuous real-time analytics can be especially useful when it comes to IoT data since the stream of data is also constant. The ability to immediately process and query this data allows current data to inform decisions and guide policy making within the business.
Purely historical data is useful, but today’s world requires up-to-date, real-time analytics to stay current. Only using historical data can have an impact on the ability to compete, understand trends and address market changes quickly. Current, timely data results in more accurate insights, but only if the analytics are processed in real time as well. Even if data is collected in real-time, it no longer adds value if it is only processed and analyzed minutes or hours later. Real-time analytics provide the latest and greatest insights and inform you on the most current trends. Even data from yesterday can be inaccurate when attempting to diagnose a new problem. In addition, real-time analytics can help you discover trends and issues as soon as they occur, enabling quick problem solving. This can be useful in so many areas; from licensing audits to customer retention, timely discovery and notifications can provide great benefits.
As the world changes and we continue to collect data, it is important to stay on top of your business. IoT devices can give you real-time visibility into operations, and when you layer real-time analytics on top, you can revolutionize business decisions immediately. This combination can enable new insights and benefits for your company, just by using data that you already collect.
