The analysts at Juniper Research assume that the total number of sensors and devices connected to the IoT will rise to over 50 billion by 2022. According to Juniper, this figure is currently estimated at 21 billion. The number of companies that want to invest in IoT technologies in the future will continue to grow rapidly due to technological progress, which is constantly producing smaller, cheaper and more effective sensors. The challenge today is no longer the implementation of the technology, but the added value that companies can derive from the collected data. However, IT teams that need to deploy new IoT solutions and deliver this value face several obstacles in accomplishing this task. Gaining the desired value by making insights from data quickly and easily accessible to the business has always been difficult. Adding more data, data sources, data types and streaming data to the existing data mix can make this nearly impossible with existing methods of data processing, storage and analysis. To get the most out of their IoT investments, organizations need to align necessary tactics within their strategy.

Automation

Due to the many connected devices and the resulting volumes of data, automation is the only realistic solution to deal with the enormous amounts of IoT data. From raw IoT data, automation helps organizations receive, process and deliver valuable data and derived insights for use in real time. It can ensure that IT teams can manage astronomical volumes of data and are able to deliver insights in a way that an organization can use and derive value from. Automation reduces the need for human interaction by eliminating the manual programming and repetitive, time-consuming requirements of data infrastructure projects. This has several key benefits: Insights from data can be delivered in much less time at a lower cost with significantly improved quality and reliability of results. In addition, it allows the responsible employees to concentrate on the more strategic content of their work.

But it is not enough to simply automate the processing of data. The only way to process data efficiently is to stream it on site. And as soon as it is created, not at a later point in time. An example from a transportation company illustrates why it's important to use live data streaming to leverage real-time analytics:

Imagine a bus company that has hundreds of buses on the road every day. The company wants to understand how its bus fleet is running as close to real-time as possible to maximize the overall efficiency of the service. With IoT data collected from on-board sensors, the bus company can analyze this data in real time in the field to diagnose problems immediately. In the past, data was downloaded from the on-board sensors at the end of the working day. This was problematic as a bus could have been out of service for a whole day or behind schedule for other reasons. So there was no way to use data for a whole day to avoid problems later on. With streaming data, however, the problem could be detected by sensor units in real time and action could then be taken. For example, if a bus is at risk of failure. By processing the data in real time, the bus company could immediately detect if the brake pads were thinning and then notify the mechanical department to replace them before the bus could break down.

Data types generated by the IoT

Hundreds of sensors on buses, thousands of sensors on a modern airliner, video surveillance cameras, machines in a factory - there is a huge amalgamation of different data sources and formats, all coming from IoT devices. Some of this is traditional, structured data, but there is a rapidly growing amount of semi-structured and unstructured data that needs to be processed in real time at best. But before all this information can be turned into actionable insights, it needs to be collected and put into a manageable form. A task far too complex to be carried out by humans - automation is the only way to do this efficiently.