From an analytics perspective, “Data in Motion” applications refer to the ability to process and analyse data from connected devices at the edge. The data can be collected from devices that are themselves in motion, such as cars, trucks, planes and autonomous vehicles. It can also be collected from the Internet of Anything (IoAT), where devices stream data from the edge in real time.
It’s important to note that data in motion applications have the ability to stream, process and analyse the data in real time as opposed to putting it into a more static data store for subsequent analysis.
Data in motion applications need to manage both the flow of data and the processing of parallel data streams. In order to achieve that, data in motion applications need to be able to:
- Gather, move and filter data in real time from the data centre, cloud and edge devices.
- Intelligently route data to processing systems.
- Join and split data streams in transit.
- Detect complex patterns within data streams in real time.
- Create customised dashboards to visualise data streams.
Examples of data in motion use cases could include:
- Companies that run vehicle fleets adding sensors to all of their vehicles and streaming multiple data points in real time to make “by the second” insights and decisions.
- Waste management companies putting sensors in bins and refuse containers streaming in real time to plot customised daily routes for refuse trucks.
- Using sensors and monitors in modern farming to make decisions on the fly to maximise yields and reduce consumption of natural resources.
