At the heart of deriving value from an IoT implementation is not just data – it is data analytics. IoT “listening posts,” such as devices and sensors, are throwing off a lot of streaming data, but to make sense of it and take meaningful downstream actions requires driving the analytics process efficiently and at scale.
A framework to think about harnessing this data requires one to think about two dimensions: first, understanding the types of data that can be analyzed; and second, how the end-user will use this data. Data can be very different in nature. Normally one would think of the data that comes straight from the sensor that being used to perform various kinds of business enablement use cases. However, there is also metadata that can predict device behavior, anomalies and security issues. In addition, your network provider may provide to you usage data – bytes/dollars in a given period.
Looking at the second dimension of how an end-user would engage with the data, there are five essential steps an end-user should follow to build a successful data path:
If you bring both of these dimensions together, the two-dimensional framework allows one to think clearly about a variety of use cases, depending on the type of data and the end user in mind. If you are in the fleet management industry, where KORE has deep expertise, here are three “free” examples to illustrate the approach:
As IoT deployments scale up in organizations from a few hundred devices to hundreds of thousands of devices, putting a use case mapped against the type of data used and the five-step process outlined in the framework above, can help realize the different types of value IoT data can unlock.
Learn how a partnership with KORE will can simplify the challenge of data analytics to help meet your IoT goals.