The 5 Types of Data That Power the Internet of Things
The speed at which the Internet of Things (IoT) has been developing is phenomenal. So fast, that it could follow other technological revolutions—what Gartner calls “trough of disillusionment,” which is representative of the boom and bust of technological development.
Despite this possibility, many companies are still allocating lots of money into IoT research and development, and the most prominent of these are derived from the industrial sector, which should be a good predictor for IoT technologies to feature in the supply chain, though exactly how is still not completely clear.
What is clear is that current practices are generating an overwhelming amount of data, which brings companies to ponder a new question of information logistics—how to decipher which IoT data should be sent to which location.
To answer this question, IT departments, the supply chain and the marketing department, at least, will need to collaborate. The role of marketing will be key as industrial companies aim to develop new products that can be sold to their customers.
The question of who will own an IoT system once it’s deployed remains open.
With all this in mind, it is worth considering whether the different levels or types of data produced by IoT should be placed within a framework in order to get the most value from it. Here are five proposals as a contribution to this debate, the five types of data that power the Internet of Things:
1. Data that is collected by IoT sensors within devices, including the serial number of the object, as well as other data attributes such as temperature, location, humidity, etc.
2. Data subsets that are collected in a data repository. Not all data will end up here, and the repository is usually hosted in the cloud. This brings up more questions about which data should be sent to where, etc. One of the key factors in this will be how much data the object in question will be able to store.
3. Data collection from the billions of distinct IoT date information feeds into the repository, regardless of where it is hosted.
4. Providing general reports generated from the consolidated database.
5. The use of the database of information to provide advanced analytics applications with the potential for more insight into the obscure processes taking place across a specific subset of devices within the field. The new practice of predictive analytics will come into play here.
Finally, although a plethora of data will be created, a key issue in IoT is that because it is an emerging technology, very few standards have been created for it thus far. As IoT continues to grow, we should see more standards developed for ease of use and data sharing.