IoT is an evolving field, and with the ongoing technological advancements, one will find many organizations that build solutions adding value across various industries. One reason for this is because the usage of IoT in any industry enables the organization to optimize its resources and assets. This optimization is made possible by IoT as it plays a crucial role in enabling the DIKW pyramid realization for the organization.
IoT – any device connected to the internet and can identify itself providing data.
DIKW pyramid – this pyramid is a representation of how Data progresses to Wisdom.
Let us look at the definitions:
Data: Facts and statistics collected together for reference or analysis.
Information: Facts provided or learned about something.
Knowledge: Facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Wisdom: The quality of having experience, knowledge, and good judgement.
While Data and Information are relatively easy to understand, there is ambiguity in Knowledge and Wisdom. The best statement I have seen regarding this is, “Knowledge is knowing that a tomato is a fruit. Wisdom is knowing not to put it in a fruit salad.”
IoT enables assets to generate data in real-time, and when this is compared among similar assets, we can extract a layer of information. When organizations can use this information, they can understand their assets and operations better. Successful companies are then able to act with this insight, thus tapping the Wisdom layer. Therefore, to attain the Wisdom layer, you need to have a reliable Knowledge layer and the Information layer from the data gathered by IoT-enabled assets.
Let us consider the Transport and Logistics vertical to provide context to these layers and how they can be represented at each level. In the T&L industry, with an IoT enabled asset, you at the very minimal need to know:
- Where: where is the asset?
- How: how is the asset condition?
- When: when will the asset reach?
The IoT-enabled asset provides you location and any measurement data about the condition of the cargo. This is your first layer called the Data layer, e.g., temperature, humidity, ignition state, movement state, and more. When one processes the data, it gives you the Information layer. At this layer, you have more context, such as:
- If the assets are in route
- Loading status
- Entry and exit points of interest, like customs checkpoints, rest areas, and loading-unloading bays.
This collected information can be further analyzed to gain insights, and this layer would be the Knowledge layer. Typically, there will be Artificial Intelligence and Big Data repositories to do various analysis, like:
- Time for clearing at customs location (delays are highlighted)
- Best suitable time for the journey (considering road restrictions for trucks, traffic, etc.)
- Best performing vehicle in terms of mileage (fuel usage and driver behavior)
Now with this information, you can plan your operations with minimal downtime and expense. To a degree, Robotic Process Automation can be incorporated based on the industry to minimize human interaction and enhance operational performance. This is your Wisdom layer:
- Letting go of the old fleet that is not giving required mileage.
- Deciding the time of departure.
- Planning routes and assets for operation.
- Automating the ETA updates.
- Enabling quality of service.
Each layer of the DIKW pyramid uses different technologies, and there can be continuous modular enrichment in terms of functional and operational performance. VectorGlobe has worked on this DIKW pyramid and is realizing this with its FleetElements and MachineConnect solutions.
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