Intelligent Data Requires Context
By 2020, more than eight billion people and businesses, and at least 30 billion devices, are connected.Connectivity is not meaningful communication. Data is not information. The more complex the data problem, the more important the semantic context becomes so you can share your data with confidence that it will be understood.
Data needs context to become information. The recipients of information are normally humans and we humans are good at context. Unfortunately, machines are not. For a machine to “understand” data, the context has to be provided in an unambiguous way. This is where semantics comes in. Overlaying your data with descriptive metadata gives it meaning that other machines can understand. The semantic web technologies, such as Resource Description Framework, allow intelligence to be embedded within data making self-consistent blocks of data that need no interpretation.
Using semantic data means that sharing data with others is easier and less prone to misunderstandings and apps can be intelligent about how they consume data, reducing application development time. Semantics embeds the intelligence in with the data and avoids mistakes as simple as this where a spacecraft crashed because one team used metric units and another used imperial, but they shared only the raw numbers. Data in context allows more seamless communication up and down supply chains where it can be clear that this data means exactly this about this particular thing, leaving less to interpretation and therefore mistakes.
Semantic Discovery and Sharing
Organisations’ supply-chain and internal data communication requirements only seem to become more complicated with time. Being able to share data knowing that you can trust the recipient and trust that they will not misinterpret your data is now vital.
- The accurate, unambiguous description of your data is the first step in sharing – it allows your data sources to be found.
- The second step in sharing is about who or what is allowed to receive your data.
- The third step is the semantic overlay architecture to allow the consumer to know what they have received.
Any interaction with the source must have some arbitration. This negotiated access requires a mechanism of brokered interactions. Once the data is found and the follow request granted, the data flows from the producer to the consumer.
The final step is that the semantic overlay on the data allows the consumer of the data to know what they have received. For example: this is a pressure in pascals from a hydraulic coupling – not an atmospheric pressure in mmHg. The semantic context allows data to be understood and actioned.
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