Research and Development

Creating a distinction between data as a liability and information as an asset can be problematic. The emphasis needs to shift from the problem (and cost) of storage, towards leveraging value from data.

Currently the incentives to explore the value of data are high as efficiencies are sought. Abstraction creates opportunities to discern new relationships within content and describe different relationships between objects.

Data needs to be available at an ‘abstract’ level and the application of Semantic Web techniques supports this ‘abstraction’ by creating and maintaining indices which can be managed and utilised across diverse data sources. Automation of these processes creates efficiencies through the search, discovery and playback of information via these stored indices.

This approach supports innovation through a rapid and agile approach to R&D as basically it lets you do things fast- why is this?

Flexibility lets you do things fast, and the Semantic Web is flexible -

  • inherent flexibility of the Semantic Web data model (RDF)
  • it can be evolved incrementally with changes to one part of a system not requiring re-designs across the rest of the system.
  • abstraction - through loosely coupled data systems – offers an improvement on tightly coupled systems by isolating the consumers and producers of data.

And because you can do things fast, you can do lots more things than you could before. You can afford to do things that fail and you can afford to do things that are unproven and speculative. Of course, you can also do things that you would have done with other technology stacks, only you can have them up and running in a fraction of the time that you otherwise would have spent.

This creates an ecosystem which assists R&D towards process and product innovation through the exploration of data.