What problem does it solve?

Experimental environments for innovation with data need to be adaptive and need to be able to connect with existing data stores to address current and prospective information requirements.

There are many drivers which makes this approach attractive and some of these are highly compelling. Namely cost reduction and process improvement. Innovation can emerge through collaboration, investigation and exploration across diverse data sets.

semantic web techniques for R&D towards innovation with data

Semantic Web techniques lower the risks associated with researching and developing solutions and applications associated with data.  It is faster, and as it only creates a set of references to data, it does not interfere with existing data stores; and it can work with loosely associated sources of information.

This creates opportunities to explore improvements to existing search and discovery of information; and this process informs the options to enhance the value of information as a resource which can drive efficiency across an organisation.

Alternative approaches would be considerably less flexible with the timescale to establish success (or failure), very drawn out through both planning, stakeholder consultation and actual application development.

Semantic Web techniques are relatively lightweight, flexible, standards-based and start to build a set of reusable components. It is quicker to deploy so it can adapt quickly to changing needs and problems.