A research platform is being created to help UK organisations tap into big data analytics.
The publicly-funded UK Science & Technology Facilities Council (STFC) is offering to help firms and public bodies create big data analytics systems.
Firms that successfully complete a 'proof of concept' assessment will be able to draw upon the computational facilities and data scientists at the STFC's Hartree Centre to test big data analytics systems.
The centre is making available a collaborative data environment, a suite of big data analytics tools provided by IBM. The environment will provide a range of capabilities, including distributed storage, stream processing, data warehouse, and high performance computing services.
Lee Hannis, business development manager at the STFC, said the initiative is designed to stimulate the UK economy by helping organisations better exploit their data.
"We want to help other organisations become the next Google or Facebook. That is why we have invested in the collaborative data environment."
Hannis described the collaborative data environment as "a platform to allow business to very quickly bring their data and scientists alongside ours and create a blueprint of what their operational system might look like".
The STFC is one of the UK's seven publicly funded research councils responsible for supporting, coordinating, and promoting research, innovation, and skills development. STFC's Hartree Centre is based at its Daresbury Laboratory in North West England, located at the Sci-Tech Daresbury national science and innovation campus.
The STFC has already worked with organisations in several industries to help them implement big data systems.
Democrata, a UK construction company, is using analytics at Hartree Centre to automate environmental impact assessments and better predict risks when building rail and road links. Currently risk assessment surveys can be slow and cumbersome due to the need to consult numerous data sources.
Global healthcare company GSK is working with the Hartree Centre to develop clustering techniques similar to those used on social networking sites to identify correlations between genes, biological processes, and known diseases.