SAN JOSE, Calif.
Oct. 10, 2013
/PRNewswire/ -- Scientists from IBM (NYSE:
) today announced the
Accelerated Discovery Lab
, a new collaborative environment specifically targeted at helping clients find unknown relationships from disparate data sets.
The workspace includes access to diverse data sources, unique research capabilities for analytics such as domain models, text analytics and natural language processing capabilities derived from Watson, a powerful hardware and software infrastructure, and broad domain expertise including biology, medicine, finance, weather modeling, mathematics, computer science and information technology. This combination reduces time to insight resulting in business impact -- cost savings, revenue generation and scientific impact -- ahead of the traditional pace of discovery.
The notion of Moore's Law for Big Data has less to do with how fast data is growing, and more with how many connections one can make with that data, and how fast those connections are growing. While companies could utilize data scientists to analyze their own information, they may miss insights that can only be found by bringing their understanding together with other experts, data sources, and tools to create different context and discover new value in their Big Data.
"If we think about Big Data today, we mostly use it to find answers and correlations to ideas that are already known. Increasingly what we need to do is figure out ways to find things that aren't known within that data," said
, Director, Strategy and Program Development, IBM Research Accelerated Discovery Lab. "Whether it's through exploring thousands of public government databases, searching every patent filing in the world, including text and chemical symbols, to develop new drugs or mixing social media and psychology data to determine intrinsic traits, there's a big innovation opportunity if companies are able to accelerate discovery by merging their own assets with contextual data."
With much of today's discovery relying on rooting through massive amounts of data, gathered from a broad variety of channels, it is painful for many businesses and scientists to manage the diversity and the sheer physical volumes of data for multiple projects and to locate and share necessary resources and skills outside their organizations.