Of all the activities for which information management is a critical component, modern research and development is perhaps the most challenging. Extracting the intelligence required to make those “Eureka!” discoveries that lead to a breakthrough drug, new anti-aging skin cream or improved house paint requires powerful analytic capabilities, an integrated view of many sources of data and open communication between project stakeholders. Back before scientific enterprises stretched across geographies and time zones, this used to happen at the company lunch table. It was there that project team leaders – such as the head chemist, materials scientist, biologist and other contributors – would gather to share their unique knowledge and expertise, compare findings and put their collective brain power to work.

But in today’s highly distributed and complex R&D environments, achieving this rich level of intelligence is not so simple. Raw information dumped into databases and data warehouses has replaced the knowledge-driven categorization and analysis that happened at the lunch table. Disjointed processes and disparate information silos impede collaborative project ownership and decision-making. Valuable insights are hidden in a deluge of data, inaccessible to the researchers who need them and disconnected from other relevant sources of information. Simply put, content has exploded at the expense of context. How can scientific enterprises strike the right balance?

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