The Company Lunch Table 2.0Tweet
Frank Brown, chief science officer at Accelrys, believes the best research emerges from collaboration
Back before research enterprises stretched across geographies, contract organisations and specialised departments, R&D innovation was centred on personal communication at the company lunch table. It was here that project team leaders would gather to share their knowledge. Rather than focusing on a single aspect of a project, these free-form interactions crossed disciplinary boundaries, and the intersection of ideas resulted in faster discoveries in areas ranging from consumer packaged goods to speciality chemicals and pharmaceuticals.
Thanks to advances in technology, our ability to generate data has increased exponentially. Yet now there is too much content and not enough context. Raw information dumped into databases has replaced the intuitive categorisation and intelligence capabilities that dominated the lunch table. Disjointed processes and disparate data silos have hindered cross-disciplinary information sharing. Valuable insights are lost in the deluge of data, inaccessible to the researchers who need them, and disconnected from other relevant sources of knowledge.
Here are three steps to building the ‘2.0’ version of the company lunch table – one that supports rich collaboration, while also embracing the breadth and complexity of today’s global information landscape:
Move the lunch table to the ‘cloud’
Collaboration at a real-world lunch table is not entirely practical when an organisation’s top formulator is in Boston and the lead chemist works for a contract research organisation in Beijing. It’s impossible when sharing information is too cumbersome due to systems and processes that lock knowledge in discipline-centric data silos.
The global scope of scientific research is here to stay, so organisations need to look at technologies that enable collaboration ‘in the cloud’ – i.e., in a web-based environment that allows stakeholders to interact and share information regardless of where they are located, how data is structured, or what their area of specialisation is.
Simplify data integration
The knowledge that drives R&D can come from many sources. In addition to data generated from current experiments, researchers can greatly speed progress by incorporating information from previous projects, the scientific literature, and from in-house and publicly available databases.
But integrating complex scientific information has traditionally been no easy task. Research generated by a single chemist or materials scientist is often spread across a diverse array of formats, instruments and proprietary systems, and includes everything from text documents to images generated by a laboratory microscope. And the volume is enormous – spanning thousands or even millions of possible compounds, formulations, polymers, and more. Stakeholders can easily spend countless hours finding needed information, preparing data for analysis, and formatting and distributing results.
Fortunately, next-generation service-oriented technologies are simplifying data integration by enabling a unified approach to managing complex scientific information.
Find the context in the content
The insights researchers are able to gain when conversing informally are extremely rich, because human brains are adept at making contextually relevant associations of which a structured database is incapable. For example, a human would know immediately that the words ‘auto’, ‘automobile’, and ‘car’ mean the same thing, or that a past experiment may be ‘kind of’ like one being conducted in a current project.
But what happens when the available knowledge base includes an enormous breadth of sources, data formats, and locations? It’s impossible to access the most relevant information quickly when it’s hidden among thousands of pages of published literature. This is where emerging technologies such as advanced semantic search and text analytics come in. These types of artificially intelligent categorisation tools can help remove the time and cost constraints involved in extracting the context so that research collaborators can capitalise on all the valuable stores of data available to them – structured and unstructured, proprietary and public. They have to be designed to handle the complexities of scientific data, however. For instance, a molecule may be represented by name, by an ID number, as an image, etc., so a search solution must be ‘scientifically-aware’ enough to recognise the variations.
Scientific organisations need to bring back rich collaboration that existed at the company lunch table, but in a form more suited to the modern research environment. With a cloud-based approach to information sharing, simpler data integration and contextually relevant search, today’s researchers can better navigate the data storm, make new discoveries and forge a faster path to innovation.