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?

Take an Enterprise-Level Approach

First, organizations need to move beyond format and discipline-centric data management solutions that trap information in silos, create barriers to collaboration and add unnecessary effort, rework and expense to the R&D process. To optimize research efficiency and ultimately drive faster discoveries, an enterprise approach is best. This requires an underlying IT architecture that is service-oriented and global in scope.

Integrating complex scientific information has become more and more challenging as R&D operations have expanded to include multiple departments, partners and locations. Research generated by a single chemist or materials scientist, much less an interdisciplinary group, 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 drug compounds, cosmetics formulations, industrial polymers and more.

Extracting maximum value from massive quantities of scientific data requires both the ability to integrate disparate information sources, as well as a way to quickly find the content most relevant to the research problem at hand. The ability to analyze data as a cohesive whole, especially across different areas of specialization, allows researchers to make important connections that otherwise would have been missed. They also have to be able to access specific information – a skin cell image or an existing formulation recipe – without a lot of hassle.

Fortunately, new infrastructure paradigms based on service-oriented architecture are changing this. A Web services-based IT foundation for scientific business intelligence can support the integration of multiple sources of information in a plug-and-play environment, so that organizations can build bridges across the extended research enterprise. Through this shared knowledge base, researchers can unlock, analyze and report on all the information sources available to them (both within and outside the organization) without the time and expense involved in enlisting IT resources to build customized point solutions.

Build a Lunch Table in the Cloud

Cloud computing, whether inside or outside the firewall, offers great possibilities when it comes to enabling richer communication, since this provides an ideal forum for project stakeholders to interact and share ideas regardless of where they are located, how the information is formatted, or what their area of specialization is. When a browser is all that’s needed to get a seat at the table, collaboration can once again play a key role in the innovation process, but there are technical considerations that first need to be taken into account.

Because the data involved in modern scientific research is so vast and complex, it doesn’t make sense, nor is it really possible, to take legacy infrastructure (like a large chemistry or biology data warehouse) that’s cemented to the floor and move it to the cloud. There are just too many transactional systems wrapped around these data hubs. At the same time, installing thick-client technologies at every site to transact on one or many data warehouses would introduce too much latency. Instead, organizations should focus on enabling the integration, shared access and reporting of project-centric data via a cloud-based project data mart.

For example, suppose a pharmaceutical company is working with a contract research organization on a drug discovery project. Today, many scientific organizations actually install their legacy IT systems at the outsourcer’s site in order to exchange and analyze data. Not only is this costly, it’s also incredibly inefficient, as systems now need to be maintained both within the organization’s internal IT infrastructure as well as at the CRO site. And the redundancies multiply as more departments, locations and partners are involved. With a cloud-based project data mart and reporting sitting on top of a services-based architecture, critical information, workflows and transactions that need to be accessed by collaborators can be maintained globally with a much lower seat cost and support burden.

Extract Context from Content

The term “business intelligence” has risen in the realm of information management for a reason. A collection of letters, numbers, figures or images are meaningless until processed in a way that makes the information understandable and usable. That’s what distinguishes raw data from true intelligence. The human brain is one of the most powerful processors available, which is why the insights researchers are able to gain when conversing informally is so rich. For example, humans are adept at making contextually relevant associations that a structured database is incapable of – like understanding 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 when the available knowledge base includes an enormous breadth of sources, data formats and locations, relying on human processing alone is simply not feasible. This is where emerging technologies such as advanced semantic search and text analytics come in. These types of artificially intelligent categorization tools can help remove the time and cost constraints involved in extracting the context from complex content so that research collaborators can capitalize on all the valuable stores of data available to them – structured and unstructured, proprietary and public. For these tools to work in a research environment, 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 recognize the variations, just as a scientist sitting down to lunch would.  
 
R&D organizations developing everything from personal care products to fine chemicals, pharmaceuticals or industrial materials need to bring back rich intelligence capabilities that existed at the company lunch table, but in a form more suited to today’s complex information landscape. With a services-enabled, enterprise approach to information sharing, collaborative tools that leverage the power of the cloud and contextually relevant search, today’s researchers can more effectively transform data into intelligence, make new discoveries and forge a faster path to innovation.

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