April 15, 2012 (Vol. 32, No. 8)
Chris Molloy, Ph.D.
“In the long history of humankind … those who learned to collaborate and improvise most effectively have prevailed.”
In any ecosystem there exists multiple interdependent relationships, niches, and environmental pressures. In all ecosystems there are food chains that convert energy from simple to increasingly complex states.
This metaphor is absolutely applicable to the data-driven environment of life sciences and healthcare R&D, where the efficient generation of complex information assets is key to success. Expressing the creation of complex knowledge assets from simple data through a “data-chain” enables a new thinking and one that is shared with other technology-dependent sectors. This approach enables collaborative thinking.
Today’s world is a 24/7 highly connected data-driven society where more people have smartphones than fresh running water. Yet while we are empowering the patient and the individual to be at the center of their data ecosystem, we are asking the brightest minds in the world to work in document-driven, linear processes familiar to researchers of a precomputer age.
In all sectors of R&D, researchers are finding it harder to collaborate with colleagues in the next building than to program their home TV from the workplace. R&D has become an information science and understanding the ecosystem is the first step in managing it.
Making processes and data interoperable across the ecosystem relies on making information available in real-time, and in a structured and secured manner. This can then be readily consumed by those from multiple disciplines who have the need to use it and the need to build high-quality capital knowledge assets.
The Real Ecosystem
Traditionally the R&D process has been a linear progression through a multidisciplinary set of teams chained together to provide basic research, new product discovery, regulated trials, and manufacturing. This concept, popular for over 30 years, does not reflect the way that these teams really collaborate and, in fact, serves to entrench a siloed mentality that is often reinforced by separate historical management and informatics structures.
In reality the R&D process is a complex interdependent community of projects, supported by various teams each providing skills and guidance to move products from inception to delivery: an ecosystem of ideas, data, and information.
Everyone in the ecosystem is both a generator and consumer of data.
The scientific method starts with a hypothesis “an idea based upon facts already known”—and ends with the communication of a conclusion to one or more partners to take the next step. The ganging together of these cycles of experimentation serves to build projects, departments, and entire R&D organizations (Figure 1). This is a food chain of information, and ideas become more valuable and complex as they progress through the ecosystem.
Facts, like food, need to be in a consumable form to be utilized effectively; they need to be complete and comprehensible; they need to be delivered at the right time and actionable. So often in R&D this fails to happen, leading to inefficient “digestion” of the data, IP loss, inadequate decision making, and a poor corpus of organizational knowledge.
Individuals should be at the center of their data ecosystem, not dependent upon others to define what they receive.
A recent survey of 682 researchers by IDBS and Scientific Computing reviewed the ability of researchers to work within that data ecosystem. The research shows that today’s researchers wish to, but fail to collaborate effectively. In many cases this is simply because they cannot efficiently move data from one person to another.
Figure 1. Scientific workflows combining to make Scientific Enterprises