Research in commercial labs and companies is increasingly goal driven – but is there still room for curiosity? And how can we harness a question-based approach as we become ever more inter-disciplinary? The answer lay in good integration of data says Barbara Holtz
The complexity of research and development projects is escalating. The stakes are high for R&D departments as an increasingly conscientious international community seeks sustainable solutions to large-scale challenges. The intricate nature of these projects requires a convergence of disciplines. Experts in areas such as physics, chemistry, biology and materials science now collaborate earlier in the product development lifecycle and across development stages to answer market-led demands for commercially viable solutions that also balance social, economic and environmental needs.
In many industries, organisations are adopting a question-based approach for managing multifaceted R&D projects that may not have a well-defined conclusion or a clear understanding of required tasks. In this approach, research teams work through a series of questions to arrive at decisions about how best to continue. They can retrospectively understand the best way to complete a project, and then recreate the decision path and learn from it to develop best practices and drive continuous improvement.
Today’s global challenges and the projects they spawn are driving a shift in the way scientific work is performed in R&D departments and laboratories. Research is increasingly purpose-driven rather than curiosity-driven as teams work toward a specified goal. A question-based approach keeps multi-disciplinary teams focused on the purpose while preserving freedom to stay creative and open to unexpected discoveries.
With a question-based approach, researchers can build a thread of relevant answers that support completion.
Sustainability drives inter-disciplinary work
The rising social mandate for companies to act responsibly brings sustainability to the forefront across many industries. In the agricultural sector, researchers seek marketable solutions that meet society’s needs without compromising the ability of future generations to flourish. Genetic engineers collaborate with botanists to improve the nutritional profile of food crops while making the plants more resistant to pests, diseases and spoilage. They also modify non-food crops to increase efficiency in the production of biofuels and pharmaceuticals.
Developers of consumer packaged goods face pressure to reduce or eliminate tests on animals and to adopt scientifically validated alternatives. Rather than spreading sunblock lotion on a rat and observing what may befall the creature, they must understand toxicology from a biological perspective. Formulators and chemists collaborate with molecular biologists to understand what effect a chemical will have on a cellular and molecular level in biological systems such as skin.
In high technology, sensors that traditionally used electrical, optical or mechanical apparatus now use biological molecules such as proteins that change in reaction to environmental characteristics such as the presence of chemicals. Because these molecules behave predictably in response to certain stimuli, they can be used to accurately detect input from the physical environment at more subtle levels with a more sustainable use of resources. Biological processes are also used in chemical production. Enzymes and bacteria can be more energy-efficient sources for chemical processes than traditional facilitators such as heat, electricity or pressure.
Specialists from a variety of fields converge to steer strategies like these from concept to production. A question-based approach to research and development helps multidisciplinary teams determine project methodology, guiding all stages of inquiry and analysis. Using questions as the foundation for R&D projects helps research teams maintain focus while allowing them to keep their horizons broad.
A wicked problem
That’s important because often with complex development assignments nobody knows at the outset what the final answer will be or how to arrive at it. These are sometimes called wicked problems because they are difficult to solve due to contradictory or changing requirements and incomplete definitions of essential tasks. Because there may not be a single perfect answer, multidisciplinary experts must weigh the pros and cons of a variety of possible solutions and then make a judgment about which is best.
With a question-based approach, researchers can build a thread of relevant answers that support completion. Over time they may observe patterns in the answers. The required tasks and methodology take shape as the project progresses. They may realize that a crucial question that was asked first should in fact be asked last.
To derive full value from the question-based approach, organizations should leave room for creativity and flexibility in R&D strategies. Researchers and developers are typically not allowed much freedom in their approach, timelines or deliverables as companies push for a quick and predictable return on their investments. Rather than adopting the role of discoverers, these professionals must often simply recycle successful procedures from previous work. This methodology doesn’t support continuous improvement or breakthrough innovations. A question-based approach permits responsiveness to new ideas, guiding development processes that become better defined over the course of the project.
Scientifically aware information systems that understand data and processes in context can provide structure for the way multi-disciplinary research groups work through question-based projects. Such knowledge systems help R&D teams track their actions so they can arrive at an understanding of which methods and activities effectively move the project toward completion. Convergent teams coalesce as they discover relevant patterns and best practices that drive continuous improvement.
Just good science
As experts from various scientific and technical disciplines converge to undertake multifaceted projects, shared protocols facilitate communication and a laser focus on the activity at hand. A question-based approach can provide a firm foundation for this, but only if specialists speak the same language. These professionals come together with diverse intentions, experiences and ways of approaching problems. Each of these disciplines typically communicates with an exclusive vocabulary, has unique processes and uses different data types.
This is an impediment for many established companies as they strive to modernise entrenched practices within traditionally isolated discrete areas of expertise. To support discipline convergence, companies should enforce enterprise-wide standardisation in data models to merge data and processes. And they should adopt ontologies that define a common language across all activities. Experts from diverse disciplines need to capture information in a digital system that presents findings in a standardised format that can be easily interpreted.
Goals in R&D should include eliminating incomplete and incompatible information and reducing inconsistencies that require teams to revisit earlier stages of development. Organisations can address these objectives by enforcing standardisation for data models and language and by establishing best practices for business processes and the ways people work. Information rarely arises spontaneously. It is usually generated through processes. When organisations standardise the processes that create information, they can leverage and reuse the information more effectively.
Reusable information and consistent methods support creative interdisciplinary collaboration that fuels innovation. These techniques require holistic knowledge systems that can store and interpret information about various activities, providing a foundation for structuring question-based R&D projects, identifying best practices and establishing standards.
In support of accelerating innovation, many leading companies participate in pre-competitive alliances to establish standardised languages and ontologies that span industries. Proprietary standards, processes and formats hinder the accessibility and reusability of information. Availability of knowledge benefits everyone – a rising tide lifts all boats. Perceptive organisations cooperate in relationships that extend standards and best practices beyond corporate walls to generate value for the worldwide science community.
Groups such as Pistoia Alliance and Allotrope Foundation bring organisations together to develop publicly available frameworks to overcome common obstacles in R&D. Pistoia and Allotrope member organisations collaborate on open projects that standardise the vocabularies and knowledge collections of experimental data, allowing scientists to extract the greatest value from data.
A very modern model
As part of the convergence trend, computing and mathematics now support many disciplines and facilitate better outcomes through virtual modeling and predictive analytics. Researchers can model scenarios virtually before performing them physically, which helps convergent teams gain a better understanding of what they are trying to achieve.
Virtual modeling is a component of the Fourth Industrial Revolution. The First Industrial Revolution introduced mechanised production powered by water and steam. The second harnessed electricity to create mass production. The third automated production through information technology. In the Fourth Industrial Revolution, the real and the virtual realms merge in computer simulations.
In this new era, agile product development brings specialists together to discuss the development of a potential product in a certain market for a specific demographic. They can suggest possible materials, costs, sourcing and production processes to help them envision the final product. Various scenarios can then be assessed using virtual models to predict viability. Scientists can model different product designs as well as supply chain, manufacturing and testing processes. Research teams get answers more quickly while saving materials. They also gain better understanding of their methods and the ability to define and refine development activities through the question-based approach.
R&D teams must remain open to multiple sources of knowledge, including virtual. However, reliable data is essential for successful outcomes with virtual modeling and predictive analytics. Reliable data depends on a robust, shared system for managing data in context, common ontologies and standardised processes. With these requirements in place, modeling/simulation and predictive science can illuminate an optimal path for development.
As experts from multiple scientific and technical disciplines converge to develop sustainable solutions to large-scale problems, maintaining focus can be a challenge –especially when methodologies and outcomes are not well defined. A question-based approach to project management provides a disciplined framework to help convergent research teams achieve excellence while leaving room for creativity. Without some elasticity in the R&D process, it is difficult for spontaneous discoveries to arise that lead to innovation.
For the question-based approach to succeed, organisations and industries must standardise vocabularies, processes and data types. With ontologies in place that define a common language across all processes, research teams can transform holistic knowledge into best practices and common standards that drive continuous improvement.
Barbara Holtz is Senior Business Consultant at BIOVIA Dassault Systèmes