Just Doing the Science is not Enough Anymore

John Trigg

Prior to about 1900 most scientific innovation and development was either embedded in an industrial process, or was an outcome of academic or privately initiated research.  The progressive introduction of industrial R&D laboratories heralded a new era of innovation and development with an extensive dependence on the skills, knowledge and creativity of individual scientists.  The evolution has continued into the ‘information age’ with a growing dependence on information technology, as both an integral part of the scientific process, and as a means of managing scientific information and knowledge.

Whereas laboratories used to be very science-centric and the only qualification for working in a laboratory was a strong scientific background, modern laboratories present an increasing challenge in terms of process understanding (quality, regulatory, health & safety, legal) and an aptitude and competence with technology.  In a lot of respects the laboratory is becoming a knowledge ecosystem based on a mix of people skills, technology and processes. If this is the case, then how well are we doing?  How good are our processes; how efficient are the technologies we are using, and how well are we able to meet the increasingly complex scientific challenges that we face?

The dependence of science on technology grows relentlessly.  From the basic application of computational power to undertake scientific calculations at unprecedented speeds, up to the current situation of extensive and sophisticated laboratory automation, black box measurement devices and multiuser information management systems, technology is causing glassware and paper notebooks to become increasingly rare in the laboratory landscape. 

Discovery and development are increasingly recognised as two steps in a holistic product life-cycle process rather than stand-alone functions.  Innovation itself has moved on from ‘Eureka moments’ and chance discoveries to become a managed industrial process with an in-built need to address quality, regulatory, health & safety and IP requirements. Just doing the science isn’t enough anymore.

With this increasing demand for competency in science, technology and process understanding, an area of concern is how well our higher education establishments are able to meet industry’s needs for potential employees with an appropriate set of skills.  The view is often taken that the demographic problems associated with ‘technology literacy’ will be resolved by attrition; new laboratory workers joining the organisation will have grown up in a digital world (digital natives).  To some extent this is true, but technology continues to evolve at an ever-increasing rate, and today’s digital natives may find themselves challenged to keep up with further advances in technology, in the same way today’s digital immigrants are.  It’s not just a basic competency in technology skills that is needed but also a deeper understanding of the continually evolving strategic and tactical roles that technology plays in the laboratory, both in terms of the science and the laboratory processes.

This extensive adoption of technology presents a difficult ROI calculation due to the growing need to seamlessly transfer and share data between systems.  Despite all of the outstanding advances, it would be quite revealing to add up how much money has been spent in the industry, to create and maintain custom solutions and middleware to solve integration problems, how much time has been wasted in not having direct and immediate access to data locked in inaccessible systems, and how many risks have been taken in using all kinds of crude and insecure methods of transferring data.  To some extent this is a legacy issue, as most of our current systems were not necessarily designed to work together.  In addition, systems often do a poor job of separating the content from the functionality, thus making the integration challenge more difficult.  Collectively we have never challenged the vendors to address the issue by demanding universal support for an open platform that provides data interchange standards and systems’ integration standards. We have more than enough technology available to achieve this; what we don’t seem to have is the communal will power and muscle to make it happen.  Or is it perhaps the lack of sufficient understanding of the capabilities of technology that limits our ability to articulate the problems?

Overall, there’s no doubt that there has been considerable progress in the laboratory’s evolution as a knowledge ecosystem, but there is still a long way to go.  If we accept that there are still problems to solve, the big question is how do we go about it?  Mass collaboration, or collaborative innovation, is providing a revolutionary, technology-based and disruptive approach to tackling a wide range of business, social and environmental problems. A joined-up world opens up the potential for an unprecedented level of grass roots collaboration to address an increasing number of problems, many of which have been created by industrial age practices.  We have an unprecedented opportunity in the laboratory community to apply some 21st century thinking and 21st century action to tackle some of these issues.  The challenge is firstly, to find a non-competitive process in which we can collaborate with solution providers to achieve common goals, free of politics and commercial interests, and which places the advancement of science at the forefront, and secondly, to collaborate with higher education establishments to encourage programmes designed to address the technology and process understanding needs of modern industrial science.