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Building a Smart Laboratory 2017


Summary


Summary


Te term Knowledge Management (KM) embraces a wide range of processes, technologies, and behaviours that support a collaborative approach to managing ‘knowledge’ within an organisation. In many respects, it is synonymous with the concept of a smart laboratory. An industry has since grown up to provide training, soſtware, and consultancy services to achieve organisational goals. However, the underlying principles are simple and are focused on corporate culture and individual behaviour. Te five nodes in Figure 9 represent a


generalisation of the major knowledge processes, and it is quite evident that technology, in the form of the laboratory informatics tools, has an enabling role in a laboratory knowledge ecosystem. But it’s the human element that makes knowledge management work. Systems will facilitate the management of data and information, but knowledge is a human quality and the right kind of culture, skills and expertise are needed if best use is to be made of the technology. Te same is true for a smart laboratory. From laboratory informatics to knowledge


management, technology is predicated on logical and systematic processes. But serendipity has always had a significant role in science. Many scientific advances have originated from ‘what if’ moments, chance observations, and things that went wrong. Failure oſten has more to teach than success! With a growing emphasis on right-first- time, error-reduction, and productivity, it is a management challenge to take the time to review and assess successes and failures. Te role of the informatics tools within a


smart laboratory, or ‘knowledge ecosystem’ (Figure 9) is important. Tey are strong in terms of capturing, recording, and organising data, and increasingly, they provide facilities for sharing information. But further opportunities arise with


Un-order Mathematical complexity


Order Process


engineering Rules


Social complexity


Systems thinking


Heuristics Epistemology Fig. 10: The landscape of management www.scientific-computing.com/BASL2017 Fig. 9: Knowledge processes Sense/explore/discover


Mostly done as an individual activity –people looking for, or encountering new items of knowledge.


Evaluate/learn


Intergrate, organise and test with other or pre-existing knowledge (either tacit or explicit) to generate new facts, ideas, knowledge, concepts.


Share


Communicate with others (could be explicit or tacit). This forces structure (organisation) and further integration.


regard to evaluation and learning (data analytics) and experimental design. Further afield, they will contribute to predictive science. Nevertheless there needs to be some space for


‘right brain’ thinking, alongside those systematic and structured approaches for increasing efficiency and productivity. Innovation depends on knowledge and understanding. Although technology can assemble and look aſter the data, making sense of it is down to human assessment


“ From laboratory informatics to knowledge management, technology is predicated on logical and systematic processes”


and understanding. In the main, so-called knowledge management ‘solutions’ are no more than data or information management solutions. It’s only when the human component is added that knowledge management can flourish, and even then, it needs the right environment – hence the concept of a ‘laboratory ecosystem’, or smart laboratory. Although management may want to see such an ecosystem, it can buy only the tools, it needs to create the right environment if the knowledge ecosystem is to be nurtured and cultivated. Te ecosystem is dependent on an open and collaborative culture and supportive leadership; not secrecy, discipline or rigid management. Participants need to opt in; not be forced in. One worry is that the digital revolution may be driving a lot of thinking to be ‘digital’, with


Capture/record


Save the ‘knowledge’ somewhere; e.g. lab notebook, technical report, etc.


Organise


Codify, index, make readily accessible. Could be accomplished or assisted by others, or by automation.


the risk that random, analogue mindsets and gut feelings may be seen as irrelevant and inconsistent with modern concepts of science. David Snowden, founder of the Cynefin


Centre, wrote an article entitled ‘Multi-ontology


sense making – a new simplicity in decision making’. [18]


In it, he raises interesting questions about


business processes and the extent to which they are fit for purpose in different domains. Although the model is generic, it can readily be applied to the laboratory. For example, the lower leſt-hand box (Process


engineering) relates to laboratory functions that fit an ordered and rule-based environment – typically a routine, QA, highly automated laboratory, dominated by a systematic workflow. On the other hand, the top right-hand box (Social complexity) relates to a classical research and innovation function based on complexity, chaos, creativity, and innovation. Te other two boxes represent hybrid environments: one in which emerging rules can be applied to complex or chaotic environments; and one in which an ordered, well-planned approach may require innovative thinking to develop understanding. A concern about process engineering is the


extent to which it can de-humanise laboratory activities and reduce the demand for intellectual input, or indeed, any fundamental knowledge about the science and technology that is in use. Nevertheless, there’s little argument against the productivity and efficiency benefits of automation and process improvements (Lean, Six- Sigma) in routine and QA laboratories. It remains to be seen whether there are other long-term


41


Ontology


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