LABORATORY INFORMATICS


Intelligent integration


SOPHIA KTORI CONTINUES THE DISCUSSION ON SOFTWARE INTEGRATION IN THE LABORATORY


One of the most obvious, and immediate challenges when bringing new software into a lab


environment is the likely ‘spaghetti soup’ of existing platforms – possibly from multiple vendors – that are already installed, suggests Richard Milne, vice president and general manager, Digital Science, at Thermo Fisher Scientific. ‘Each of these will offer different levels of integration’.


The situation is compounded because


even in the same organisation, there may be different suites of instruments and software in separate labs and across departments. While there is an ambition to integrate instrumentation and software tools across a business and geographic sites, the reality may be what Milne describes as an ‘unstructured legacy of decisions’. Each of which represented a theoretically attractive investment at the time, but which in practice offered a point solution that ultimately ‘confuses’ the whole environment. This means many organisations will have some level of legacy investment,


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in instrumentation, in SOPs, in working practices, and in pieces of software that people use every day. It’s likely that most integration projects will be very much in a ‘brownfield’ setting, rather than setting up ‘greenfield’ labs, Milne said. And that brownfield environment will almost undoubtedly be very fragmented in terms of its legacy systems. So, connectivity needs to happen at the enterprise level, not just at the lab level, and encompass that existing ecosystem of digital technologies, Milne believes. ‘The overarching aim is to generate an


environment that allows you to unravel that spaghetti soup of existing platforms, and make sure that there’s coherent organisation, and the ability to use it all,’ Milne said. ‘Ultimately, this will allow organisations to purchase tools based on the capabilities and features of those tools, rather than on whether they will talk to the lab’s existing equipment.’ And this means that wherever R&D teams, service providers or partners are located and whatever technology they use, they should be able to collaborate and share data


Spring 2021 Scientific Computing World 17


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