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LABORATORY INFORMATICS


data analytics and regulated environments is another topic – and something that people tend to shy away from because there are few tools and processes.’ Putting in place an infrastructure that


will give an organisation control and access to a complete breadth and depth of data will almost inevitably mean working with legacy systems and legacy data, Schaefer acknowledges.


Proof in the field Even today, companies’ ability to find and have confidence in a platform that will facilitate data control without hindering permission-based access at a granular level is held back, so that it can take five to ten years for software – and particularly for those platforms that have to accomplish such a lot with vast amounts of data – to prove themselves in the field, suggests Jeff Carter, co-founder and COO at Arxspan, which was acquired by Bruker in March. ‘The accumulation of all that data makes performance and responsiveness a real challenge, and companies want proof that a platform can cope with today’s data, and also equally manage accumulated data over subsequent years, whether from existing or new sources.’ Yet modern-day technology runs in


”Users can easily find and extract the information they need, and they can search through all of the data and metadata using key fields that will help guide them through associated information”


readable. ‘Adopt AnIML and even tools that haven’t been built specifically to support the format will be able to work with AnIML, as long as they support XML. The XML ecosystem now includes possibly thousands of relevant tools, and this will then drive down the cost of both data access and control, because it means you don’t have to custom-build everything for reading your data from scratch. For the user, this translates to not having to turn every search into a major IT project.’ AnIML ticks all the boxes in terms of data accessibility, and also fits in with the


www.scientific-computing.com | @scwmagazine


principals of FAIR data, Schaefer said. ‘If you can fulfil these basic principles and address accessibility and reusability, then you are working towards a more global philosophy of data control.’ That ability to access, search and


understand data in context can be particularly important when something goes wrong and you want to find out why. For example, if there is a purity problem on a production line, Schaefer suggests. Having immediate access to all the data linked with the affected batches can speed identification of where the problem may have arisen, and how to deal with it. ‘You can then compare every factor, including operating parameters, sources of materials, instruments used and operators working those machines, between batches, to identify where the problem may have arisen.’ This isn’t a validated system, Schaefer stresses. ‘But what it does let you do is feed GMP data in and use it to compare with other data. It helps you see the bigger picture. What you can’t do is make GMP decisions without a validated process. The area of


relatively short development cycles, whereas the pharma industry, for example, runs in really long cycles, Carter suggested, so that in five or so years technology can almost become obsolete. We just have to look at mobile phone technology to appreciate that speed of development. ‘While pharma may not be ready to adopt platforms that are being released today for another five or more years down the line – when they’ve been proven – in that timeframe technology may already have moved through two development cycles and brought out new generations of software.’ But it is doable, Carter notes. ‘You just


have to look at Google and Facebook.’ The differentiating factor is that Google and Facebook can build their own hardware from the ground up and run their own, massive data centres. That’s not part of what pharma wants to have to manage routinely. Rather, pharma companies are increasingly looking to get out of having to run data centres, and use one of the big data cloud hosts.’


An issue of vision and foresight Carter maintains that implementing a sustainable infrastructure for managing and controlling data may thus not be so much an informatics technology issue, as it is an issue of vision and foresight. Companies, labs and individual scientists


October/November 2019 Scientific Computing World 17


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