LABORATORY INFORMATICS


investment in educating personnel.’ Breaking the process down into manageable chunks and making small, quick wins during early implementation of new software and processes can help to get people on board, and make the value of change immediately evident. ‘Whether that means deploying UDM or using the FAIR toolkit, these stepwise changes will show short-term benefits and encourage wider adoption,’ Lynch notes.


”Whether that data is generated at the R&D stage, or at latter-stage clinical trials, having complete oversight and control of every aspect of that data is imperative”


because the process of data acquisition, storage, utility and reporting is more seamless. ‘We are working with some of the


world’s biggest biopharma companies, including Roche and GSK, to develop a unified data format that will hopefully go some way to helping people exchange data and experimental information. This will also work alongside initiatives, such as Allotrope Foundation, to help make the process of standardisation seamless across disciplines.’ How do companies start to put in


place an infrastructure that will support www.scientific-computing.com | @scwmagazine


data control and the FAIR principles? ‘It's probably worth starting with a kind of cartoon view of the data lifecycle, including who you may need to share that data with,’ Lynch said. ‘Just that outline, or sketch gives you a more complete view of who your key stakeholders are, in what formats you will need to deliver that data to them, and what data may also need to be brought in from third party partners, contract research organisations (CROs) or from the public domain. From there, you can start to derive some idea of how to store your data, make it more interoperable, and ease data exchange and analysis to drive scientific decision making.’


Achieving quick wins


Implementing an underlying infrastructure is just as much a business change as it may be a technology or human-oriented change, Lynch noted. ‘It will almost certainly not be a quick fix but quick wins can be achieved. Software and data investment must be paralleled with an


Increased quality of data Understanding what a lab expects to do with its data and how scientific information is captured and then managed to allow that use is fundamental to the concept of data control, according to Jabe Wilson, consulting director for text and data analytics at Elsevier. It's all part of this same basic concept of long-term utility that inevitably filters through every discussion. ‘That understanding should go hand-in-hand with setting in place tools that can help improve the quality of data, and ideally apply standardised taxonomies and dictionaries. Increased quality of data will increase confidence in its utility, improve interoperability, and also help users derive more contextual relevance. That assurance of data relevance and quality means that AI and machine learning can be exploited to derive meaning from patterns and in-depth analyses.’


Another principle that may be


overlooked is that of timeliness, Wilson continues. ‘This is about making sure that your systems for controlling data can also allow that data to be processed quickly so that it is available in the right form, and in real time.’ Laboratories need to embrace the concept of data science and its potential to accelerate R&D and product development, Wilson suggests, referring to comments by Novartis’ chief digital officer Bertrand Bodson, who in an interview shortly after his appointment in 2018 stated, ‘We already rely on data, but how can we unlock its power to drive more of our decisions, so that we can get better drugs to patients faster?’ In reality that’s the bottom line for any pharma company, and for any R&D-driven industry.


August/September 2019 Scientific Computing World 17


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