LABORATORY INFORMATICS
across many different disciplines. ‘You have to ensure that you retain all of your data in a usable format, and that, if necessary, it will pass regulatory muster today and potentially years down the line.’ The need for data standardisation
comes up in any discussion on data management or control. Wilson said: ‘The need for standardised taxonomies, dictionaries and ontologies makes it possible to interrogate and compare data from one source, experiment or scientist, alongside data from any other sources. Make sure your identifiers and synonyms are aligned, whether you are referring to protein or gene identifiers, or describing diseases or phenotypes.’
g ‘Making the most of data will likewise be
a driver of competitiveness and ultimately success, aiding faster, more insightful decision making,’ Wilson points out. ‘To do that, companies need complete control of their data, so that they can easily find it, understand it, mine it and analyse it collectively.’ Lack of control of data can therefore impact on competitiveness. ‘One pharma company that has a better handle on their data than another will be better informed for making decisions on pipeline, and ultimately may get to market sooner.’ Better data means more informed decisions, and so lower attrition rates.
Data wrangling It may seem obvious, but most labs are way off that ability to usefully exploit every piece of data. As Bodson said in the same interview: ‘Our data scientists probably spend 80 per cent of their time right now on data wrangling to get the data in good shape, which is really a pain.’ This is not an uncommon bottleneck,
Wilson notes, but companies may be slow to understand that investing in new tools – or ensuring full application of existing tools – that can help to get data in the right shape, will pay off in the long run. Novartis is investing in building a platform to organise its data and ensure that it is fit for purpose, findable and accessible – we come back to FAIR Data principles – but that investment should start at the level of the scientists who generate and use that
18 Scientific Computing World August/September 2019
“Increased quality of data will increase confidence in its utility, improve interoperability, and also help users derive more contextual relevance”
data, so that they understand how and why they should record and annotate their results, Wilson said.
Avoiding the need to ‘fairify’ data ‘If you are carrying out an experiment you obviously make sure that it will deliver results for immediate use, but should ensure that they capture and record and make accessible every bit of data that may be relevant to future use of results. This will then make your data assets far more usable, both across a business, and also between partners or service providers, such as contract research organisations (CROs). Build this concept in from the ground up and you won’t have the literal, time- and effort-related costs of having to ‘fairify’ your data at a later stage.’ Wilson acknowledges that the
complexities of data control are not the same for every industry. In life sciences and pharma in particular, diverse, content- rich and high-throughput technologies for biology and chemistry generate vast quantities of disparate data, potentially
Harmonised and contextualised data Elsevier has applied its expertise in this area to develop a suite of data tools that it is making available to the industry. The organisation’s PharmaPendium gateway gives customers the ability to search regulatory documents and data from FDA and EMA, and add insight to in-house R&D, without adding to the complexity of data husbanding. The cloud-based Entellect platform has been developed to deliver harmonised and contextualised data that can then be exploited for advanced AI- driven analytics, Wilson explained. Entellect effectively links and manages
disparate in-house and third party data, and gives users access to Elsevier’s own databases and content collections for pharma R&D. It is then possible to leverage off-the-shelf data analysis applications or, through Elsevier’s Professional Services division, develop custom analytics applications. At its most basic level Entellect acts as an integration tool so that users can make their own data ‘fair’. But as an open platform, Wilson explained: ‘Entellect is also API-friendly, so users can interrogate the system and pull out information that they need’. ‘Users have immediate access to
diverse data, from drug chemical information to clinical trials data, and can and use that data in any way they like. It’s a hugely powerful tool that gives people ultimate control over their data and how it is applied to generate meaningful intelligence.’ Wilson develops the idea of data
science as an art. ‘You need to provide your scientists with the tools they need to be very intuitive and flexible about the work they do, but with the discipline to know how to collect, manage and control that data. This may need quite deep cultural changes so that it will eventually become second nature.'
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