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LABORATORY INFORMATICS


Advancing progress in life sciences


highlights trends in life sciences research and development


Christian Marcazzo, general manager at IDBS,


Increasingly, data is becoming a focus for life science R&D firms looking to accelerate the product development lifecycle. The effective gathering, recording and storage of data is vital and adopting the right tools, processes and technologies that enable this will be key to gaining a competitive advantage. To achieve this, processes and


technologies that overcome their legacy counterparts will be key. There are several trends affecting this change – from unlocking dark data and security, to AI and the modern workforce – and that is driving the adoption of digitised assets and processes in the life sciences R&D industry.


Data strategies and unlocking dark data The need for new technologies and systems that streamline R&D is becoming ever more pressing. Legacy systems and processes of recording and collating data are prone to errors, owing to human fallibility, ineffective data collection tools, and siloed systems and teams. As can be seen across most industries, organisations are increasingly moving systems and services to the cloud. For R&D firms, cloud-based software-as-a-service (SaaS) platforms that integrate all systems are the most effective way of overcoming legacy. Not only do integrated cloud platforms


ensure more effective data gathering and storage, as all data can be uploaded and accessed instantaneously, they also enable all data to be used and fully contextualised across the entire drug development lifecycle, revealing previously


20 Scientific Computing World October/November 2019


“No organisation is too big to fail when it comes to security. In 2017, the UK’s National Health Service fell victim to its most disruptive cyber-attack”


hidden insights. This siloed data is what is often referred to as ‘dark data’. In life science labs, dark data is


uncovered by tracking and recording the full context of experiments in a single platform. Materials, lab conditions, and equipment used are just some of the factors that can provide crucial insights. Take, for example, the scenario of failure at the fermentation stage, owing to a contaminated bioreactor. If it is possible to pinpoint the exact moment of failure, the same situation can be avoided in future. Remediation will occur at a faster rate, as this stage can be rolled back to the moment exactly prior to failure, rather than having to start the entire process again. A study from Veritas suggests the


volume of dark data may account for half


of all information stored, so the potential advantage for R&D firms able to utilise this data is clear.


Data security


It is said that data, after talent, is an organisation’s biggest asset. Yet it’s hardly ever treated as such, with few firms really taking a data-first approach. What’s more, owing to recent high-profile data breaches, the implementation of robust cybersecurity tools and practices is topping the agendas of many R&D organisations. This is vital to establish trust with stakeholders and investors, which are, of course, key drivers of growth. No organisation is too big to fail when


it comes to security. In 2017, the UK’s National Health Service fell victim to the most disruptive cyber-attack in its history. The breach saw computer access locked for 24 GP trusts, prevented 40 hospitals from going online, and threatened to destroy patient data unless the hackers’ demands were met. A similar situation arising in a smaller R&D firm could result in a total loss of confidence in its business proposition from investors, not to mention fines from regulatory bodies; penalties smaller firms can ill afford. Again, cloud offerings are better equipped to ensure data security and


@scwmagazine | www.scientific-computing.com


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Gorodenkoff/Shutterstock.com


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