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It’s mostly preaching to the converted to say that ‘open-source is changing enterprises’. The 2020 Open Source Security and Risk Analysis (OSSRA) Report found that 99 per cent of enterprise codebases audited in 2019 contained open-source components, and that 70 per cent of all audited codebases were entirely open-source.

Hearts and minds have most certainly been won, then, but there are still a surprising number of enterprise outliers when it comes to adopting open-source tools and methods. It’s no surprise that regulated industries are one such open-source averse group.

It’s still difficult to shake off the reputation open-source resources can have for being badly-built, experimental, or put together by communities with less recognisable credentials than big players in software. When your industry exists on trust in your methods – be it protecting client finances in banking, or the health of your patients in pharma – it’s often easier just to make do, and plan something more adventurous ‘tomorrow’.

This approach made a certain amount of sense in years past, when embracing open-source was more a question of saving capex with ‘free’ software, and taking the risk.

Then, along comes something like Covid-19, and the CEO of Pfizer – who are now among those leading the way in a usable vaccine – singing the praises of open-source approaches back in March 2020. Months down the line, AstraZeneca and Oxford University’s 70 percent-efficacy Covid-19 vaccine emerged. AstraZeneca is having a public conversation around how it’s “embracing data science and AI across [the] organisation” while it continues to “push the boundaries of science to deliver life-changing medicines”.

Maybe tomorrow has finally arrived.

At Mango, our primary interest is in data science and analytics, but we also have a great interest in the open-source programming language R when we’re thinking about statistical programming. We’re not attached to R for any other reason than we find it hugely effective in overcoming the obstacles the pharmaceutical industry recognises implicitly – accessing better capabilities, and faster.

With a growing number of pharmaceutical companies starting to move towards R for clinical submissions, we thought it would be useful to find out why. Asking experts from Janssen, Roche, Bayer and more, we collected first-hand use cases, experiences and stories of challenges overcome, as well as finding out how these companies are breaking the deadlock of open-source’s reputation versus its huge potential for good in a world where everything needs to move faster, while performing exceptionally. Watch the full round table recording here.

If you’d like to find out more, please get in touch and we’d be happy to continue the conversation.

Author: Rich Pugh, Chief Data Scientist at Mango

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2020 has been a year for the history books – unfortunately though for reasons we’d all prefer to forget! Many businesses have experienced a really tough year, but rather than seeing it as one long crisis, I prefer to view it as many different waves of mini-market changes.

To explain, factors like lockdown rules and related government guidance that have and continue to change in accordance with the severity of the Covid-19 pandemic, has led to many different market environments. It’s a constantly evolving reality and those companies who are best able to predict and adapt have been able to steal a march against more flat-footed rivals –  online retailers that now have the definite edge over bricks and mortar retailers are a perfect example. 

Looking inward

As a business owner, the crisis has been a time of doubt, and has prompted an in-depth analysis of costs and revenues. Many organisations have been forced to explore ways of creating more value with existing or fewer resources. Trimming waste from budgets, and ensuring ROI, has been essential for us and many others, and there is a strong desire to embed a data-driven approach to areas that previously might not have been considered. At Mango, conference budgets for instance have been transferred and utilised elsewhere in the business, allowing our data science teams to widen their approaches and deliver more value. 

Implementing a data-driven approach allows organisations to optimise their business effectively, which has helped them to make quicker, effective decisions. In fact, a recent report by Sisense found that 49% of respondents surveyed said analytics were more or much more important than before COVID-19. The changed circumstances has led to a requirement for more agile approaches backed up by predictive analytics. 

Whilst many organisations were in crisis mode in the early parts of the year, the new circumstances have allowed time for consideration and change. Business as usual was never going to be as effective in a swiftly transforming world, and it has created an opportunity for companies to try different things, bring forward innovation and change approaches to markets. We’ve seen technology providers embrace the opportunity by speeding up release cycles and driving their engagement with totally different markets. The ease with which my mum started using Teams for video calls was a fascinating compliment to the developers of that product and I’ve no doubt that the digital revolution for marginalised groups such as the elderly has been enhanced massively.

Looking outward

We work within a range of styles with our customers – some prefer to completely outsource, while others look to us to develop and enhance an existing team. When lockdown bit early, many companies immediately put a halt to recruitment processes, which meant that in order to execute workloads, we were able to help create data science teams for customers to deploy and maintain momentum around data-led initiatives.

Several months on and as we face renewed restrictions, I believe that this time around a lot of organisations will regard it as an opportunity to roll out new methods and move further towards harnessing the power of data science. Covid-19 has provided a stimulus to boards to be creative and flexible since all businesses have been affected. It’s an ideal opportunity for organisations to step up and adapt their business model to take advantage of areas such as innovation and data science, which might well have been on the agenda, but were probably tucked away a bit further down ‘for review’ in a few years’ time. Taking action and investing now is vital and a relative “free hit” for leadership teams.

Virtual is going to be totally dominant from now on. Those companies who have embraced it wholeheartedly will have a massive advantage, and I think we’ll see an acceleration in the adoption of online only business in pretty much every aspect of our lives. We see this as beneficial for Mango in that a transition to a digital approach to business necessitates a primarily analytic led strategy.

Looking forward

With most organisations moving towards a less office-based environment, there are opportunities to change styles of working and this will include how analytic code is held and distributed. This may well involve outsourcing of analytic development, where virtual teams can become extremely effective. In the future we are likely to see more confederacy in teams enabling organisations to extend teams and create focused high delivery groups from different resources. I think we’ll see much more team augmentation with increasingly effective outcomes. It’s exactly why now is the time to invest in data science initiatives.

Author: Matt Aldridge, CEO at Mango Solutions