and are rapidly entering the life sciences industry with the promise to boost productivity, speed up innovation and power data-driven decision-making.
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The next generation of cloud-based solutions are key to the successful adoption of these technologies and moving
The pharma conundrum
Life sciences organisations are facing disruption from internal and external sources. On one hand, we are seeing a shifting dynamic of rising payer – or formulary – power in the industry, coupled with a decrease in physician’s prescribing influence and society’s willingness to pay. Deciding where to focus spending sin the face of lagging productivity and R&D returns is compelling more and more biopharma companies to evaluate the possibility of automation.
On the other hand, more agile technology giants such as Amazon and Google have already made their first steps into the sector – bringing extensive financial clout and proven expertise of using emerging technologies. Technology, it seems, will be a key enabler and differentiator to success in the life sciences space – and now even ‘traditional’ players can compete with the giants.
Data, data, data
Pharma, like most industries, is hungry for data. A recent study reveals that C-level executives are especially keen to collect data on brand and reputation, financial forecasts and customer demands. Machine learning technology is now changing this, helping pharma businesses to easily explore data and identify complex patterns from vast data sets including patient health data, clinical trial feedback and research outcomes.
Machine learning – help or hinderance
The need for
Many of these challenges can be resolved by turning to more advanced platforms that automate significant amounts of the data preparation process, provide complete end-to-end visibility in their operations and ensure the human is kept fully in the loop.[…]
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