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Giving pharma a heavy dose of data science

Giving pharma a heavy dose of data science

and are rapidly entering the life sciences industry with the promise to boost productivity, speed up innovation and power data-driven decision-making.

SwissCognitiveThe next generation of cloud-based solutions are key to the successful adoption of these technologies and moving and beyond the current ivory towers of data science and into the hands of employees.

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 in the industry is clear to see. Nine in ten UK SMEs require data science as part of their drug discovery operations and one in two require and . But there are still issues to overcome around time-efficiency and transparency. Average project timeframes expand, not shrink, when models offer unclear or limited capabilities for data discovery, curation and preparation and are not reproducible across other data sets and business problems. There are also the questions of whether the prediction accuracy is visible, and if output can be understood without further input from specialist data scientists.

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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