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Thursday, March 23, 2023

Top Data Science trends business executives in life sciences must track in 2023

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Data science has grown rapidly in recent years, from statistical problem solving to tackling real-world situations and making accurate fact-driven forecasts. It has transformed how fundamental health operations are carried out in the life sciences, as well as optimising commercial models through expedited consumer insights, targeted omnichannel campaigns, and streamlined sales and marketing activities.

Data science has grown rapidly in recent years, from statistical problem solving to tackling real-world situations and making trustworthy fact-driven forecasts. The application of data science and analytics in life sciences has progressed from fundamentally altering how basic health procedures are carried out to optimising commercial models through accelerated customer insights, personalised omnichannel campaigns, and streamlined sales and marketing activities. With additional developments and discoveries in data and technology industries, 2023 might be the year of data science. It is critical for commercial analytics and IT teams in life sciences to be future-ready, track the growth of data science applications, keep on top of major trends, and gain a competitive edge.

Here are four data science trends that can assist business leaders in the bio sciences fine-tune their models and enhance outcomes:

Real-time analytics enable exceptional customer experiences

71% of today’s customers want firms to provide individualised connections, and 62% of Healthcare Professionals (HCPs) expect hyper-contextualized interactions from sales reps. Analytics-driven recommendations and next-best-action systems that use AI and ML approaches to monitor consumer behavioural drivers, improve interaction settings, and produce near-real-time suggestions will remain crucial. In addition, sophisticated predictive and adaptive analytics must be included throughout these systems. Predictive analytics employ relevant data to forecast a customer’s expected behaviour, whereas adaptive analytics learn from each customer encounter to enhance predictions and constantly increase proposal success. These proposals have the potential to greatly improve marketing and field-force effectiveness during client interactions.

Industrialized Machine Learning (ML) is assisting in the acceleration of business choices

In the biological sciences, machine learning models are on the rise, promising better therapeutic efficiency, expedited drug development, early illness detection, efficient marketing, optimal customer experiences, expanded treatment options, and more. However, due to data and procedural issues, few of them make it past the pilot level. ML industrialisation lowers the barriers to creating and using ML models, allowing businesses to scale them effectively and deliver insights in time to support business decisions. Advanced cloud technologies and data science platforms are key enablers, allowing data scientists, engineers, and architects to collaborate, rapidly design, and deploy AI systems.

Natural Language Processing (NLP) is assisting in the speedier discovery of consumer insights

NLP is a rapidly expanding discipline with several real-world applications, including medical text processing for patient safety event reporting, regulatory compliance, commercial content production, social media listening, consumer personalisation, and more. Deep learning models can convert unstructured language in documents and databases into normalised, structured data that can be analysed, resulting in considerable cost and time savings. Some life sciences businesses have already integrated NLP into their operations, and it is anticipated to gain traction in the coming months.

Low-code and no-code development solutions that promote organisational agility

Low-code and no-code systems can be a game changer for life sciences organisations, allowing them to process enormous amounts of healthcare data fast. As a consequence, operational efficiency, ease of integration, efficient risk management and governance, and rapid business insights will be realised. Data science applications continue to improve, introducing new efficiencies that will support commercial operations’ growth and innovation. Tracking and acting on these patterns will enable life sciences businesses to get access to meaningful, business-critical information in record time, respond strategically to competition and market changes, achieve considerable operational efficiencies at scale, and sustain better outcomes.

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