The field of data science has seen explosive growth over the years, expanding beyond statistical problem-solving to addressing real-world issues and creating reliable fact-driven predictions across industries. In fact, data and analytics leaders who share data externally end up generating three times more measurable economic benefits vis-à-vis those who don’t.
In life sciences, the use of data science and analytics has grown from fundamentally changing how the most basic health procedures are conducted (simply by shaping and mapping unstructured information) to optimizing commercial models through accelerated customer insights, personalized omnichannel campaigns, streamlined sales and marketing activities, etc.
With more advances and innovations being made in related data and technology fields, there’s every reason to believe that 2023 could well be defined as the year of data science and analytics. This makes it all the more important for life sciences’ commercial analytics and IT teams to be future ready, monitor the evolution of data science applications, stay on top of the key trends, and gain a competitive advantage.
Here are 4 data science trends that can help life sciences commercial leaders fine-tune their models and improve outcomes:
71% of today’s consumers expect personalized interactions from companies, per McKinsey2. An Indegene survey also revealed that 62% of Healthcare Professionals (HCPs) demand hyper-contextualized interactions from sales representatives – making personalization a top-of-mind priority for life sciences leaders. Analytics-driven recommendations and next-best-action systems will continue to be critical. These systems leverage AI and ML techniques to analyze customer behavioral drivers, optimize interaction parameters, and develop near-real-time recommendations, indicating what to do next, for a particular customer.
Sophisticated predictive and adaptive analytics are also crucial to have embedded within these systems. Predictive analytics use relevant data to predict the expected behavior of a customer, such as how likely will an individual engage with a specific content or campaign. Adaptive analytics learn from each customer interaction to refine predictions and continuously improve the success of propositions. When delivered in real-time, these recommendations can significantly optimize marketing and field-force effectiveness during customer engagements.
The use of ML models is on the rise among life sciences companies as it promises increased therapy efficiencies, accelerated discovery of drugs, early diagnosis of diseases, effective marketing, optimized customer experiences, enhanced treatment opportunities, and more. But even with a multitude of ML models being developed or tested, very few of them ever make it past the ‘pilot purgatory’. Why? Because of umpteen data and process-related challenges involved in operationalizing ML models. At best, even if they are deployed, it is not at the speed or scale to meet business needs. Eventually, only a handful of people end up using these models, with the dreams of scale not being entirely realized. This challenge is far too common in other industries too.
This is where ML industrialization becomes highly critical. ML industrialization reduces the hurdles involved in developing and operationalizing ML models, pushing them beyond the pilot stage and allowing companies to scale them effectively and generate insights in time to support business decisions when they are likely to be most impactful. The key enablers to getting this done right will include a combination of advanced cloud technologies (for storage and processing data at scale) and data science platforms to empower data scientists, engineers, and architects to collaborate, rapidly build and deploy AI applications.
NLP is a growing field with an incredible number of real-world applications. In essence, it structures valuable information from unstructured biomedical literature. A combination of deep learning models can help transform unstructured text in documents and databases into normalized, structured data (such as knowledge graphs) suitable for analysis. This can lead to significant savings in terms of reduction in manual efforts and minimizes the time taken to unearth insights that can aid appropriate actions.
While some life sciences companies have already embedded NLP at the core of their operations, we expect this to grow in popularity in the coming months with NLP playing a pivotal role in several AI use cases that support downstream tasks. These may include medical content processing for patient safety incident reporting, regulatory compliance, commercial content creation, social media listening, customer personalization, and beyond.
Similar to its impact in other industries, low-code, no-code tools can be a powerful game-changer for life sciences companies. It places non-citizen data science business users on a fast-track mode to identify and enable process improvements through automated workflows designed to improve patient care and enhance customer-centricity efforts.
Companies who adopt these tools will benefit immensely from the ability to quickly process large quantities of healthcare data, through simple visual drag-and-drop or point-and-click user interfaces, which will result in operational efficiency, ease of integration, effective risk management and governance, and quick business insights – ultimately accelerating time to market. Streamlining and automating processes through this approach will empower life sciences organizations with more control, oversight, and reduced risk.
Data science applications continue to evolve, creating new efficiencies that will complement growth and innovation in commercial operations. Tracking and acting on these trends will enable life sciences organizations to access relevant, business-critical insights in record time, respond to competition and market changes strategically, drive significant process efficiencies at scale, and sustain superior outcomes.
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