In an era marked by rapid technological advancements, the pharmaceutical and life sciences industry stands at the forefront of transformative change. The integration of analytics and AI has emerged as a game-changer, revolutionizing the way these sectors operate. From drug discovery and development to patient care and regulatory compliance, the marriage of data-driven insights and cutting-edge technologies has opened new frontiers for innovation and efficiency.
In this dynamic landscape, the impact of analytics and AI extends far beyond mere buzzwords. These tools have become indispensable for companies seeking to navigate the complexities of drug development, optimize operational processes, and deliver personalized healthcare solutions. This blog explores some of our favorite stories from pharmaceutical and life sciences customers from 2023, reinforcing how these enterprises can harness the power of analytics and AI to not only survive but thrive in an environment defined by constant evolution and heightened expectations.
1. Streamlining Analytics & Machine Learning at Novartis
Like in many pharma companies, the business team at Novartis has a weekly task of updating data in Excel to generate important metrics. This process involves repetitive manual calculations of various key performance metrics and decisions are made based on the outcomes.
The team faced some obstacles, such as modifying key parameters in the existing process. Additionally, there was a lack of real-time data refresh and ineffective data tracking, leading to discrepancies due to human error. This also affected the team's ability to identify risks in budget forecasts and field-force allocation.
To solve these issues, Novartis’s data engineering and data science teams came together and developed an automated solution using Dataiku. With this solution, the team can now avoid repetitive manual calculations and make more informed decisions based on accurate and real-time data. Key performance indicator (KPI) reports are refreshed automatically, and pre-built templates are used for visualization and reporting on the custom-built forecast models. To learn more about the use case, check out their Frontrunner award submission.
2. Generating Targeted & Actionable Internal Medical Insights at Moderna
Having great stores of patient and drug-related data is useless if you don’t have the means to leverage, transform, and model that data. Platforms like Dataiku help data teams clean and transform data from free-form texts, medical reports, doctors’ notes, hand written notes from the field, and more. With a consolidated workbench for pharmacists, analysts, and data engineers, the platform can provide timely insights using large language models (LLMs) with sentiment analysis and natural language understanding that can drive infectious disease intervention and education.
For Moderna, a leading global pharmaceutical and biotechnology company, this type of use case allowed them to:
- Leverage Generative AI
- Drive improved efficiency within the data team (process time was reduced from 10 hours per month to fraction of it, thanks to automation).
- See a cost savings of about 40 hours per month.
- Reuse models across a variety of natural language processing (NLP) use cases.
- Discover new insights by way of sentiment analysis trends.
To learn more about the use case, check out the Frontrunner award submission.
3. Enterprise-Level Data Democratization at Merck
At Merck, a market leader in biopharmaceutical research, manufacturing, and supply, collective efforts from various departments, regions, and teams are pivotal for maintaining the quality and accuracy of data projects.
While digitalization and continuous medical advances have helped the team meet higher expectations and needs, it has also generated increasingly large data stores. Democratization of this data at an enterprise level was identified as a transformative business solution that would break down data silos, promote further collaboration, and empower employees with data-driven insights. But going "enterprise" with data democratization would make the challenge multifold due to the associated cross functional demands, policies and scaling efforts.
Merck explored various product options, and most of them posed yet another barrier of "stiff learning curve." Irrespective of user personas, many products required technical integrations and coding language capabilities. So the critical business challenge Merck contemplated was how to enable people, processes, and technology at scale and speed to achieve their enterprise-level data democratization and advanced analytics goals.
Leveraging Dataiku met the four key components of success for these goals: data accessibility, self-service (no/low code) analytics, governance, and enablement. The value of this new enterprise data strategy is reflected in hyper-productivity gains, enhanced decision-making, increased collaboration, and improved business agility. To learn more about the use case, check out the Frontrunner award submission.
4. Driving a Sea Change in Drug Discovery at Regeneron
In partnership with Regeneron, the MIT Technology Review published a report with Dataiku exploring how AI is gaining momentum across the life sciences space. In it, they share how they already have a dozen different ML and AI models used in their research and drug discovery process — from analyzing large volumes of images for detecting subvisible particles in injection formulations, including proteins and silicon oil droplets, to predicting and preventing potential immune responses to Regeneron’s products.
AI augments human researchers, doctors, and other participants in the medical community to make processes more efficient and find information that might otherwise be lost, says Shah Nawaz, chief technology officer and vice president of digital transformation at Regeneron. Nawaz also spoke on our panel “Making Everyday AI a Reality in the Age of Generative AI” at Everyday AI New York in September!
Looking Ahead
In addition to the customers and use cases highlighted above, we also want to shout-out other subject matter experts and analytics & AI leaders from pharmaceutical and life sciences companies who spoke at Everyday AI New York in September. Your participation was invaluable!
- Abdul Shaik, Head of Enterprise Data & Analytics, Regeneron
- Ranjit Kumble, Ph.D., VP of Data Science Solutions & Initiatives, Pfizer
- Renu Genring, Former Sr. Director, Enterprise Data Science CoE, Alkermes
- Polina Venyukov, Associate Director, Enterprise Data Science CoE, Alkermes
- Kezhuo Zhang, Associate Director, Enterprise Data Science CoE, Alkermes
As we stand at the intersection of technology and healthcare, these narratives not only celebrate the achievements of today but also set the stage for the continued evolution of the pharmaceutical and life sciences sectors. By harnessing the power of analytics and AI, these industries are not only adapting to change but also pioneering innovations that have the potential to transform patient outcomes and the future of healthcare as a whole.