Data production in life sciences and biomedicine is at an all-time high. Scientists and clinicians have laboratory instruments at their disposal that can deeply explore the nature of life and disease, generating massive amounts of data quickly for relatively little money. As a result, life sciences has become among the top data generators out of all the sciences. Along with the massive press to advance Artificial Intelligence (AI), the sheer volume of data and analytics needs of the field now require a better understanding of datasets, the ability to create high-quality cohorts of data quickly, and the ability to utilize large amounts of data from other sources in single meta-analyses. That’s where the concept of data being FAIR comes into play. FAIR refers to the ideal state of data management for scientific data. While the industry has been refining and trying different approaches to FAIR for the last decade, it has yet to agree on many data standards or formats, thus making FAIR fleeting.

The iCMC Digital Transformation Program is a holistic program to create one integrated state-of-the-art digital ecosystem across all development labs (R&D and industrial Affairs) in Sanofi worldwide. The talk will particularly focus on the benefits & challenges of a data-centric approach in such a program: 1) Harmonizing the process and the data: a deep change of mindset; 2) Accelerating decision-making for all the teams; 3) Unlocking the data to enable new use cases thanks to Advance Analytics & Machine Learning; and, 4) Establishing the Data Continuum between R&D and Industrial Affairs.

Managing research data, and making it available for reuse by others in line with Open Research & FAIR principles, is a major issue for research-intensive organizations. In this case study we will outline an institute-wide review of data management, carried out at The Franklin. We will describe the processes involved in the review of current services, the learnings, and next steps.

The healthcare industry is undergoing a major transformation driven by gradual shifting from One-size-fits-all to stratified and outcome-based targeted treatment. As enhancing patient outcome and reducing costs become critical for healthcare and pharma companies, Precision medicine and its ecosystem is emerging as a critical focus area. In this presentation, we will look to explore key growth opportunities, precision medicine has to offer for different stakeholders across pharmaceuticals, healthcare providers, digital health, diagnostics, data analytics and technology companies.

Data tells the story of our business's past, present, and future, and system design supports the process of reusing data efficiently. Although everyone starts from a different level, there are some basic principles to follow. This presentation will focus on the three pillars of PEOPLE, PROCESSES, and TECHNOLOGY, and will describe the driving forces behind meaningful data integration of preclinical and clinical data.

The 20 years since the Human Genome Project has seen transformational advances in the molecular understanding of cancers and rare genetic diseases but precision medicine has had limited impact on improving diagnosis or treatment complex chronic diseases such as neurological and neuropsychiatric disorders. To overcome this challenge, we have completely reimagined how to analyze patient data in order to understanding the mechanistic drivers of disease biology and develop diagnostic biomarkers and precision-targeted therapies for chronic diseases which affect billions of people.

Neurologic disorders have long posed persistent challenges for clinicians and researchers. However, in recent years, significant advancements in data availability, computational power, and techniques have sparked a revolution in our understanding and actionable insights for these diseases. This session will delve into these exciting developments, exploring the latest trends, specific software, datasets, and collaborative communities that are driving new breakthroughs and discoveries in the field.

We present the Data Products and Data Mesh concept and how they can support the best use and re-use of data generated in pharma research. The main driver of using these concepts is the challenging transition of moving data as a byproduct of the research process, to becoming one of its critical assets. We present different ideas and implementations of data products in the pharma space and how research and informatics can work together through the Roche Data Insights (RDI) strategic theme to create data products and build a data mesh across the research process.