Tackling Challenges in Life Sciences Master Data Management Through Effective Machine Learning Implementation

Life sciences companies have always had to overcome specific master data management (MDM) challenges because of the massive, diverse and intricate clinical research records or patient data and regulatory mandates. It is critical that this data be well-managed to maintain efficient operations, meet various regulatory requirements and develop better patient outcomes. The transformative capabilities of machine learning (ML) can be great to simplify and streamline data integrations, improve data quality, as well as deliver predictive insights within MDM frameworks. While ML is not a silver bullet for life sciences MDM solutions thanks to data privacy issues, lack of standardization and scalability limitation where this may not make an impact. This paper also focuses on the barriers and provides recommendations for applying ML technologies in a practical approach within life sciences MDM. This study examines strategies for improving data management in life sciences, such as leveraging more advanced ML techniques like natural language processing (NLP) to work with unstructured data; anomaly detection to clean noisy or missingness in raw records and federated learning to train algorithms using privacy-preserving collaboration. This study uses a case studies and technical analysis approach to identify finally best practices and future research directions for utilizing the ML's full potential in life sciences MDM.