The implementation of a streamlined and connected data ecosystem is critical to the transformation of healthcare technology and circumventing data regret.

Damion Nero, Head of Data Science at Takeda, discusses the regulatory challenges and opportunities of Real-World Data.

The EMA’s Big Data Steering Group has released its third workplan outlining the key actions to be delivered between 2022 and 2025.

Looking to the next few years of data management in clinical trials, hybrid models which interpolate AI and ML approaches to assist trial coordinators will increasingly become the new normal.

Analytical data is a key tool which can be leveraged to augment digital drug discovery if data workflows are properly implemented.

FAIR data principles aim to standardise and enhance the management of scientific data through unique identifiers and standardised protocols. The gradual implementation of these principles can improve research efficiency, facilitating better data utilisation by both humans and machines.

In the lead up to our 2022 Pharma Data & SmartLabs Congress taking place this September, we sat down with Maya Natarajan, Senior Director Knowledge Graphs at Neo4j. Being a key player within the field, Maya gave us insights into the pioneering work Neo4j is doing with knowledge graphs and the types of graph analytics that Neo4J provides.

Key opinion leaders from AbbVie, Genentech, and the University of Southampton ask, “how and why must we change the way we work with data?”

Data quality, bias, and standardisation are all important to account for when planning for federated and non-federated learning factoring in recurrent neural networks. Biobank frameworks offer the opportunity to augment healthcare data through the appropriate implementation of machine learning.

If some gaps in patient data are inevitable, then the use of RWE and RWD presents an opportunity to fill them in synthetically. Coordinated data sharing is an important aspect of consistent RWD usage.