Data Management Expert Groups (DMEGs)

Data Management Expert Groups (DMEGs) are specialised forums created by the Association for Clinical Data Management (ACDM) to bring together professionals with deep expertise in specific areas of clinical data management. These groups foster collaboration, share best practices, and drive innovation, helping shape industry standards and advance the field.

Aim and Objectives
The primary goal of a DMEG is to provide ACDM members with a valuable platform where Subject Matter Experts (SMEs) across Clinical Research can engage in discussions, develop best practices, and lead the way forward in their areas of expertise. The specific objectives of DMEGs are to:
Create useful resources (e.g., articles, white papers, best practices, guidelines) for the ACDM membership to utilise.
Support the development of training activities to enhance industry knowledge.
Organise and participate in ACDM Hot Topics sessions and other events of special interest.
Contribute to conferences by encouraging attendance and actively engaging in relevant activities.
Keep members informed through regular updates and communications, fostering increased engagement.
Approach and Leadership

Each DMEG is led by one or more individuals who drive the group’s objectives forward in a non-biased and effective manner. Leaders are responsible for ensuring the group operates efficiently, creating a legacy for the group by identifying successors, and maintaining communication with the ACDM board regarding intentions and plans. The groups are designed to be dynamic, with active participation from ACDM members who are passionate and committed to the group’s goals.
Engagement and Support

Explore our DMEGs below

Data Science DMEG

The Data Science DMEG focuses on exploring the application of advanced data analytics, machine learning, and AI in clinical trials to enhance data quality, optimise trial efficiency, and generate deeper insights, while sharing best practices and bridging the gap between traditional data management and innovative data science tools within clinical research.

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