What We Learned at SCDM 2025—and How to Act on It

Each year, the Society for Clinical Data Management (SCDM) conference offers a pulse check on where the field is heading—and a roadmap for what to do next.

If you were in Baltimore this fall, you likely felt the shift: clinical data management is evolving from reactive cleaning to proactive orchestration.

Here’s what we learned at this year’s event and how data and biometrics teams can turn those insights into action.

1. AI is shifting from demos to agentic workflows

This year, vendors and speakers showcased agentic AI patterns—LLM- and SLM-driven “agents” that monitor data streams, suggest reconciliation steps, and explain their reasoning for audit.

These are no longer hypothetical tools. Teams are already piloting AI in quality control, coding suggestions, and anomaly triage—areas where oversight by humans remains essential.

The takeaway was clear: start where results can be measured, and build governance before you scale. Organizations that emphasize explainability and validation early will reach production faster and with more confidence.

2. ICH-M11 and Digital Data Flow are the new north star

Many sessions focused on the foundations of the next era of clinical research: structured protocols and interoperable metadata.

ICH-M11 and Digital Data Flow (DDF) are reshaping how information moves across the trial lifecycle. The idea of “design once, reuse everywhere” came up again and again. Case report forms, coding lists, and edit checks can now be generated rather than rebuilt by hand.

Forward-looking teams are experimenting with structured authoring templates and automated CRF generation. These approaches aren’t simply about compliance—they’re about achieving speed, consistency, and traceability without compromise.

3. Leadership conversations centered on execution over hype

At the Leadership Forum, the buzzword fatigue was gone. Instead of debating the promise of AI, leaders dug into what’s actually working—how they govern, validate, and measure performance in real studies.

Cycle-time reduction, query aging, and issue resolution became the metrics that mattered. The takeaway? True progress isn’t about showcasing sophistication; it’s about proving consistent, measurable impact. As one speaker noted, “Transformation only matters if you can measure it.”

4. Upskilling in regulatory and AI literacy is now table stakes

This year’s pre-conference workshops focused heavily on hands-on skill building. Attendees explored practical AI use cases alongside updated sessions on regulatory expectations. Data managers are no longer just custodians of information—they are becoming AI-literate compliance stewards.

Understanding how to validate, document, and govern intelligent systems will soon be as essential as mastering CDISC standards. This upskilling movement isn’t only about technical fluency; it’s about ensuring that innovation and compliance advance together.

5. End-to-end thinking beats point fixes

Another recurring theme was the growing emphasis on end-to-end data flow. Across sessions and materials, speakers reinforced that lasting improvements come from linking protocol design, data capture, coding, and review within a single governed pipeline.

Point solutions can fix individual problems. Integrated ecosystems, by contrast, address the system itself.

Organizations embracing this mindset are reducing handoffs, simplifying workflows, and reimagining their data architecture for flow rather than files. The result is greater speed, transparency, and resilience.

6. The field’s maturity was on full display

The “Festival of Opportunity” lived up to its name. The discipline is expanding into full-fledged clinical data science, where technical skill meets operational strategy and technology stewardship.

Today’s data professionals bridge the worlds of research, analytics, and IT. Their work reflects not only technical progress but also a deeper understanding of how to connect data, people, and process to drive better science.

How to act on SCDM 2025’s insights

If there was one message from Baltimore, it was this: momentum only matters when it leads to motion.

To translate this year’s trends into tangible results, data teams can begin with three clear steps:

1. Start small with explainable AI.
   Pilot use cases that improve quality control or coding accuracy. Measure gains in query resolution and turnaround time.

2. Embed ICH-M11 principles now.
   Move toward structured protocol authoring so forms, mappings, and edit checks can be generated consistently and easily audited.

3. Design for flow, not files.
   Identify where manual rebuilds or data transfers still occur, and replace them with seamless, continuous data movement across your pipeline.

The opportunity ahead

The future of data management will be connected, intelligent, and governed by design. AI, interoperability, and standardization are no longer abstract goals—they’re practical tools that can accelerate science and improve quality.

At OpenClinica, we’re proud to support the data management community as it embraces this next chapter. Whether you’re exploring AI pilots, adopting structured design, or modernizing your data flow, our mission is the same: to help you turn insights into lasting capability.

👉 Request a demo and discover how you can bring these SCDM learnings to life in your next study.