Solving Data Management Challenges with Strategic CRO Oversight

One

Your data management CRO isn’t fully leveraging technology.

One common challenge is an over-reliance on manual processes for data cleaning. Manual review can introduce inefficiencies and increase the risk of errors, as humans are naturally prone to oversight.

At PharPoint, most of our data cleaning tasks are supported by SAS programming. This allows for faster, higher-quality processing of large datasets and reduces the risk of human error.

Another contributing factor to poor technology adoption can be insufficient training for data coordinators. Without exposure to advanced tools or techniques – such as SAS or enhanced Excel functionality – team members may not realize what is possible beyond manual review. Comprehensive training and a collaborative team can help bridge this gap.

Two

Your data management CRO is waiting until database lock to conduct 90% of the data cleaning.

Another key observation is the tendency of data management teams to lag behind in data cleaning efforts until the database lock stage. When the majority of cleaning happens at the end of a study, it creates bottlenecks for all stakeholders — data managers, CRAs, and site staff.

Early and ongoing cleaning throughout the study lifecycle helps maintain data quality, reduces last-minute surprises, and ensures a smoother path to database lock. Ultimately, this approach helps teams supports timely deliverables with the highest data quality, whether for interim analyses, regulatory submission requirements or final database lock.

Three

Your data management CRO doesn’t collaborate with their biostatistics team.

Data Management and Biostatistics often operate as separate functions, yet their work is highly interconnected. Early collaboration with biostatisticians can provide data managers with valuable context and prevent downstream challenges post-database lock.

In one recent example of this cross-functional teamwork in action, the PharPoint data management team was determining how to best report a complex screen failure situation. The team held discussions with the biostatistician to help brainstorm, who was able to inform on which potential data entry solutions under consideration would impact final analysis outputs. What the data management team was considering as the leading option was ultimately decided against with the input from the biostatistical perspective.

This type of casual collaboration and integration across functions helps data managers proactively make informed decisions while keeping statistical endpoints in mind.