Clinical research organizations (CROs), the extended R&D arm of pharmaceutical and biotechnology firms, are pivotal to clinical trial success and the launch of drugs or therapies. With hordes of data on best-fit investigator profiles, patients, and investigation sites, they manage compliance reporting, forecast project resources and requirements, and design clinical trials.
However, this treasure trove of data was becoming a bottleneck for a global CRO. Having recently acquired a new business, the client struggled to consolidate diverse data sources across inoperable systems. Operational efficiency was impacted due to data inconsistencies, inaccuracies, and silos that required teams to sift through hundreds of data records manually. The lack of consolidated and accurate data hindered the ability to forecast resource needs and other critical aspects of strategic planning, and limited visibility in sites with readily available resources for conducting a study. It kept the sales teams out of the loop with real-time ground realities. These challenges came with high stakes: regulatory non-compliance, inability to meet study demands, and missed opportunities to collaborate with global sponsors.
This was not just an unmanaged data sprawl; it was fast becoming a business threat. The client turned to Persistent to standardize and monetize more than 2.2 million reference data points across business functions so that sales and operations teams could work as one.
Managing data sprawl with AI
Recognizing the profound impact of these data silos, Persistent deployed a robust, scalable Master Data Management (MDM) Hub built on Reltio MDM. At the heart of this solution was an advanced AI-based matching algorithm that transformed the client’s data ecosystem. By leveraging advanced machine learning algorithms, the AI could profile, match, and harmonize more than 60 critical data elements spanning therapeutic codes, study phases, investigator identities, and drug records.
First, the AI analyzed datasets to identify patterns, inconsistencies, and anomalies across disparate sources. Through self-learning techniques, it continually refined its understanding of how different data attributes related and where discrepancies most commonly arose. Next, the platform executed sophisticated data matching routines. Rather than relying on simple rule-based matching, the AI incorporated probabilistic matching, fuzzy logic, and contextual analysis. For example, it could recognize when investigator names were spelled differently or when affiliated sites had address variations, intelligently linking these records to prevent duplication. This matching process was not static; the AI adapted as new data was introduced, learning from previous corrections and user feedback to improve future accuracy.
In addition, the AI orchestrated automated data quality checks before ingestion, applying custom quality rules to flag and remediate issues such as incomplete entries, outdated information, or conflicting reference data. These rules were flexible and could be refined over time, ensuring the data pipeline became more robust with continued use.
The AI also optimized data validation and accelerated loading processes by predicting potential bottlenecks or errors in data integration. It automatically adjusted load priorities or suggested corrective actions to data stewards, dramatically reducing manual intervention and speeding up the data integration process by 50%.
By continuously monitoring, cleansing, and standardizing incoming information, the AI-driven system ensured that only the highest quality data entered the master repository. This empowered downstream analytics, regulatory reporting, and business forecasting with trustworthy, complete, and up-to-date information. The automated and enhanced data management enables business teams to make more confident, timely decisions fueled by reliable insights.
Beyond the MDM hub, Persistent established a centralized repository for all reference data in Reltio RDM, positioning it as the definitive source of truth. Collibra was integrated to define and enforce data governance policies, automate stewardship workflows, and synchronize changes in reference data seamlessly with Reltio RDM. This holistic approach ensured that business policies, data processes, and AI-driven automation worked in concert to maintain data quality and consistency.
40% Improved forecasting, 60% faster new data source integration
With AI, the client standardized more than 2.2 million reference data points into master records, substantially reducing duplication and boosting data accuracy. AI-driven automated validation and reporting frameworks saved over 55% of the time and effort previously expended on manual data correction. Forecasting and analytics capabilities improved by 40% with cleansed, standardized, and de-duplicated datasets, empowering sales teams to confidently predict resource needs and identify high-potential sites and investigators. Integrating new data source systems is now 60% faster, as is the client’s ability to pursue business opportunities and strategic initiatives.
Most importantly, previously unattainable KPIs, such as timely resource forecasting, regulatory compliance, and rapid business expansion, became achievable.
