Exploring New Frontiers with Reltio’s Data Capabilities in Pharma

In today’s rapidly evolving pharmaceutical landscape, Master Data Management is evolving beyond traditional record-keeping into a strategic enabler of commercial excellence. At Chryselys, we’re leveraging Reltio’s innovative capabilities to transform pharmaceutical commercial operations.

Here are several promising areas where we see tremendous potential for innovation.

Real-time Data Quality Dashboards: Visibility When It Matters
Imagine territory alignment decisions informed by immediate visibility into data completeness, accuracy, and consistency metrics rather than discovering issues after quarterly cycles. The potential benefits include shortened territory realignment cycles and significantly improved sales force effectiveness through higher-quality targeting data.

At Chryselys, we have built real-time data quality dashboards on Reltio to transition commercial data managements teams from reactive to proactive data management.

By incorporating AI into these dashboards, we have developed solution that not only identify data quality issues but also provide intelligent recommendations for remediation and predict potential downstream impacts on business operations before they occur.

Contextual Data Quality Anomalies: Business-Aware Insights
Data anomalies are most valuable when understood within their business context. Our solution works through connecting Reltio’s robust anomaly detection capabilities with enterprise landscape awareness to revolutionize how pharmaceutical companies interpret data issues. For example, systems that don’t just flag unusual changes in HCP data but contextualize them within recent mergers, regulatory changes, or market events could save countless hours of investigation while providing commercial teams with accurate relationship data during critical market transitions.

Our approach leverages AI to analyse patterns across multiple data domains, automatically generating narrative explanations that connect anomalies to business events and providing data stewards with context-rich insights rather than isolated alerts.

Stewardship Co-pilot: Intelligent Governance Automation
Data stewardship often becomes a bottleneck in pharmaceutical organizations where thousands of data change requests require manual review. We’re enhancing Reltio’s stewardship capabilities, with AI-powered assistance, could transform this process.

An intelligent “Co-pilot” could potentially automate a significant percentage of routine data change requests based on learned patterns and business rules, reducing the time-to-completion for data changes from days to hours and ensuring commercial teams operate with the most current information about healthcare providers and organizations.

AI powered Co-pilot works by analysing historical decision patterns, suggesting approvals based on organizational precedent, and even
drafting response communications to requestors—all while continuously learning from human stewards’ actions to improve recommendations over time.

Reference Data Automation: Maintaining the Foundation
Pharmaceutical reference data—product codes, organizational hierarchies, classification systems—forms the foundation of commercial operations, yet managing constant changes creates significant overhead.

We’re pioneering an approach that goes beyond basic reference data management by leveraging Reltio’s flexible data model combined with generative AI capabilities to:

• Automatically identify new reference data from incoming sources and enterprise systems that may require formalization into master reference sets.
• Intelligently map new reference values to existing canonical forms, dramatically reducing manual matching efforts
• Create new canonical forms when needed, following organizational naming conventions and hierarchy rules.
• Convert local taxonomies to global categories through GenAI-powered semantic analysis that understands conceptual similarities across different terminology systems.

This approach is particularly powerful for global pharmaceutical organizations managing reference data across multiple regions, therapeutic areas, and acquired product portfolios. By applying AI to recognize patterns and suggest mappings, the system can automate up to 80% of routine reference data management tasks while improving consistency and reducing time-to-insight.