WHAT IS IQVIA EXPLORER
Helping life science companies make better, faster commercial decisions
IQVIA Explorer turns complex, one-time analytics projects into an always-on commercial intelligence capability. Users can apply external indicators and ask business questions, and Explorer brings together the relevant IQVIA data, analytical methods and workflows to provide near real-time intelligence to support decision-making.
The result? Teams can identify opportunities sooner, improve execution and make faster, more informed decisions that drive commercial performance and ROI.
How it works
Interconnected reporting, scenario modeling and workflows.
- Patient journey insights inform the targetable HCP universe.
- Market access assumptions flow into forecasting.
- Impact metrics refine segmentation and targeting.
- Scenario updates cascade across the broader decision cycle.
Turn intelligence into confident decisions and action.
Why IQVIA Explorer
Connected Data
Intelligence Layer
Agentic Execution
IQVIA Explorer delivers faster results - from multi-week projects to same-day answers
Frequently Asked Questions:
IQVIA Explorer begins with our foundation of integrating IQVIA and third-party data, then enriching data with reference, catalogue, ontology and semantic layers to create high-quality AI-ready data for advanced analytic use cases. This intelligence layer connects insights for commercial forecasting and budgeting activities with pull through to executive- and external-level reporting.
Ultimately, the ontology allows customers to move from disconnected analytics and siloed decision processes to a scalable, decision-centric operating model that delivers faster insights, greater consistency, improved responsiveness to market events, and a stronger return on their data and AI investments.
Agentic workflows are operationalized across volumes of commercial use cases and connect data intelligence to workflows.
Most importantly, the ontology provides the foundation required for agentic analytics. AI agents need more than access to data — they require context, relationships, business rules and an understanding of how decisions are made. The ontology enables agents to understand what the data represents, apply the appropriate analytical methods, reason across multiple business domains and recommend or orchestrate actions within workflows. This transforms AI from simply generating answers to supporting and automating end-to-end decision making.
ARTICLE
AI and Analytics Evolution within Commercial Life Sciences
