Fit-for-purpose data, decision-ready insights: A conversation with Narasimha Kumar and Tobi Oremulé

Expert interview

Healthcare organizations are working with more data than ever before, but turning that information into confident, timely decisions remains a major challenge. As evidence needs become more complex across regulatory, HTA, payer, clinical, and commercial settings, the focus is shifting from simply having more data to using the right data in the right way.

In this video, Narasimha Kumar, Global Head of Technology and Data Services, and Tobi Oremulé, Head of Pre-Sales and Solutioning, discuss how fit-for-purpose data can help transform evidence complexity into decision-ready insights, and how organizations can build reusable evidence foundations that create value across the healthcare product lifecycle.

This video was originally published as part of the Evidence Insights, “From more data to better decisions: How fit-for-purpose data turns evidence complexity into decision-ready insights“.

Questions

• 00:00: Introduction
• 00:41: What does “fit-for-purpose data” mean in practice, and why is the distinction between more data and the right data so important now?
• 03:23: Before organizations select datasets or start an analysis, how should they define the decision they need to support? What changes when evidence generation starts with the decision rather than the data that happens to be available?
• 05:55: How do federated analytics and privacy-preserving access models help organizations generate evidence while maintaining governance, compliance, and trust?
• 08:07: How can organizations design evidence generation for reuse from the start?
• 12:46: Where can automation, AI, and expert data services add the most value – and what needs to be in place to ensure outputs remain governed, explainable, scientifically rigorous, and decision-ready?
• 15:25: Can you share a practical example where starting with the evidence question, selecting the right data sources, and applying the right governance or analytics model helped address a RWE challenge?
• 17:06: What is the first step organizations should take to move from fragmented, one-off evidence projects toward reusable evidence foundations that can support better decisions over time?

Explore the Evidence Insights series

Read the feature: Narasimha Kumar explains how reusable data foundations are becoming essential to generate trusted, decision-ready evidence across the product lifecycle.

Download the white paper: In From Fragmented Data to Decision-Ready Evidence, we explore how fit-for-purpose data networks can help life sciences teams generate evidence faster, strengthen confidence in their analyses, and make faster, better-informed decisions across the product lifecycle.

More from Narasimha Kumar

In “Building a reusable evidence foundation for confident decision-making,” published in The Evidence Base, Kumar explains how reusable data foundations are becoming essential for teams to generate trusted, decision-ready evidence across the product lifecycle.

“AI in drug development is not a data problem – it’s an evidence problem“, published in MedCity News, Kumar explores how the challenge for organizations is no longer collecting data, but preserving the context, meaning, and relationships that transform data into usable evidence.

In “How data from medical practice can inform care,” published in MedNous, Kumar explores how real‑world data challenges traditional views of standard of care and enables more accurate, data‑driven decisions across trial design and evidence generation.

In “AI Readiness in Pharma – Getting the Foundation Right,” published in Bio‑IT World, Kumar examines how data fragmentation, complex regulations, and limited interoperability are slowing AI adoption—and what it takes to build trusted, cross‑border evidence ecosystems.