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For the longest time, companies stayed away from trying out new tech stacks in pharmacovigilance (PV). But with the rise of generative AI, things are changing-fast. Big players like Bayer teamed up with Genpact to use AI for automating case intake. Sanofi joined hands with IQVIA to apply AI for end-to-end case processing. And these are just a couple of examples. As case volumes keep going up year after year, everyone’s now in a race to bring AI into the game-to cut costs, boost quality, speed up case processing, and let safety teams focus on what really matters....

A few months ago, I had an insightful conversation with a PV leader who asked about the current opportunities within PV technology for a CRO. Like most, my initial response was a generic suggestion—embrace AI, automate processes, and so on. However, this post isn’t about the usual recommendations you hear everywhere. Now that I’ve spent considerable time in the pharmacovigilance domain, I’ve encountered specific bottlenecks that persist in PV technology companies....

Over the past year, I’ve had the opportunity to interview for several senior Product Manager roles. After going through multiple interviews, I noticed a pattern in the questions being asked, and I thought it would be helpful to share them here on my blog for those preparing for similar positions. The questions I encountered typically fell into four main categories: Categories of Interview Questions By sharing these insights, I hope to provide a useful resource for fellow product managers looking to advance their careers....

Data Visualization Basics for Product Managers As a product manager, you’re constantly swimming in a sea of data. Whether it’s metrics on product performance, website traffic, customer data, or market insights, understanding and making sense of this data is crucial for informed decision-making. Today, let’s dive into the basics of data visualization and how it can empower you in your role. Getting Started with Data Before diving into visualization, it’s essential to get acquainted with your data....

Factor analysis is a statistical method that can be used to reduce many variables into a smaller number of factors that explain the underlying structure of the data. Principal components analysis (PCA) is a type of factor analysis that is commonly used in practice. PCA works by transforming the original variables into a new set of variables called principal components. The principal components are ordered by their eigenvalues, with the first principal component explaining the most variance in the data, followed by the second principal component, and so on....