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Data & Analytics in the Age of AI: Lessons from Healthcare Analytics

by Aniruddha Ghosh | at Minnebar20

AI is transforming work, and data & analytics is no exception. But beyond the headlines, what are companies actually doing with AI in their analytics workflows today?

In this session, we will start with a look at how organizations across industries are applying AI to existing analytics workflows: accelerating insight generation, automating reporting, enabling natural language data exploration, and more. We will ground this in real examples and observations from what is happening in practice.

Then we will take a step deeper into why this is harder than it looks. AI powered analytics does not work without strong fundamentals such as clear data definitions, governed semantic layers, and high quality data. Without these, AI might not just underperform, but it can dangerously mislead.

We will use healthcare as a lens for these challenges, where the higher stakes make these issues unavoidable. How do you ensure privacy and security when AI interacts with sensitive data? How do you evaluate and trust AI generated outputs? What should AI be allowed to do and what it should not? How do you make the case for investment when the return is not always immediate?

Whether you work in healthcare or any other industry, these lessons apply broadly. We will discuss what is working, what is not, and what data teams should be thinking about before diving in.

Aniruddha Ghosh

Ani Ghosh is a Data Analytics Manager at MedStar Health, leading the Capacity & Throughput Data and Analytics program to enhance operations across 10 acute care hospitals. Prior to MedStar Health, Ani was a Data Scientist at Target, where he led a team of analysts and engineers building data products that assessed forecasting model performance across $100B revenue verticals. His experience spans the full analytics spectrum, from executive dashboards and data engineering to deploying machine learning models at scale. Ani is passionate about closing the gap between data and decision-making, and believes that the biggest barrier to analytics impact is not technology but how analytics work is organized and governed. Outside of work, he enjoys tennis, hiking, and exploring national parks.

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