Data Quality vs Quantity issue with ML and AI applications with Arvind Balasundaram

In this week’s CDO Insight series we have with us Arvind Balasundaram who with over twenty years of experience, is a prominent figure in the realm of commercial insights and analytics. His extensive career has seen him specialize in global customer insights, strategic analytics, and market research. Renowned for his adept use of data and analytics, he fuels actionable insights and strategic choices within the pharmaceutical sector.

For more than five years, Arvind has been at the helm of Regeneron Pharmaceuticals' Commercial Insights & Analytics division, playing a pivotal role in the company's expansion and success. Prior to joining Regeneron, he held distinguished positions, including Head of Global Customer Insights at Sanofi and Senior Director at Johnson & Johnson.

As AI and Machine Learning gain momentum, the spotlight on data quality versus quantity intensifies. The interview sheds light on the pivotal role of data quality versus quantity in AI and ML. It emphasizes the challenge of persuading business leaders to prioritize quality over quantity, and the delicate balance between AI's effectiveness and transparency. Leveraging expertise in Commercial Insights & Analytics, the interview underscores the link between high-quality data and successful AI outcomes. It explores strategies for advocating data quality despite budget constraints, focusing on long-term benefits of enhanced AI performance and efficiency gains.

AIM: Why is there a growing talk about data quality in AI and Machine Learning projects? How does this relate to changes in technology and business? What role does data quality play in today’s world of Generative AI?