Speaker "Daniel Shenfeld" Details Back



Product/Data Fit: The Lean Startup Method and AI Products


Introducing machine learning and AI components into a product leads to strategic product development challenges, arising from the inherent uncertainty involved in data and machine learning models. How to quantify and measure the value of data? How to clearly define data science deliverables? When should an unsuccessful modeling effort lead to a product pivot? This talk aims to provide a unifying framework to tackle these challenges. I will introduce the concept of product/data fit and explain how it relates to product/market fit. I will describe how product considerations determine prediction value and guide the choice of modeling metrics, and how the lean startup build-measure-learn methodology can be adapted to accelerate both product/market fit and product/data fit. I will discuss case studies from healthcare and other verticals, highlighting guiding principles and common pitfalls and demonstrating how this approach can shorten time to market and help achieve financial business goals of AI driven products.


Daniel is the founder and principal of Manganese Solutions, an AI strategy consultancy that helps companies develop and execute market-driven AI solutions. Daniel is a seasoned data science executive who has led product-oriented teams at multiple companies, ranging from early stage startups to national organizations, including responsibility for multimillion dollar strategic initiatives. Daniel holds a PhD in mathematics from Princeton University. He is an expert in machine learning and AI, with deep domain expertise in healthcare and biotech, including publications in top journals.