Daniel is the founder and principal of Manganese Solutions, an AI strategy consultancy focused on product-oriented machine learning and AI strategy and execution. He is a data science executive with over 8 years of experience leading 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 the healthcare and biotech spaces, including publications in prestigious academic journals.
Product/Data Fit: The Lean Startup Method and AI Products
by Founding Principal, Manganese
Machine learning and AI add uncertainty to product development because their level of performance can’t be guaranteed in advance. This uncertainty raises new strategic challenges in product development: 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.