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Hamit Hamutcu
Hamit Hamutcu

Hamit Hamutcu

Co-Founder
Initiative for Analytics and Data Science Standards, Elements
Bio

Hamit Hamutcu is a senior advisor at the Institute for Experiential AI at Northeastern University where he leads strategy, marketing, and partnership efforts to develop programs that contribute to and work with the global AI ecosystem. He brings 30 years of industry and consulting experience in the areas of analytics and data-driven business strategy. Hamit is the co-founder of the Initiative for Analytics and Data Science Standards, a nonprofit initiative that aims to develop industry standards for the knowledge and skills required in data science roles and data and AI literacy. He is also a co-founder of Elements, a data skills assessment platform to support hiring and upskilling at organizations. Previously, Hamit was a managing partner for Peppers & Rogers Group in Stamford, Connecticut, where he headed the Global Analytics Group and oversaw the growth of the analytics practice. He helped his clients develop best-practice data and analytics organizations, build data infrastructure, and deploy models to support business goals. He launched several offices for the company globally, including Singapore, South Africa, Istanbul, Middle East and Germany. Earlier in his career, Hamit held several marketing analytics and technology positions at FedEx in Memphis, Tennessee, where he led IT and business teams to leverage enormous amounts of company data generated to serve its customers better. Hamit is a frequent speaker, writer, and board member at various startups and nonprofit organizations. He earned his Bachelor of Science in electronics engineering at Boğaziçi University in Istanbul and received his MBA from the University of Florida.


Building Data and AI Literacies at Scale: Your Organizational Superpower

This talk will cover the importance of building essential skills across the organization to maximize the impact of data and AI initiatives and dramatically improve the quality of decision-making. It will also cover frameworks for defining fundamental data and AI literacy skills, methods to develop effective upskilling programs, and tools to assess skills across a wide variety of disciplines and knowledge domains.