Ai-Ready Data: Common Use Cases and Considerations
Unfortunately, cleaning and transforming data to be compatible with machine learning and artificial intelligence processes is no small task. As a result, a 2024 Bloomberg Research Data survey of over 100 systematic investors found that data quality and interoperability are amongst the largest bottlenecks to their AI and systematic research efforts. Purpose-built and curated for rigorous investment research, Bloomberg’s research datasets provide end-to-end solutions to power your research workflows. Key features such as investable universe filtering, long point-in-time history, accurate timestamps, granular metadata and fast time series retrieval support sophisticated analysis, backtesting and signal generation.
Join us for a webinar on how Bloomberg's AI-ready Data and APIs are helping clients navigate a rapidly evolving innovation cycle with confidence.
Speakers
Andrew Fleet
Team Lead, Research Sales
Bloomberg Enterprise Data
Andrew Fleet leads the Research Data sales team at Bloomberg. He is responsible for leading and developing his team in one of the company's largest growth areas. Prior to this, he held various roles within Enterprise Data and Core Terminal Sales. Over the last 12 years, Andrew has worked across a broad variety of buy-side and sell-side customers, specializing in Bloomberg’s technology suite, Data Management suite and Research suite. Andrew received his BA in Finance and Accounting from the Lerner College of Business and Economics at the University of Delaware.
Michael Beal
Head of Enterprise Data Science Specialists
Bloomberg
Michael Beal is the head of Data Science for Bloomberg Enterprise Data, North America. His team is responsible for helping define Bloomberg Enterprise Data’s A.I. Strategy, publishing data-driven research and prototyping new products and solutions for quantitative and A.I. workflows. Prior to joining Bloomberg, Michael was the co-founder of J.P. Morgan’s Intelligent Solutions group, where he led the build of the industry’s first machine learning approach to Collateral and Counterparty Risk Optimization for OTC derivative products. After J.P. Morgan, Michael spent 8 years leveraging proprietary cloud technologies and Generative A.I. to systematically manage U.S. equity portfolios for family offices. Michael graduated from Harvard College with Honors in Economics and Harvard Business School with distinction.