Accelerating Product Understanding with the Bloomberg Virtual Data Room (VDR) and Data GO
Using the VDR, clients can inspect a 3 year sample of the data product to answer a broad range of questions around the quality, coverage and shape of the data. VDR is hosted in Python Jupyter Notebooks, making it easy for users to validate the datasets and answer questions such as:
Coverage: Does this dataset cover my entire investment universe?
Quality: Are there any missing data points?
Consistency: Are there outliers that I need to adjust for?
Connectivity: Will it be easy to link the dataset with my existing data?
Ease: How easy is it to use this dataset for my firm’s specific use cases?
Tune in to hear from our head of enterprise data science specialists on how you can use VDR to quickly validate enterprise datasets and make a purchase decision.
Speakers
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.