Angana Jacob - Data as the True Competitive Moat (S7E26)

Today, I am speaking with Angana Jacob, Head of the Research Data group within the Enterprise Data business at Bloomberg. We talk about Angana’s career path through quantitative research and data platforms, and how the industry has evolved from a world dominated by bespoke models and backtests to one where many models have become increasingly commoditized. A central theme of our conversation is the idea that while models are easier than ever to replicate, data — how it’s sourced, cleaned, standardized, linked, and delivered — has become the true competitive moat. We discuss what it means to “do data correctly,” how Bloomberg decides which datasets to build or sunset, how modern quants think about their data pipelines and tech stacks, and why aligning research data with production and back-office systems matters more than most people realize. Throughout the episode, we focus on Bloomberg’s goal of shortening a client’s time to alpha, and what that looks like in practice. At its core, this episode is about a simple but powerful idea: when everyone has access to similar models, durable edge increasingly comes from the data beneath them. Please enjoy my episode with Angana Jacob. 0:53 Guest introduction and career journey 5:13 Evolution of quant research and the importance of data 12:28 Bloomberg's approach to data and shortening time to alpha 16:17 Building vs. buying data sets and their life cycle at Bloomberg 20:37 Building a company geographic exposure data set 23:21 Evolution of quant data pipelines and alignment across offices 29:13 Modern tech stack and challenges in data platform development 38:15 Balancing raw data with value-added modeling 42:02 Surprising trends in data usage and strategy convergence 46:28 Future trends in data needs and the next frontier 49:03 Common mistakes and lessons in data wrangling 51:25 Advice for quants on leveraging data 54:08 Guest's current obsessions outside of work

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