『Selling Signals - the Data Monetisation Podcast』のカバーアート

Selling Signals - the Data Monetisation Podcast

Selling Signals - the Data Monetisation Podcast

著者: James Worthington and Eric Evans
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Selling Signals is the podcast for anyone building, selling, or buying data, with a focus on commercialising data in the investor ecosystem.

Each episode brings together industry insiders to share real, first-hand experience from the front lines of data sales. We unpack what actually works when turning raw data into revenue, whilst exploring other data buying silos to break down the walls between them.

Selling Signals delivers practical lessons to help data teams sell better and build stronger, more commercial data businesses.

© 2026 Selling Signals - the Data Monetisation Podcast
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  • Freeman Lewin: Agentic Data Sourcing
    2026/09/03

    What happens when the person sourcing your data is no longer a person?

    In this episode of Selling Signals, we’re joined by Freeman Lewin, co-founder of Brickroad. Freeman began his career in private equity and later worked as an attorney negotiating data and AI contracts.

    Brickroad uses agents to discover new data suppliers and automate what Freeman calls the “messy middle” of procurement. Its agents search thousands of sources and infer key details about a dataset. They then surface the suppliers most worth investigating.

    We also discuss how vendors should think about monetising their data with both frontier AI labs and smaller specialist AI teams.

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    49 分
  • Tjeerd van Cappelle: Literally Selling Signals
    2026/08/20

    In this episode of Selling Signals, we’re joined by Tjeerd van Cappelle, founder of aiLiftOff. Tjeerd spent more than 16 years on the buy side as a quantitative investor before building a company that forecasts revenues and earnings for stocks globally.

    We discuss what quants look for in data vendors and why a backtest is only the start of the conversation. We also cover the challenge of selling model output, where trust can matter as much as accuracy.

    The episode ends with a fascinating discussion on artificial stock markets and what they could mean for AI-driven investment research.

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    43 分
  • Matt Ober: What the Buy Side Really Wants
    2026/08/06

    Matt Ober was looking for unconventional datasets before alternative data became an established industry. He has led data strategy at WorldQuant, served as Chief Data Scientist at Third Point and now works with data providers through Social Leverage and Initial Data Offering. In this episode, he explains how the market operates from the fund's side.

    The buying process differs sharply between quant and fundamental investors. A quant team may begin by examining coverage, history, frequency, delivery and point-in-time integrity. A fundamental analyst usually begins with a company and a specific investment question.

    We discuss what vendors should provide during a trial, why external research cannot replace a fund's own testing and how poor documentation can waste a limited evaluation window. Matt also gives his views on pricing, renewals, distribution and the importance of maintaining good relationships with buyers.

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    54 分
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