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.

In this episode of Selling Signals, we’re joined by Kai Searle, Senior Consultant at Engage PSG. Kai helps data, research and AI businesses build go-to-market teams across the US, UK and EMEA.

We discuss why hiring for data companies differs from hiring for SaaS. Kai explains what separates elite data sellers from average ones, and how candidates can assess whether an opportunity is as strong as the hiring team says it is.

This episode is for founders building commercial teams and for candidates navigating the data sales market.

In this episode of Selling Signals, we’re joined by Nick Derewlany, a data monetisation leader who has worked across GlobalData and Trustpilot building new data products inside larger organisations.

Nick brings a practical view on how corporates should think about commercialising existing data assets. We talk about why “we have lots of data” is not the same as “we have a data business”, and what companies should prove before hiring a team or putting large revenue targets in front of the board.

The conversation also covers the path from 0 to 1, the importance of repeatable demand, and why $1m ARR across several customers is a very different signal from one large bespoke deal.

This episode is for anyone building, buying or selling data products, particularly inside companies where data monetisation sits alongside a much larger core business.

In this episode, we’re joined by Brad Preston, founder of Beagleworks. Brad spent two decades on the buyside in South Africa before moving into data and research, giving him a rare perspective on how alternative data gets used outside the US and Europe.

We discuss what makes emerging markets different for data providers, why pricing needs to reflect liquidity and stock coverage, and how Brad thinks about the line between raw data and research. A highlight of the conversation is Brad walking through how he would evaluate a hypothetical South African consumer receipt dataset, from the economics of the market to the questions it could help investors answer.

This episode is essential listening for anyone selling alternative data into smaller or less mature markets, or trying to understand what buyside users really need before a dataset becomes useful.

In this episode of Selling Signals, we’re joined by Jordan Hauer, Founder and CEO of Amass Insights. Through Amass, Jordan helps investment firms discover, evaluate, and source datasets while also helping data providers better understand and monetise their assets.

Having spent more than a decade at the centre of the market, Jordan has a unique perspective on how alternative data has evolved, from the industry’s early days of scarce and highly differentiated datasets to today’s world of abundant data and rapidly advancing AI tools.

We discuss which datasets are seeing the strongest demand in 2026, how funds evaluate new data sources, and why sales cycles remain stubbornly long despite significant improvements in the market. Jordan also shares his views on trials, pricing, backtesting, and the common mistakes data providers make when trying to sell to investment firms.

Whether you’re a data provider, investor, or anyone building in the data economy, this episode offers a valuable look at where the industry is heading next.

In this episode of Selling Signals, we’re joined by Jonathan Chin, co-founder of Facteus and author of Data-Minded. Jonathan brings a founder’s view on what it takes to build a transaction data provider trusted by institutional investors.

We talk about why data businesses do not behave like SaaS companies. Data often sells optionality rather than a fixed outcome, which changes the way buyers evaluate products. Jonathan explains where SaaS playbooks break down, what still carries across and why data quality issues are different from software bugs.

We also discuss aggregation, alpha decay and the role of AI in the future of alternative data. A big part of the conversation focuses on whether alternative datasets could become part of future model training rather than simply being queried through tools or MCP servers.

In this episode of Selling Signals, we’re joined by Sheraz Bhatti, who led Customer Success at Similarweb and now does the same at Quartr.

We discuss what Customer Success means in a data business and why it differs from the standard SaaS playbook. Sheraz explains what good adoption looks like, why usage metrics can be misleading, and how data vendors can tell whether their product is making it into real investment decisions.

We also cover churn risk, internal champions and what “land and expand” looks like when the product being sold is data.

In this episode of Selling Signals, we’re joined by Julia Meigh, who has spent years helping investors decide what data to buy, with a particular focus on macro and ESG. Few people have had a closer view of how macro investors think about new datasets or how providers break into the market generally.

We discuss what macro funds actually need from data and where vendors still have room to win. Julia also shares her view on geopolitical uncertainty and on prediction markets, both of which are playing a bigger role in macro investing.

This episode is essential listening for anyone building data products for macro investors or trying to understand where that market is heading.