Jonathan Chin: Building a Tier 1 Dataset

Jordan Hauer: Taking Stock of the Alternative Data Industry

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.

Jonathan Chin: Building a Tier 1 Dataset

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.

Sheraz Bhatti: Land and Expand

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.

Julia Meigh: Macro, Mayhem & (Prediction) Markets

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.

Florence Broderick: The Cutting Edge of Data Sales

In this episode of Selling Signals, we’re joined by Florence Broderick, CRO at General Index, where she is helping scale a commodities data business. Florence has worked across both software and data companies, which gives her a rare perspective on how different those worlds are when it comes to selling, hiring and supporting customers. We discuss why SaaS is still ahead of DaaS in go-to-market maturity and how AI is changing sales teams in practice. Florence shares what is actually working in prospecting today, how data vendors should think about AI use in customer contracts, and why in-person events may become even more important as more of the sales process becomes automated. This episode is essential listening for anyone building a data business, modernising a GTM team, or trying to understand what data companies still need to learn from SaaS.