For most of the past decade, alternative data conferences have asked the same underlying question in slightly different language: does this stuff actually produce alpha? The answer, settled some time ago for institutional buyers willing to do the work, has shifted the conversation. The questions on the agenda for the London edition of the Eagle Alpha Alternative Data Conference, hosted by A-Team Group, now in its eighth year, are notably different in character. They are operational, not evidential. They concern ownership, accountability, scale, and the integration of AI agents into workflows that until recently were defined by human judgement and bespoke pipelines.
The opening Leaders Panel sets the tone. Rather than rehashing the case for alternative data, it asks what is durable and what is hype – particularly across AI agents, textual data, private and industry datasets, and the push beyond equities. Where, in other words, are firms actually reallocating spend in 2026, and which capabilities once treated as differentiating are now being quietly deprioritised? Renato Guerrieri of Downing, Timothée Consigny of H2O Asset Management, Matthew Bell of Man Group, and Decision Desk HQ’s Rob Schack join moderator Julia Meigh for that opening exchange – a panel weighted toward practitioners who have moved past evaluation and into deployment.That maturity framing runs through the rest of the day. A panel on building and scaling the alternative data function, with Alba Seoane (Point72), Caio Natividade (Deutsche Bank), Tim Drye (Curious Blue Fish) and author Alexander Denev, takes up the questions that come after a programme works: how teams should be structured, who owns a dataset once it is in production, what happens when a feed drifts or breaks, and what the minimum viable alternative data stack actually looks like. These are not the questions a firm asks when it is still proving the thesis. They are the questions it asks when alternative data has become operationally critical and needs the governance scaffolding to match.
A separate session on intraday data and signal decay, moderated by CFM’s Mark Fleming Williams, presses on a related point: higher frequency is not free. Intraday alternative data has demonstrated value for high-frequency and systematic strategies, but the panel – including Davide Alfano of Kaleidoscope Capital and Joe Gits of BridgeWise and Context Analytics – will examine where intraday signals genuinely change discretionary decisions, and where the trade-off between frequency and historical depth makes that case harder. The framing implicitly rejects the assumption that more timestamps mean more insight.
The AI thread is woven across the day rather than confined to a single session. A fireside chat on web scraping that does not break, with ex-Citadel’s Nico Smuts and Man Group’s Jahmal Nicholson, takes on the unglamorous reality that building scrapers is easy and maintaining them is the hard part – self-healing pipelines, hallucination control, change detection, and how to make scraping accessible without losing quality controls. It is a session pitched squarely at the engineering reality of running AI-enabled discovery at scale, rather than the conceptual case for it.
Two further sessions push at the boundaries of what alternative data now covers. A panel on macro, markets and risk – with Mario Dell’Era of EnBw, Petr Merkuryev of Medusa Investment Partners, and Lombard Odier’s Filippo Pallotti – addresses the awkward fact that traditional quant signals have been tested by interest rate cycles, inflation, and geopolitical disruption in ways that demand recalibration rather than reassurance. A closing fireside on prediction markets, with Crossbridge Capital’s Manish Singh interviewed by Man Group’s Matthew Bell, takes on the harder question of whether political and prediction-market data is a credible institutional input, where the regulatory treatment diverges between the US and Europe, and where the MNPI and inference-risk boundaries actually sit.The European regulatory lens is what most clearly differentiates the London edition from its New York counterparts. GDPR, cross-jurisdictional data exposure, and the provenance questions that sit underneath any web-sourced or consumer dataset are not peripheral concerns for European buyers; they are gating ones. The agenda treats them as such, rather than as a compliance afterthought.
Alongside the formal sessions are the gated 1:1 meetings between buyers and vendors – a format that has become the principal commercial reason vendors attend, and the principal discovery channel for buyers looking past the same names that appear at every event. The two Fresh Features slots, mid-morning and mid-afternoon, complement those meetings: a sequence of new-to-market and established data vendors each given three minutes to describe the problem they solve and the value they add. The format is fast, high-energy, and designed to give the audience maximum exposure to new ideas in minimum time. More than half of the data providers in London this year are new to the usual European circuit – a deliberate weighting toward unfamiliar names.
The picture across the programme is not of a market searching for legitimacy. It is of one negotiating the harder problems that follow legitimacy: governance, accountability, infrastructure, and the integration of AI into workflows that were never designed for it. For data managers, quant researchers, and heads of trading desks now responsible for production-grade alternative data programmes, the questions on this agenda are the ones already on their desks.
The Eagle Alpha Alternative Data Conference, London, hosted by A-Team Group, takes place on 19 May 2026 at America Square Conference Centre. Full agenda and registration: a-teaminsight.com/events/alternative-data-conference-london
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