The alternative data industry has a conversion problem. Vendors offer trials expecting them to sell themselves. Buy-side firms accept trials with genuine intent but competing priorities. The result is a gap between a promising evaluation and a signed contract where deals routinely die, often for reasons that have nothing to do with the quality of the data.
A panel at the recent A-Team/Eagle Alpha Alternative Data Conference in New York brought together Carrie Anton, Director of Data and Research Management at Jain Global; Don D’Amico, Founder and CEO of Glacier Network; and Mark Fleming Williams, Head of Data Sourcing at CFM, to examine what actually happens during trials, what kills deals, and what makes conversions work. The session was moderated by Eric Duncan, Head of Business Development at Databento.
Trials are not what vendors think they are
The discussion made clear that a fundamental misalignment exists between how vendors and buyers understand the purpose and mechanics of a trial. From the vendor side, a trial is often treated as a distribution exercise. Reduce the price to zero, hand over the data, and check back before the clock runs out. For buyers, it is a structured internal research project competing for time, attention, and resources against every other priority on the desk.At a quantitative fund, for example, the process is sequential and resource-intensive. A data scientist first examines the dataset for structural integrity, consistency, and point-in-time accuracy, raising questions about outliers and anomalies. Only once that initial quality assessment is complete – typically after a month – does the dataset pass to a quant researcher for alpha testing, a process that may take another month or two. Legal and regulatory teams engage in parallel or towards the end. The entire workflow can comfortably consume three months, and that assumes no interruptions.
For a multi-strategy fund managing a global investment team, the timeline pressure is even more acute. And paradoxically, 90 days is often too short rather than too generous. Earnings seasons, staffing gaps, conferences, and shifting internal priorities can force a trial to pause mid-evaluation. The request for extensions is not, as vendors sometimes suspect, an attempt to extract free access. It reflects the operational reality of a team balancing multiple concurrent evaluations with limited bandwidth.
One of the most common failure points, the panel noted, is the absence of a structured check-in cadence. A vendor that hands over data on day one and returns on day 89 asking for a verdict is setting the trial up to fail. Regular engagement at defined intervals – day 15, day 45 – builds a shared understanding of whether the evaluation is progressing, stalled, or needs course correction.
What kills deals
An audience poll during the session confirmed what the panelists expected: insufficient alpha signal is the most commonly cited reason for not licensing a dataset after trial. But the discussion revealed that the factors which actually prevent conversion are often more structural than analytical.
Commercial model rigidity emerged as a persistent deal-breaker. Funds that operate as a consolidated global resource – where a portfolio manager in New York and an analyst in Hong Kong or London need access to the same data, for example – frequently encounter licensing models built around regional seats or per-location fees. Adding an incremental licensing cost for an additional region can stall a negotiation that was otherwise progressing well, even when both sides agree the data has value.
Pricing structure matters as much as price level. Step-up models, where the cost increases gradually as the fund’s usage and confidence grow, were identified as significantly easier to get across the line internally, particularly when senior management approval is required above a cost threshold. A proof-of-concept pricing structure with defined escalation points and exit options gives the buyer a way to manage risk while giving the vendor a path to full commercial value.
Trust and transparency were flagged as make-or-break factors from the earliest stage. A dataset that arrives looking materially different from what was described in the sales process can end a trial immediately and damage the vendor’s reputation for future opportunities. The panel framed the trial as a test of the partnership, not just the product. Responsiveness to questions, honesty about data limitations, and willingness to explain methodology openly all contribute to whether the relationship survives evaluation.
Compliance: the growing friction layer
While alpha and commercial terms dominate most trial post-mortems, compliance is an increasingly significant and complex dimension of the licensing decision. Panelists estimated that compliance concerns terminate roughly 10 percent of data onboarding processes, a figure that may rise as new regulatory requirements and AI-related complications take hold.
Several emerging compliance themes surfaced during the discussion. The convergence of data and research is blurring traditional sourcing categories, and some buy-side firms are now combining their data and research due diligence questionnaires into a single, more comprehensive process. This reflects a practical reality: datasets that were once purely quantitative are increasingly incorporating human-sourced inputs – expert panels, manual gap-filling, qualitative overlays – that trigger different compliance obligations. The panel noted that these hybrid methodologies are not always disclosed upfront, and discovering them mid-diligence is a red flag that can derail or significantly slow the process.
AI integration presents a distinct compliance challenge. Vendors adding AI-powered features to existing platforms – analytics tools that now incorporate generative AI, for instance – may regard this as a product enhancement. For compliance teams, it constitutes a material change that should trigger re-diligence and updated documentation. Questions about what happens to user prompts, whether they are stored, analysed, or used to develop other products, are now part of the compliance conversation, though market-standard terms for addressing them remain elusive.
The discussion also highlighted the European regulatory dimension. DORA (the Digital Operational Resilience Act) imposes more stringent requirements on data providers serving European financial institutions than many US-based vendors expect, creating a potential compliance surprise for vendors expanding into European markets.
Derived data rights and model-training permissions represent another area of growing but unresolved complexity. The ability to create derived products from licensed data, feed data into proprietary models, and the deletion obligations that attach to trial data are questions that ideally should be addressed in trial agreements upfront. In practice, the panel observed, neither side tends to think about these issues carefully enough during the trial phase – and resolving them after the fact is considerably harder.
What makes trials convert
The panel converged on a practical set of conditions that distinguish trials likely to convert from those destined to expire.
Timing and urgency ranked highest. A trial initiated because a team has an immediate, specific use case is far more likely to progress than one accepted opportunistically. When the data arrives at the right moment for the right research question, internal prioritisation follows naturally.
Vendor engagement throughout the process – not just at the beginning and end – signals a partnership orientation that the buy side values. Rapid, substantive responses to technical questions during the evaluation period build confidence and momentum.
Pre-trial alignment on commercial terms, compliance expectations, and timeline was consistently identified as the single most effective way to prevent late-stage deal collapse. Discussing licensing structure, regional access, pricing models, and key compliance requirements before the data is delivered means that a successful analytical evaluation can move directly to conversion rather than stalling in a protracted negotiation.
Finally, documentation quality matters more than vendors may appreciate. Complete, accurate due diligence questionnaires that disclose methodology, data sources, human inputs, and AI usage upfront accelerate the compliance process and reduce the risk of surprises that erode trust and consume time.
The message from the buy side was clear: the vendors most likely to convert trials are those who treat the trial period as a structured, collaborative process rather than a passive waiting game, and who arrive prepared for the commercial and compliance conversations that follow a successful evaluation.
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