GDELT and SEC EDGAR delivered live into your Snowflake account, so buy-side and quant teams build, join, and backtest signals in the environment where their models already run.
Clinical trial competitive intelligence is the practice of monitoring competitors’ trial activity – which programmes they run, at what phase, where, and how enrolment is progressing. Element Data provides the data foundation: the AACT build of ClinicalTrials.gov, live in your Snowflake account and joinable to your internal pipeline data.
Clinical trial competitive intelligence (CI) is the systematic analysis of publicly registered trial activity to understand a competitor’s R&D strategy. It answers questions like: which sponsors are active in a therapeutic area, what phase their programmes have reached, which sites and investigators they use, and how their enrolment timelines compare – all derived from the ClinicalTrials.gov registry.
None of that information is secret. Sponsors are required to register interventional trials, and the registry is public. The competitive advantage is not access – it is the speed and specificity with which your team can interrogate it.
Who is running trials in my therapeutic area, and at what phase?
How does a competitor’s pipeline compare to ours by indication?
Which sites and investigators are competitors using – and are we competing for the same ones?
How are enrolment timelines and eligibility criteria trending across comparable studies?
Who is running trials in my therapeutic area, and at what phase?
Where the comparison is honest. Commercial CI platforms such as Citeline and GlobalData add curation on top of registry data: normalised drug names, deal activity, analyst commentary. If you want that layer, buy it – we are not selling a replacement for it. What we sell is the underlying data in your environment, so your team is never locked out of the raw records and can answer questions no vendor anticipated.
Sponsors, indications, or intervention classes. This is analyst work, not data work, and it is where the quality of the whole exercise is determined.
Query AACT for all trials matching the set, with phase, status, start and completion dates, and enrolment.
The facilities tables turn a pipeline view into a site-competition view - which is often the more actionable one.
Put internal programmes next to competitor programmes in the same query. This step is the one a third-party tool structurally cannot do for you.
Because the dataset is maintained, yesterday's analysis re-runs against today's registry without anyone re-downloading anything.
Competitive-intelligence and business-development teams at pharmaceutical and biotech companies, portfolio strategists, and the life-sciences consultancies advising them. See Snowflake data for pharma teams for audience-specific framing, or pharma pipeline benchmarking for the adjacent use case.
From ClinicalTrials.gov, via the AACT database maintained by CTTI. Element Data delivers AACT live in Snowflake as the foundation for competitive-intelligence analysis. It is public registry data – the intelligence comes from how you query it, not from privileged access.
Those platforms package trial data into their own interface, add editorial curation, and charge for the combination. That has real value, and we would not pretend otherwise. What Element Data does is different in kind: we deliver the underlying data into your Snowflake account, so your team owns the raw records and can run any analysis – not only the reports someone else designed. Teams that want a finished product should buy a platform. Teams with analysts who want to ask their own questions usually prefer this.
Yes, and it is usually the point. Because the data lives in your Snowflake account, you can join competitor trial activity directly to your internal portfolio, commercial, and scientific datasets. A CI platform cannot do this without you exporting your confidential pipeline into it.
Being straight about the limits: registry data reflects what sponsors chose to register and when they updated it. Preclinical work is invisible. Some trials are registered late, and status fields go stale. Commercial intent, deal activity, and internal go/no-go decisions are not in there at all. Registry analysis is a strong signal for pipeline shape and site strategy – it is not a substitute for market intelligence.
The share is usually live in your account the same working day. A first therapeutic-area landscape – sponsors, phases, enrolment, sites – is typically a day or two of analyst work after that, because the hard part is defining your comparator set, not accessing the data.
No. If your analysts write SQL, they can work directly against the share. If your Snowflake environment needs setting up first, our parent company Liquid Technologies does that work – but it is genuinely optional, and most CI teams start without it.
Element Data is built by Liquid Technologies (Liqteq). We deploy frontier AI inside healthcare and regulated organisations’ own environments, cloud or on-premises, so they get frontier capability without their data ever leaving their control. When a project needs pipelines or models built around that data, the same team does it, inside the same boundary.
AI solutions provider and data engineering, delivered inside your environment.
AI-powered payer contract intelligence for IPAs, MSOs, and provider networks.
Video intelligence turning camera feeds into real-time operational alerts.
The AI operations layer for teams, deployed inside your own cloud.
We share AACT into your Snowflake account and walk your analysts through the tables that matter. No platform migration, no lock-in.
Tell us about your data requirements, and our team will Shortly Connect with you
