ClinicalTrials.gov contains over 500,000 trial records. Getting that data into a format your team can actually query takes weeks.
That gap between public availability and production readiness is where most clinical trial data projects stall.
AACT changes the equation. It is ClinicalTrials.gov data normalized into structured tables, with consistent sponsor fields, clean trial status history, and a schema designed for enterprise analysis. Teams at Roche, Amgen, AstraZeneca, and Sumitovant pull AACT from Snowflake Marketplace because it lands ready to join internal tables. No ingestion pipeline. No cleanup sprint before the analysis can start.
The Problem with Raw ClinicalTrials.gov Data
ClinicalTrials.gov is a public resource. Anyone can download the data. But public does not mean usable.
Raw exports from ClinicalTrials.gov arrive with inconsistent sponsor names. Pfizer appears as “Pfizer”, “Pfizer Inc.”, “Pfizer, Inc”, and a dozen other variations. Trial status fields contain free text that needs mapping. Intervention descriptions mix drugs, devices, and procedures without clean categorization.
A data engineer tasked with building a clinical trial intelligence layer does not start with analysis. They start with normalization. Weeks of it.
That normalization work is invisible to the business. The request was “show me competitor trial activity in oncology.” The reality is three sprints of schema mapping before anyone sees a dashboard.
What AACT Actually Delivers
AACT is the Aggregate Analysis of ClinicalTrials.gov dataset, maintained by the Clinical Trials Transformation Initiative. It takes the raw ClinicalTrials.gov XML and transforms it into relational tables with consistent field definitions.
Sponsor names are normalized. Trial phases map to standard categories. Status history is queryable. The data arrives structured for the questions pharma and biotech teams actually ask.
Element Data delivers AACT directly inside Snowflake. No file downloads. No API integrations. No pipeline to build or maintain. The data lands in your Snowflake environment as a live share, refreshed regularly, ready to query on day one.
How Pharma Teams Use AACT for Competitive Intelligence
The most common use case is pipeline benchmarking. A business development team preparing for an acquisition needs to know what trials the target company is running, what phases those trials are in, and how that compares to competitors in the same therapeutic area.
With normalized AACT data, that analysis takes hours instead of weeks.
Other common use cases include:
- Therapeutic area mapping: Which sponsors are most active in a specific indication? Where is trial volume increasing or declining?
- Trial phase analysis: How many Phase 3 trials are active in oncology? Which sponsors are advancing assets from Phase 2?
- Site and geography analysis: Where are trials being conducted? Which countries show increasing enrollment activity?
- Regulatory intelligence: Which trials have been terminated, suspended, or withdrawn? What patterns emerge across sponsors?
These questions require clean, joinable data. Raw ClinicalTrials.gov exports do not support them without significant transformation.
Read more: AACT clinical trial dataset or AACT on Snowflake Marketplace
Why Snowflake Marketplace Changes the Procurement Model
Traditional external data procurement follows a painful pattern. Identify the dataset. Negotiate a contract. Wait for delivery. Build an ingestion pipeline. Discover schema issues. Fix them. Finally, start analysis.
Snowflake Marketplace collapses that timeline.
AACT from Element Data is available as a data share. You request access, the data appears in your Snowflake environment, and your team can start querying immediately. No file transfers. No ETL jobs. No pipeline maintenance.
When the data refreshes, your view updates automatically. No manual re-ingestion. No schema drift to debug.
This is why Snowflake sales engineers demo Element Data to their life sciences prospects. It shows what Snowflake-native external data actually looks like in practice.
The Real Cost of Building Your Own Pipeline
A senior data engineer costs the business $150,000 or more per year fully loaded. If that engineer spends three weeks building and validating a ClinicalTrials.gov ingestion pipeline, the cost is roughly $9,000 in direct labor. That does not include the opportunity cost of the analysis that waited, or the ongoing maintenance when ClinicalTrials.gov changes its export format.
Most teams that calculate the true cost of self-service clinical trial data normalization find it exceeds the cost of a production-ready dataset by a wide margin.
The value of AACT on Snowflake is not just access. It is access without the normalization tax. The hidden cost of external data normalization
Who Uses AACT from Element Data
The request list reads like a directory of life sciences data teams. Roche, Amgen, AstraZeneca, Sumitovant Biopharma. Consultancies like ZS, Deloitte, and KPMG evaluating data options for pharma clients. Snowflake sales engineers preparing demos for healthcare prospects.
These are not academic users browsing trial records. They are commercial teams with pipeline benchmarking deadlines and competitive intelligence deliverables.
They chose AACT on Snowflake because it meets them where their data already lives, normalized and ready to join.
Frequently Asked Questions
How do I access ClinicalTrials.gov data in Snowflake?
Element Data provides the AACT dataset as a live data share on Snowflake Marketplace. You request access through the Marketplace listing, and the normalized clinical trial tables appear directly in your Snowflake environment. No downloads, no pipeline, no ETL required.
What is the difference between AACT and raw ClinicalTrials.gov data?
Raw ClinicalTrials.gov data is XML with inconsistent fields, free-text entries, and no normalization. AACT transforms that into relational tables with standardized sponsor names, mapped trial phases, and queryable status history. AACT is production-ready. Raw exports require weeks of engineering before analysis can start.
How often is AACT data refreshed on Snowflake Marketplace?
Element Data refreshes the AACT dataset regularly to reflect updates from ClinicalTrials.gov. Because it is delivered as a Snowflake data share, your view updates automatically with each refresh. No manual re-ingestion needed.
Can I join AACT data to my internal clinical or commercial data?
Yes. AACT lands in your Snowflake environment as standard tables. You can join it to internal datasets using sponsor names, therapeutic areas, NCT identifiers, or any other field your analysis requires.
Is AACT data suitable for competitive intelligence and pipeline benchmarking?
AACT is the most common dataset for pharma competitive intelligence workflows. Teams use it to track competitor trial activity, benchmark pipeline progress by phase, and analyze therapeutic area trends. The normalized structure supports these use cases without custom transformation.
Ready to skip the pipeline work? Request access to AACT on the Snowflake Marketplace and give your research team a clinical trial layer they can query today.
