Accelerate Your Data Review Processes - Today

Using the industry’s most advanced artificial intelligence (AI) models, Smart Data Quality (SDQ) gives study teams the power to manage the high volume and variety of today’s clinical trial data.

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Experience the power of data review automation

Saama is the undisputed leader in AI for life sciences, with over 90+ models trained on over 300 million data points. SDQ automates data review processes, reducing query generation times from 30 minutes to just 3 minutes per query.


Smart Data Quality (SDQ)
Automate data
review processes

Use AI modes to automatically identify discrepancies and generate queries. Eliminate manual reviews, reduce errors, and minimize trial delays.

Smart Data Quality (SDQ)
Accelerate time to
database lock

SDQ helps keep data clean in real-time, reducing the time from last patient last visit (LPLV) to database lock (DBL).

Smart Data Quality (SDQ)
Reduce time to
issue a query

With SDQ, data discrepancies are automatically identified as they are captured, reducing time to issue a query from over 25 days to under 2 days.

Smart Data Quality (SDQ)
Scalable across
your portfolio

Built on a cloud-based architecture backed by AWS, SDQ is scalable across your portfolio and is proven on large, global mega trials.

Manual Data
Review Process





27 min

To manually review and write a query.





Smart Data Quality
Automated Data
Review Process





3 min

To review and approve AI generated query.





How AI is Applied

Saama has 90+ AI models trained for life sciences on over 300 million data points. These models are deployed within SDQ to identify data discrepancies, automatically.

See how Saama reduces query times from 30 minutes to 3 minutes.

Smart Data Quality (SDQ)
Data Hub

“(SDQ) saved us an entire month. It really has had a significant impact on the first-pass quality of our clinical data and the speed through which we can move things along and make decisions.”

Head of Data Monitoring
Top 3 Global Pharmaceutical Company


Accelerate your trials while maintaining clean, high-quality data.

Smart Data Quality (SDQ)
AI assisted data reviews

Advanced AI models automatically identify data discrepancies that would typically only be caught by manual data reviews.

Smart Data Quality (SDQ)
Add interactive review listings

Review data listings manually and perform advanced data review in a single location. Users can review pre-built listings or create custom listings using generative AI, and can even assign tasks to team members and vendors.

Rule Builder
Integrated rule builder

SDQ’s self-service rule builder allows users to code data quality (DQ) checks directly within SDQ and reuse them across studies. DQ checks can be coded once and used across multiple source systems and work in conjunction with AI-driven checks.

Catalog of DQ rules

DQ rules can be created and saved as part of a catalog for reuse. Users can apply these rules at the study level and see how they were applied in previous studies.

Smart Data Quality (SDQ)
Data review dashboard

Complete data review from a single location. See summary of DQ and AI-driven checks, view source data, and pre-generated query text – all on the same screen.

Smart Data Quality (SDQ)
Pre-generated query text

For each data discrepancy, SDQ pre-generates a query response. Easily review and edit query text before sending it back to the source system.

Query approval/rejection

Review each AI-based or rules-based DQ check, along with the source data, to quickly approve or reject queries. Users can edit the pre-generated query text before approving it and sending it back to the source system.

View query responses and details

Review query responses and details directly within SDQ when connected to standard EDC systems with an API (e.g., Inform, Veeva, Medidata). View the full query trail and conduct full, end-to-end query workflows.

Automated prediction closing

If SDQ identifies a data discrepancy – but the issue is fixed in the source system before the data manager reviews it – SDQ automatically closes the auto-generated predictions, reducing duplicate queries.

Smart Data Quality (SDQ)
Bulk actions

Approve or reject data quality checks in bulk with a few clicks, saving thousands of hours for data managers.

Smart Data Quality (SDQ)
Deep link to eCRF

Go directly to the source eCRF with a single click from the prediction page, improving efficiency and streamlining workflows.

Smart Data Quality (SDQ)
AI model training

SDQ’s AI models improve over time and become more accurate with use, with regular model retraining to incorporate user inputs.

Third Party
Third-party vendor data reconciliation

Users can check for missing or incorrect data from third-party vendors, and track responses manually within SDQ. Once the vendor fixes the data, it’s automatically updated within SDQ.

View data quality checks and data ingestion status

Users can view which data quality checks have been fired and which have failed, as well as the data ingestion status from Data Hub.

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