Summary
This fact sheet presents Saama's Smart Data Quality (SDQ), which automates the data cleaning, review, and reconciliation processes for data managers in clinical trials. It outlines how SDQ utilizes advanced artificial intelligence (AI) models to manage the complexities of clinical trial data, enabling faster data review processes. The document details various features of SDQ, including automated prediction closing, bulk actions for approving or rejecting data quality checks, and an integrated rule builder for coding quality checks. It emphasizes the efficiency gains from using SDQ, such as reducing the time to review and approve queries from 27 minutes to just 3 minutes. The fact sheet also highlights the ability to track discrepancies, manage data quality tasks, and access a catalog of data quality rules for reuse across studies. Overall, the SDQ aims to enhance the productivity of data management teams by allowing them to focus on more complex queries while automating routine tasks.
