This exciting new feature in LSAC v3.0 can help you quickly resolve data issues in your source systems and files.
By Haritha Chitalia, Product Manager
Good data helps clinical research professionals make better decisions, mitigate risk, and measure the success of plans and goals more effectively. In order to have quality metrics, it is important to ingest good data and measure data quality on an ongoing basis.
We are excited to introduce you to Saama’s new, proprietary Data Quality (DQ) Engine, one of the exciting features in the latest update to Saama’s Life Science Analytics Cloud (LSAC), set to release later this month. Designed to be highly visual and customizable, the DQ Engine comes complete with:
- A Data Quality Dashboard
- Daily Data Quality Verification
- Configurable Data Rules
The DQ Engine will run on top of your source system data tables, parsing through each row and attaching data quality flags when your data rules are violated to show you what needs your attention. The engine is powered with configurable data rules applied to specified tables and columns and when a rule is violated, the row is set with a dq flag.
Here is a list of DQ rules and flags currently available in the DQ Engine:
|Mandatory||Verifies that an entered value is not null. |
Example: If a Subject Enrollment Date is null, the DQ Engine will flag the row with an Error or Warning.
|Data Conformity||Verifies that an entered value meets expected data requirements. |
Example: If Site Open Date is set before Site Closed Date, the DQ Engine will flag the row with an Error or Warning.
|Referential Integrity||Verifies that an entered value can be referenced by other relational tables. |
Example: If Visit ID from Subject Visit doesn’t match the Visit ID from Form Table, the DQ Engine will flag the row with an Orphan.
|Uniqueness Conformity||Verifies that entered value is unique per row in the source table. |
Example: If a Visit ID or Form ID is not unique, the DQ Engine will flag the row with an Error or Warning
|E: Error||Data row is invalid|
|O: Orphan||Breach of referential integrity|
|W: Warning||Data row is flagged with a warning|
|G: Good||Data row is valid and passed all DQ checks|
Saama understands that users of the DQ Engine will have unique data needs. Our Customer Success team is equipped to work with your data and enable the appropriate data rules and flags. Additionally, you will have the ability to easily review your data quality through our Data Quality Dashboard.
The DQ Dashboard displays your data quality metrics across all of your source systems for each study. The dashboard consists of high-level data summary cards that show how many records have been flagged as good or bad, along with a timestamp of when the DQ Engine last ran. Additionally, you can get an overview of which records failed on which rules, and drill into granular details of your data sets to verify the quality of your data.
At Saama, we believe in empowering clinical research professionals with as much information as possible. We’re confident that our new DQ Engine will drive better data quality and help resolve data issues in your source systems and files. If you have any questions or want more information, contact us.
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