Smart Data Quality (SDQ)
Interactive Review Listings

Review pre-built or generative-AI–built data listings, drill into the records, raise queries, and assign tasks to teams and vendors — all inside SDQ.

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Advanced data review, without the spreadsheets

Interactive Review Listings (IRL) gives study teams one place to perform advanced data review. Review Saama’s pre-built listings or create custom listings with generative AI, drill from aggregated views down to individual records, raise queries, and assign review tasks to team members and vendors — without exporting data to spreadsheets. With enhanced aggregation, IRL supports grouping, aggregation, and pivoting as part of the listing workflow, so reviewers can spot trends and outliers across an entire study and still trace any signal back to the source record.

Why teams choose Interactive Review Listings

One place for all listings

Manual review and advanced review happen together, removing tool-switching and reconciliation.

Generative-AI listing creation

Describe the listing you need in plain language and a Co-Pilot drafts the underlying query.

Collaborate across teams and vendors

Assign review tasks, add threaded comments, and track ownership in-app.

Aggregate to see the signal

Row-level grouping, aggregation, and pivoting reveal patterns individual records hide.

One workspace

Listings, aggregation, queries, and tasks in one place

Pre-built and GenAI-built listings with drill-down and team assignment — no spreadsheet exports

How Interactive Review Listings works

Listings move through a controlled, permission-aware workflow from creation to review.
1

Start from a listing — or describe one

Open a pre-built listing or create a custom one by describing it to the Co-Pilot, which drafts the query logic for you.

2

Shape and validate

Build grouping and aggregation in the dry-run screen, then save and send the listing for testing before it reaches production.

3

Review, query, and assign

Reviewers drill from aggregated views to individual records, raise queries, and assign tasks to team members and vendors.

1
Start from a listing — or describe one
Open a pre-built listing or create a custom one by describing it to the Co-Pilot, which drafts the query logic for you.
2
Shape and validate
Build grouping and aggregation in the dry-run screen, then save and send the listing for testing before it reaches production.
3
Review, query, and assign
Reviewers drill from aggregated views to individual records, raise queries, and assign tasks to team members and vendors.

Aggregation with guardrails.

Only users permitted to create listings can build aggregations, reducing the risk of introducing unblinding bias while still giving reviewers the trend views they need.

Aggregation with guardrails. Only users permitted to create listings can build aggregations, reducing the risk of introducing unblinding bias while still giving reviewers the trend views they need.

Features

Pre-built and custom listings
Use Saama’s library or build study-specific listings on demand.
GenAI-assisted authoring
Plain-language requests are converted into listing logic.
Grouping, aggregation, and pivot
Create aggregated views and drill back down to source records.
Permission-controlled aggregation
Aggregation is limited to authorized listing creators.
Task assignment
Distribute review work to teams and vendors with tracking.
Threaded comments
Collaborate on findings directly on the listing.

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