Embedded Analytics Delivery Gate (Delivery)

Embedded Analytics Delivery Gate (Delivery)

1. 01

   Confirm the client's data source connectors are live and credentialed

   Verify that each connector, such as Snowflake, BigQuery, or PostgreSQL, has valid credentials and that sample queries return within acceptable latency. This prevents downstream dashboard failures.

2. 02

   Map multi-tenant security rules to client user roles

   Define row-level and object-level permissions so each end user sees only their own data. Misconfigured tenancy is the fastest way to erode client trust in the analytics feature.

3. 03

   Validate dashboard performance against a target load of concurrent users

   Run a load test with the expected number of simultaneous viewers. If queries exceed 3 seconds at peak, enable data acceleration or pre-aggregation before handoff.

4. 04

   Review white-label branding on every embed surface

   Check that the client's logo, color palette, and domain appear consistently across dashboards, export views, and email notifications. Any vendor branding visible to end users violates the white-label promise.

5. 05

   Test natural-language querying against a set of common business questions

   Ask 10 typical questions a client stakeholder would pose, such as 'What was revenue last quarter?' and confirm the AI returns accurate, context-aware answers. This validates the AI-powered insight layer.

6. 06

   Document the integration architecture and customization points

   Produce a one-page diagram showing where the embedded analytics sits in the client's SaaS stack, including API endpoints and any custom CSS or JavaScript. This becomes the reference for future maintenance.

7. 07

   Run a user acceptance test with a small group of client power users

   Have 3 to 5 client employees interact with the dashboards and submit feedback. Capture their comments on usability, data accuracy, and missing metrics, then fix critical issues before full rollout.