Data Trust Gate (QA)

Data Trust Gate (QA)

1. 01

   Define the data quality contract with the client

   Agree on specific thresholds for accuracy, completeness, timeliness, and consistency that the client's analytics or AI initiatives require, and document them in the statement of work.

2. 02

   Inventory all data sources and pipeline touchpoints

   Map every system feeding the client's warehouse or AI models, including CRMs, marketing platforms, and third-party APIs, to identify where errors or delays can enter.

3. 03

   Implement automated monitoring for anomalies

   Configure the observability platform to alert on schema changes, volume drops, or null-rate spikes, so issues surface before they reach client-facing reports.

4. 04

   Run a baseline data quality audit

   Execute a one-time deep scan of historical data to quantify existing issues, such as duplicate records or missing fields, and establish a starting point for improvement.

5. 05

   Establish a remediation workflow for flagged issues

   Define who owns fixing each type of data problem, whether it's a pipeline engineer, a data analyst, or the client's team, and set service-level targets for resolution.

6. 06

   Verify AI outputs against source data

   Before delivering any AI-generated insights or recommendations, spot-check a sample against the underlying data to catch hallucinations or misreadings, especially given recent incidents of fabricated sources in client deliverables.

7. 07

   Document the quality posture for the client

   Provide a one-page summary of the monitoring setup, current quality scores, and the escalation path, so the client understands how data integrity is being protected.