Data Control, Quality and Completeness
Purpose
Data Control, Quality and Completeness is designed to perform data completeness and quality checks, support natural-language querying through an intelligent dictionary, and provide policy Q&A and DPIA support. It also helps perform proactive checks on completeness, conformance, and reconciliation results while logging queries and decisions for evidence and audit.
Primary users
The primary user is not specified in the provided information. The agent is associated with the FRM team and is intended for use in a financial services context.
Where it fits (process/stage/trigger)
This agent fits into data control, quality assurance, and audit evidence processes where completeness, conformance, reconciliation, policy interpretation, or DPIA support is required. It can be triggered when users need to validate data quality, query policy information, review reconciliation outcomes, or retain evidence of queries and decisions.
Key capabilities / workflow
The agent performs data completeness and quality checks, facilitates natural-language querying through an intelligent dictionary, provides natural-language policy Q&A, supports DPIA-related activities, and proactively checks completeness, conformance, and reconciliation results. It also logs queries and decisions to support evidence retention and audit needs.
Inputs
Inputs are not specified in the provided information. The source information only indicates that the agent works with data requiring completeness and quality checks, policy questions, DPIA support needs, and completeness, conformance, or reconciliation results.
Outputs / Deliverables
Outputs include completeness and quality check results, natural-language policy answers, DPIA support, proactive findings on completeness, conformance, and reconciliation results, and logs of queries and decisions for evidence and audit. No additional output formats were specified.
Value
The agent supports stronger data control by helping users identify completeness and quality issues, query relevant policy information in natural language, and maintain evidence of decisions for audit purposes. Its value comes from improving transparency, supporting control activities, and reducing manual effort in reviewing data quality, conformance, reconciliation, and related policy questions.
