Mediamath Data Quality & Governance

Data Quality & Governance

Establish the processes that keep Mediamath reporting accurate, trustworthy, and auditable.

Tracking Architecture Overview

• Document pixels, SDKs, APIs, and supporting components in use.
• Ownership matrix covering who maintains each tracking element.
• Change management expectations when updates or migrations occur.

Monitoring & QA Cadence

• Scheduled QA (pre/post-launch, weekly spot checks) with responsible teams.
• Automated alerts or thresholds for data drops, latency, or duplication.
• Reconciliation steps comparing platform metrics to analytics or CRM sources.

Data Governance Standards

• Naming conventions, campaign taxonomy, and ID mapping references.
• Access controls for data exports, warehouse tables, and BI tools.
• Alignment with Privacy Considerations to enforce consent rules.

Issue Resolution Workflow

• Intake channels for reporting data quality issues and expected response windows.
• Triage checklist to gauge scope, impact, and suspected root cause.
• Documentation and postmortem requirements once issues are resolved.