End to End — AT&T's Data Quality Dashboard Redesign

Mandate: Establish an enterprise data-quality operating model so analytics and AI decisions are trustworthy, auditable, and repeatable.

Scope: End-to-end data lifecycle (ingest → transform → publish → monitor), shared definitions, quality gates, and cross-team governance.

Role: Design + Product Ops leadership — defined the Data Quality Contract (definitions + SLAs), implemented governance reviews, and aligned stakeholders on quality gates and ownership.

Leadership Footprint:

Cross-org alignment: Unified stakeholders under shared data definitions and accountability (ownership + escalation paths).
Hard decisions: Introduced quality gates and stop-ship criteria when data trust was at risk.
Tradeoffs: Balanced speed vs. correctness by automating checks where possible and escalating only high-risk exceptions.

Created a repeatable DQ operating model with clear ownership, quality thresholds, and monitoring.
Reduced rework by catching issues earlier with quality gates and exception workflows.
Improved decision confidence by making lineage, definitions, and quality status visible at decision time.

Embedded into enterprise workflows: quality gates + exception paths make trust visible at decision time, not after the fact.
Reusable patterns & constraints: standardized contracts (definitions + SLAs) and governance reviews create scalable primitives other teams can adopt.
Governance-by-design: lineage + evidence tie UX steps to audit-ready artifacts, supporting transparency and accountability expectations.
Enterprise governance primitives: designed for audit trails, access boundaries, and policy constraints to be enforced consistently across teams.
Human control: “stop-ship” criteria and escalation paths ensure high-risk decisions require review, not silent automation.
Cross-org influence: aligned stakeholders on ownership and operating model—exactly how cross-product AI UX patterns scale.

This framing aligns to Rocket’s stated AI UX mandate: reusable experience frameCase Studies, trust/clarity, and governance principles for enterprise-grade decision systems.

Data trust issues were slowing decisions and creating rework across analytics and AI-driven workflows.

Inconsistent definitions: teams interpreted the same metrics differently, reducing decision confidence.
Late detection: issues surfaced downstream, increasing rework and time-to-resolution.
No clear ownership: escalation paths were unclear, so failures lingered.
Governance risk: limited lineage/traceability made audit and compliance harder.

DEFINE→ VALIDATE→ MONITOR→ REMEDIATE (+ ownership • SLAs • evidence)

Shared operating model that makes data quality measurable, governable, and repeatable.

1) Data Quality Contract (Definitions + SLAs)

Standardized metric definitions, thresholds, and ownership to reduce ambiguity.
Established quality SLAs and "stop-ship" criteria for high-risk datasets.

2) Quality Gates + Exception Workflow

Added validation gates in the pipeline (schema, completeness, drift, consistency checks).
Defined exception paths: quarantine → triage → fix → re-validate.

3) Observability + Governance-by-Design

Made lineage, quality status, and evidence visible at decision time (not buried in logs).
Aligned auditability: user-visible steps map to logged evidence for review.

E2E DQ Dashboard

E2E DQ Report

Unified governance model: replaced ad-hoc checks with a single quality contract and review cadence adopted across teams.
Outcome-based prioritization: shifted work from "fixing reports" to preventing upstream quality regressions through gates and monitoring.
Accountability at scale: clarified ownership and escalation paths so issues resolve fast and don't recur.

How leadership shows up: decision velocity, risk reduction, and operational efficiency—measured and governed as a system.

Decision Velocity: increased decision confidence by making quality status and definitions visible at decision time.
Risk Reduction: reduced audit and compliance risk via lineage + evidence-backed quality gates.
Operational Efficiency: decreased rework by detecting issues earlier and routing exceptions through a defined workflow.
Reliability: improved pipeline stability by monitoring drift/regressions and preventing repeat incidents.

E2E DQ Report