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Enterprise Commercial Data Intelligence & Governance

Director, Digital Marketing & System Solutions & Marketing Technology
McCann Health • 2022–2025


1. The Problem Space

Prior to this initiative, commercial reporting across the agency’s $35M portfolio relied on fragmented, non-cohesive data sources. Each brand team managed isolated reporting routines, leading to heavy manual data aggregation, duplicate work, and inconsistent KPI reporting across client touchpoints.

Beyond operational friction, the system faced two major constraints:

Without a unified solution, manual reporting cycles would continue to consume hundreds of operational hours annually, while increasing data privacy risk and metric drift across agency engagements.

2. My Role

As Director spanning Digital Marketing & System Solutions and Marketing Technology (2022–2025), I served as the end-to-end Product Owner for the enterprise analytics and visualization solution. I personally:

3. The Approach

I led a structured four-stage architecture rollout:

1. Feasibility & User Flow Mapping

I audited existing brand datasets, identified recurring manual data pulls, and mapped out an end-to-end architecture connecting raw agency data sources to a centralized workspace.

2. Data Pipeline & Schema Integration

Working with IT and Data Operations, I established automated data ingestion pipelines into a unified Power BI workspace. I built standardized relational schemas across disparate sources to ensure metrics were calculated and visualized identically across every dashboard.

3. Role-Based Access Control (RBAC)

To meet strict agency privacy rules and least-privileged access policies, I designed and gated access permissions via security groups:

  • Managers: Tightly restricted to viewing direct report and specific account team metrics.
  • Department Leadership: Granted aggregate visibility across entire department operations.
  • Agency President & Execs: Granted full portfolio-wide executive visibility.

4. Interactive Executive Visualization

Built self-service, drill-down dashboards replacing static status decks, providing real-time operational clarity for senior leadership.

4. Outcomes & Impact

5. What I'd Do Differently

Looking back at this architecture, version 2.0 would incorporate Natural Language Processing (NLP) / Conversational Querying directly into the governed semantic model.

While the Power BI dashboards provided self-service visibility, enabling executives to query the data via natural language (e.g., "Show me account resource allocation variance for Q2") would allow leadership to pull custom, real-time insights instantly without navigating visual drill-downs.