How Transforming a Fortune 500’s Data Architecture Broke the Cycle of More Tools, Reporting and Vendors
Inside a Fortune 500 communications team's shift from fragmented, vendor-dependent intelligence to an enterprise-owned data foundation built for the AI era.
The data sat across vendors, platforms, and teams. Each could explain its own piece, but leaders still had to reconstruct the whole story before they could decide what to do next.
Different answers to the same question
Manual work before every new analysis
Weak connections between spend and impact
Data without shared context for AI
The answer was upstream of the reports.
STRCTR changed how source data was collected, defined, governed, and served. One enterprise-controlled foundation replaced repeated reconciliation across disconnected systems.
1
UPSTREAM CONDITIONS
Data lacked shared context.
Risk and impact surfaced at different times across channels and markets. Teams couldn’t consistently connect spend to outcomes or carry learning into the next investment.
2
FUNCTIONAL CONSEQUENCES
Teams reacted and optimized in silos.
Activity was easier to see than impact. Teams struggled to choose interventions, validate ROI, and build on past learning—keeping strategy reactive.
3
EXECUTIVE EXPERIENCE
Strategic impact lacked proof.
Different answers left leadership asking what was driving change, whether investments worked, and why the function always felt behind. The story had to be rebuilt each time.
Less time assembling data. More clarity to act.
The improvement reached beyond reporting. Leaders gained a more consistent view of communications activity, market movement, and outcomes, while the organization retained control of the underlying data.
For communications leaders: a clearer connection between strategy, investment, and impact.
For operations and finance: more visibility into duplicated work and data management costs.
For data and AI teams: governed data that can be reused across approved systems and future applications.