Enterprise Data Platform Strategy
Turning customer insight, enterprise workflows, and technical constraints into clearer platform direction, service improvements, and an actionable AI roadmap.
Fragmented signals obscured enterprise priorities
Teams were working across complex data systems, service requests, and competing stakeholder needs. Customer feedback existed, but it was distributed across interviews, help-desk conversations, and operational workflows, making it difficult to distinguish isolated requests from systemic opportunities.
Turn scattered evidence into a decision system
I led discovery across customers, product stakeholders, developers, and support teams, then synthesized the findings into themes connecting user needs with operational constraints. The work translated research into prioritized recommendations spanning self-service support, intake and routing, knowledge retrieval, escalation, and responsible uses of AI.
A clearer enterprise investment roadmap
The resulting recommendations gave leaders a shared view of recurring needs and viable opportunities. They informed executive conversations, investment priorities, intake and routing improvements, and a practical AI roadmap while keeping human oversight and implementation constraints visible.
What I delivered
Research, product strategy, service design, and human-centered AI came together to give leaders a practical path from evidence to action.
Synthesized interviews, service requests, and operational evidence into clear themes, opportunity areas, and decision-ready recommendations.
Translated recurring needs into a prioritized roadmap spanning self-service support, knowledge retrieval, intake, routing, and escalation.
Framed viable AI opportunities with clear boundaries for human review, trust, implementation feasibility, and adoption.