Transforming a Critical B2B
Data Governance Platform

Schneider Electric

Schneider Electric's global offer publication system was at risk due to legacy architecture and stakeholder misalignment.
We redesigned the governance model to restore scalability and reliability.

  • −30%

    Rejected orders.

  • −50%

    Publishing workload.

  • 1 monthly

    Release (vs 3 per year previously).

Role
Design Lead
Process
Discovery → MVP → Roadmap
Team
1 Lead PM · 1 UI Designer · Engineering team

Key decisions

  • Reframed user research as a data journey to work within a process-oriented culture
  • Coached stakeholders through OKR definition to force prioritization
  • Embedded engineers in discovery to align feasibility with design ambition
Main interface — Schneider Electric offer publication platform
The redesigned offer publication platform.

Context

At Schneider Electric, a legacy offer publication tool was powering all commercial systems: pricing, catalog, and commercial policy.
The tool:

  • Had been developed in the early 2000s
  • Was maintained by a single engineer
  • Had no documentation or redundancy
  • Was approaching end-of-life with the engineer’s retirement

We had 10 months to define the replacement strategy before operational risk became critical.

My role: define product strategy, structure discovery, and scope the MVP platform.

The Real Problem

The technical obsolescence was the trigger.
The deeper issues were operational and organizational.

Operational observations

  • Sales and offer teams were spending significant time on manual publishing tasks
  • Data validation was fragmented and largely manual
  • New offers required several months to reach the market
  • Only three releases per year were possible

Organizational observations

  • Three stakeholder groups operated with independent priorities
  • No shared definition of success
  • No end-to-end visibility on data quality
  • Strong dependency on individual expertise

This replacement was not only a technical migration, it required redefining workflows, governance, and release cadence.

Key Decisions

Reframe research around data, not users

Stakeholder conversations were structured around system architecture and data structures rather than user workflows.
We mapped the complete data journey to:

  • Created immediate alignment across technical and business stakeholders
  • Surfaced bottlenecks in validation and handoffs
  • Revealed ownership gaps

We then translated the data journey into user journeys.

Data journey map — 10 families and 4 team groups
Data journey map — how data moved across 10 families and 4 teams, systemic failures made visible.

Force stakeholder alignment with OKRs

Three stakeholder groups had competing roadmaps and release expectations.
The lead PM and I ran a structured goal-setting session: 3-month and 6-month OKRs with key results attached.
It created a shared definition of success and reduced scope drift during delivery.

OKR alignment session output
OKR session output — 3-month and 6-month goals with measurable key results.

Pull engineers into discovery early

We ran collaborative sketching sessions with engineers in the room as participants.
The goal was simple: catch feasibility issues before they became design debt.
This significantly de-risked implementation within the 10-month window.

Research & Discovery

Months of stakeholder interviews and team shadowing.
Four categories of risk emerged:

  • Technical — obsolete stack, no maintainability
  • Organizational — desynchronized teams, single point of failure
  • Operational — publishing bottlenecks, manual workload
  • Strategic — slow time-to-market

Across profiles and teams, three needs were consistently expressed:

  • A unified interface to view and edit data without context switching
  • Integrated, continuous data quality validation within the workflow
  • A reporting dashboard with exportable completeness and compliance reports

These became the backbone of the MVP scope.

Pain point mapping
Pain point mapping across teams.

Design Process

We ran feature mapping sessions with Product and Engineering.
Six MVP modules were selected:

  • Administration
  • Data Quality Management
  • Offer Management
  • Pricing Edition
  • Reporting
  • Governance & permissions

To structure ideation, I facilitated a sketching session supported by a curated UX inspiration wall, making solution exploration accessible to non-design stakeholders.

Pain point mapping
Pain point mapping across teams.

Outcomes & Learnings

  • −30%

    Rejected orders.

  • −50%

    Publishing workload.

  • 1 monthly

    Release (vs 3 per year previously).

Key learnings from this transformation:

  • Adapting research framing accelerates alignment
    Structuring discovery around stakeholder mental models (data lifecycle) enabled faster buy-in and reduced resistance to change.
    User journeys were introduced once alignment was established, increasing their adoption.
  • Early engineering involvement reduces delivery risk
    In constrained environments, involving engineers in discovery is a risk mitigation strategy.
    It improves feasibility validation and shared accountability.