That was the challenge we worked on with our partner, a leading consumer healthcare company. Key data domains such as SKUs, customer information, and pricing were managed in silos, forcing teams to rely on manual fixes and individual expertise. To support scalability and enable trustworthy insights, they neededa business-first governance framework with clear ownership of data domains.

That was the challenge we worked on with our partner, a leading consumer healthcare company. Key data domains such as SKUs, customer information, and pricing were managed in silos, forcing teams to rely on manual fixes and individual expertise. To support scalability and enable trustworthy insights, they neededa business-first governance framework with clear ownership of data domains.

We worked with all areas of the business to design a centralized governance framework anchored in six pillars (ownership, literacy, processes, tools, principles, and architecture). Together we defined roles, created a data ownership matrix, and established escalation paths. The work prepared for a Global Data Office to anchor governance long term.

We worked with all areas of the business to design a centralized governance framework anchored in six pillars (ownership, literacy, processes, tools, principles, and architecture). Together we defined roles, created a data ownership matrix, and established escalation paths. The work prepared for a Global Data Office to anchor governance long term.

Delivered a fit-for-purpose framework with:

1. A governance structure linking data domains to accountable owners.

2. Role descriptions and escalation processes.

3. Templates for standardized master data processes.

4. Blueprint for a Data Office as anchor for future governance.

This gave our partner clarity on “who owns what” and how data structures should be managed across systems.

Delivered a fit-for-purpose framework with:

1. A governance structure linking data domains to accountable owners.

2. Role descriptions and escalation processes.

3. Templates for standardized master data processes.

4. Blueprint for a Data Office as anchor for future governance.

This gave our partner clarity on “who owns what” and how data structures should be managed across systems.

Our partner gained a clear ownership model that reduced reliance on individuals and ad hoc fixes. Teams reported stronger clarity on roles, and leaders could use the governance roadmap to prioritize initiatives. Early steps were taken to formalize a data governance lead role and establish cross-functional forums.

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Our partner gained a clear ownership model that reduced reliance on individuals and ad hoc fixes. Teams reported stronger clarity on roles, and leaders could use the governance roadmap to prioritize initiatives. Early steps were taken to formalize a data governance lead role and establish cross-functional forums.

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By embedding structures and ownership, the organization is now positioned to scale with consistent, trusted data. This strengthens efficiency, supports advanced analytics, and builds the foundation for future AI-driven use cases.

By embedding structures and ownership, the organization is now positioned to scale with consistent, trusted data. This strengthens efficiency, supports advanced analytics, and builds the foundation for future AI-driven use cases.