Enterprise Data Governance Programme for a 50,000-User SaaS Platform
Following rapid growth, our client faced fragmented data ownership, inconsistent data quality and growing compliance exposure. We designed and delivered an enterprise data governance framework — establishing data stewardship, quality standards, policy documentation and a compliance-ready data architecture that reduced regulatory risk and improved decision-making confidence across the leadership team.
The governance diagnostic: mapping what actually exists
The engagement began with a two-week diagnostic to understand the current state of data governance across the organization. This involved interviews with data owners across product, engineering, customer success and finance; analysis of existing data policies and documentation; mapping of data flows across the platform infrastructure; and an initial compliance exposure assessment. The diagnostic surfaced the specific governance gaps — undefined data ownership, inconsistent quality standards, absent classification framework, incomplete GDPR documentation — that the programme needed to address.
Building governance that the organization can sustain
The most common failure mode in data governance programmes is building governance structures that are too complex for the organization to maintain. We designed the governance framework to be proportionate to the organization's size and maturity — clear ownership assignments without excessive bureaucracy, quality standards that data teams could actually measure and report against, and policy documentation written in language that non-legal stakeholders could understand and follow. The governance operating model defined the data stewardship roles, governance committee structure and policy review cadence that would sustain the framework beyond the engagement.
Measured outcomes
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