Data Governance
Data governance designed for AI, analytics, and accountability.
We help organizations design data governance frameworks, stewardship models, metadata and lineage practices, and data quality disciplines that make AI and analytics more reliable, explainable, and auditable.
Leadership questions
What we help leadership teams solve
Data Governance engagements focus on how data is owned, managed, and trusted across the organization. We work with business, data, technology, and risk leaders to ensure governance practices are practical and enable, rather than block, AI and analytics.
Who owns which data and for what purpose?
Clarify data ownership, stewardship, and decision rights across domains so that accountability is explicit, not assumed.
How do we know which data can be used for AI?
Define rules and guidance on when and how data can be used in AI and analytics, including sensitivity and usage constraints.
How do we manage data quality in a targeted way?
Focus quality efforts on data that matters most to AI outcomes and critical decisions, instead of generic cleanup efforts.
Can we trace how data moves and is transformed?
Establish lineage and metadata practices so that you can explain where data comes from, how it is transformed, and where it is used.
How does data governance support AI governance?
Connect data roles, policies, and controls to AI governance expectations around transparency, fairness, and accountability.
How much governance is enough?
Design a governance approach that is proportionate to risk and scale, avoiding both over-control and under-governance.
Workstreams
Core data governance workstreams
Governance framework & operating model
Define governance layers, forums, and decision pathways for data, connected to your broader AI and risk governance structures.
Roles & stewardship model
Clarify the responsibilities of data owners, stewards, custodians, and consumers across priority domains supporting AI and analytics.
Policies, standards & guidance
Design pragmatic policies, standards, and guidelines that set expectations for data access, use, protection, and retention.
Metadata & catalog practices
Establish approaches to capturing and maintaining business and technical metadata so that critical data becomes discoverable and understood.
Data lineage & traceability
Define lineage expectations and practical ways to trace data flow across key pipelines that feed AI models and critical analytics.
Data quality & issue management
Design targeted quality rules, monitoring, and issue management processes tied to high-impact data elements and AI use cases.
Engagement formats
Typical data governance engagement formats
Data Governance work can begin with a focused review or progress toward a more comprehensive framework and operating model. Below are representative formats that can be adapted to your scale and maturity.
Data governance baseline & gap assessment
A structured review of current data governance practices, roles, and documentation against desired outcomes for AI and analytics.
- Review of existing governance forums and artefacts
- Assessment of ownership, stewardship, and policies
- Findings and prioritized improvement areas
Framework & operating model design
A deeper engagement to design governance framework, roles, and core policy and process structures aligned to AI needs.
- Framework and forum definitions
- Role and stewardship model
- Outline of policy, standard, and guidance topics
Metadata, lineage & quality enablement
A practical engagement focused on metadata, lineage, and data quality capabilities that directly support priority AI and analytics use cases.
- Priority domains and data elements identification
- Metadata and lineage expectations for key pipelines
- Targeted quality rules and issue workflows
Integrated advisory
How data governance connects to other Truzen services
Data Governance is a core enabler for AI Strategy, Data Strategy, AI Governance, AI Risk & Compliance, and AI Operating Model work. We design governance to be consistent with, and supportive of, these related efforts.
Data & AI Strategy
Strategy defines which data domains and quality thresholds matter most.
View Data Strategy → View AI Strategy →AI Governance & Responsible AI
AI governance relies on data ownership, lineage, and quality to support explainability and accountability.
View AI Governance →AI Risk & Compliance
Data controls, quality, and usage guidance are central components of AI risk and compliance work.
View AI Risk & Compliance →AI Operating Model & Readiness
The operating model ensures data governance roles and workflows are embedded into day-to-day decision-making.
View AI Operating Model →Ready to strengthen data governance for AI?
We can start with a focused governance baseline review or move into framework and operating model design, depending on your maturity.