Data Strategy
Governance that becomes business as usual
We implement data governance frameworks that manage data quality, security, and access — transforming governance from a project into a permanent business capability.
Our approach
How we deliver enterprise data governance
DAI Consultancy's governance methodology is rooted in internationally recognized frameworks including DAMA-DMBOK, ISO 8000, and the DCAM (Data Management Capability Assessment Model). We adapt these frameworks to the specific regulatory, cultural, and operational context of GCC organizations. Our engagements span the full governance lifecycle: defining information principles and policies, establishing stewardship networks, implementing data quality management programs, building business glossaries and data catalogs, and designing the metrics that demonstrate governance value to executive stakeholders.
A critical distinction in our approach is the separation of governance (oversight) from data management (execution). We use the Governance 'V' model to establish clear accountability: governance defines the rules, management implements them, and independent review supports compliance. This separation of duties prevents the common failure mode where the teams building data systems are also responsible for policing their own work.
What's included
Deliverables
Governance Framework Design
A comprehensive governance operating model defining principles, policies, standards, roles, and escalation procedures aligned with DAMA-DMBOK.
Data Stewardship Network
Defined stewardship roles and responsibilities mapped to business domains, with training and onboarding materials for appointed stewards.
Business Glossary & Data Catalog
A searchable inventory of business terms, data definitions, and dataset metadata with lineage — ensuring everyone speaks the same data language.
Data Quality Program
Automated quality rules, measurement dashboards, and remediation workflows that continuously monitor and improve data quality across critical datasets.
Governance Metrics & Reporting
KPIs and dashboards that demonstrate governance value to executives: data quality scores, issue resolution times, policy compliance rates, and business impact.
Want to scope this for your organization?
Discuss a Governance ProgramRegional framework alignment
Localized to GCC frameworks
We map this service to the official data governance, privacy, security, sharing, and operating-model expectations that apply in each jurisdiction.
CDO-led NDMO governance, catalog, quality & classification
- Data office and CDO-led governance
- Policy library and stewardship RACI
National Data Policy roles; QDKC strategy/governance
- Ownership, accountability, and decision rights
- QDKC-based governance and culture/literacy
National policies; DGM Office, training & performance
- Data management strategy and policy library
- DGM Office establishment and committee cadence
Abu Dhabi data governance; Smart Data reuse/exchange
- Data ownership and governance (Abu Dhabi)
- Reuse and exchange under Smart Data
PDPL controller accountability; open-data roles
- Controller accountability and roles
- Data-protection-guardian readiness
Background
Why it matters
Enterprise data governance is the discipline of managing data as a strategic business asset through defined policies, standards, roles, and processes. It is not an IT function — it is a business capability that sits at the intersection of strategy, compliance, and operations.
Use cases
Industries we serve
Financial Services
Establishing enterprise-wide data definitions for customer, product, and risk data to support Basel III/IV reporting and satisfy central bank data quality expectations.
Government
Designing federated governance frameworks across multiple ministries that balance national data-sharing requirements with departmental autonomy.
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FAQ
Frequently asked questions
Enterprise data governance is the set of policies, roles, and processes that ensure data is managed as a trusted business asset. It is important because every AI model, dashboard, and regulatory report depends on the quality and reliability of underlying data — governance is what makes that data trustworthy.
We treat governance as a business capability, not an IT project. Our approach is governance-first and standards-aligned (DAMA-DMBOK, ISO 8000), and we size the operating model to each organization — lean and centralized where that works best, federated where scale and complexity demand it — so governance scales without creating bureaucratic bottlenecks.
A foundational governance program — framework design, stewardship roles, initial policies, and data quality baselines — typically takes 3-6 months. Full maturity, including cultural adoption and advanced capabilities, typically develops over 12-24 months as the organization builds the governance muscle.
Ready to get started?
Let’s discuss how our governance-first approach to enterprise data governance can accelerate your data and AI initiatives.