Business Consulting
Data and AI Governance Services
Establish clear ownership, quality, lineage and accountability for your data and AI systems, so teams can innovate quickly without creating hidden risk.
Governance That Enables Rather Than Blocks
Data and AI are only as valuable as they are trustworthy. When nobody owns key data, definitions differ between teams, sensitive information is copied into unmanaged tools and AI models reach customers without documented testing, the organization is exposed to poor decisions, regulatory findings and reputational damage. At the same time, heavy-handed governance that only adds approval steps slows down every initiative.
Hanoi Technology designs pragmatic governance that is embedded in how people already work. We define roles and decision rights, implement data catalogs, lineage and quality monitoring, and establish AI risk management aligned with frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001 and the EU AI Act, supported by tooling rather than manual paperwork.
The Challenge
Why Data and AI Governance Is Hard
Organizations struggle with governance for reasons that are cultural and technical at the same time.
No Clear Ownership
Critical data domains such as customer, product or finance have no accountable owner, so quality issues and definition conflicts are never resolved.
Limited Visibility of Data Flows
Without a catalog and lineage, teams cannot tell where data comes from, who uses it, or what breaks when a source changes, making impact analysis and audits slow.
Rapid, Unmanaged AI Adoption
Business teams adopt AI tools and build models faster than policies evolve. Few organizations maintain a complete inventory of AI use cases and their risk levels.
Governance as Paperwork
Policies exist in documents but are not reflected in tools, workflows or controls, so compliance depends on goodwill and is hard to demonstrate.
Business Value
What Effective Governance Delivers
Trusted, Findable Data
A business glossary, catalog and certified datasets help users find the right data quickly and understand what it means.
Higher Data Quality
Defined quality rules, monitoring and issue workflows fix problems at the source rather than in every downstream report.
Regulatory Confidence
Documented controls, lineage and AI risk assessments make it easier to demonstrate compliance with data protection and AI regulation.
Safer AI at Scale
An AI inventory, risk classification and lifecycle controls let teams deploy AI faster with appropriate oversight.
Controlled Access to Sensitive Data
Classification and policy-based access protect personal and confidential data while enabling legitimate use.
Clear Accountability
Owners and stewards know their responsibilities, and decisions about data and AI follow transparent processes.
Govern the Data and the Models Together
AI systems inherit every weakness of the data they are trained on and the processes that feed them. Treating data governance and AI governance as separate programs creates gaps. We design one connected framework where data ownership, quality, lineage and access controls extend naturally to models, prompts, evaluations and AI-enabled decisions.
Our Services
Hanoi Technology’s End-to-End Data and AI Governance Services
Governance services that combine operating model design with hands-on implementation.
Governance Maturity Assessment
We assess your current policies, roles, tools, data quality and AI practices against recognized frameworks and define a prioritized improvement roadmap.
- Maturity assessment
- Risk and gap analysis
- Governance roadmap
Governance Operating Model
We define governance bodies, data domains, owners, stewards and decision rights, together with policies and standards that fit your organization.
- Roles and responsibilities
- Policies and standards
- Governance council setup
Data Catalog and Lineage
We implement data catalogs with business glossaries, technical metadata and automated lineage so users and auditors can trace data end to end.
- Catalog implementation
- Business glossary
- Automated lineage
Data Quality Management
We define quality dimensions and rules for critical data elements, implement monitoring and issue workflows, and report quality trends to owners.
- Critical data element list
- Quality rules and monitoring
- Data quality dashboards
Data Privacy and Access Governance
We classify sensitive data, implement policy-based access, masking and retention, and support privacy requirements such as GDPR.
- Data classification scheme
- Access and masking policies
- Retention rules
AI Governance and Responsible AI
We build an AI inventory, risk classification, model documentation, evaluation and monitoring processes aligned with the NIST AI RMF, ISO/IEC 42001 and the EU AI Act.
- AI use-case inventory
- AI risk assessment process
- Model cards and monitoring
Our Approach
How We Implement Governance
Start with the data and AI that matter most, prove value, then scale.
- 01
Assess and Prioritize
We review current practices, regulatory obligations and pain points, and select priority data domains and AI use cases to govern first.
Deliverables: Maturity assessment · Priority domains and use cases
- 02
Design the Framework
We define the operating model, policies, standards and controls, adapted to your size, culture and regulatory exposure.
Deliverables: Governance framework · Policy set
- 03
Implement Tooling
We configure catalog, lineage, quality and access tools, integrate them with your data platforms and populate them for priority domains.
Deliverables: Configured governance tools · Populated catalog
- 04
Embed in Workflows
We integrate governance checks into data pipelines, BI publishing and AI deployment processes, so controls happen automatically.
Deliverables: Automated controls · Updated delivery workflows
- 05
Measure and Expand
We track governance KPIs, report to leadership and extend the framework to further domains and use cases over time.
Deliverables: Governance KPI dashboard · Expansion plan
Use Cases
Governance Use Cases
Situations where organizations engage us for data and AI governance.
Customer Data Domain
A company appoints owners and stewards for customer data, defines a golden record and monitors completeness and duplicates across CRM and billing systems.
Regulatory Reporting Lineage
A financial institution documents lineage from source systems to regulatory reports, enabling fast impact analysis and audit evidence.
Enterprise AI Inventory
An organization discovers and registers all AI use cases, including third-party AI tools, and classifies each by risk level and required controls.
Generative AI Usage Policy
A business defines which data can be used with which AI tools, implements technical controls and trains employees on responsible use.
Data Catalog for Self-Service Analytics
Analysts find certified datasets with business definitions and owners in one catalog instead of asking colleagues where data lives.
Sensitive Data Discovery and Masking
A company scans its data platform for personal data, applies classification labels and masks sensitive columns for non-production environments.
Technology
Governance Platforms and Frameworks
We implement leading governance tools and align with recognized frameworks.
- Microsoft Purview
- Collibra
- Alation
- Informatica Cloud Data Governance and Catalog
- Ataccama ONE
- Databricks Unity Catalog
- Snowflake Horizon
- Great Expectations
- Soda
- OpenLineage
- DAMA-DMBOK
- NIST AI RMF
- ISO/IEC 42001
- EU AI Act
- GDPR
Platforms
Deliver Data and AI Governance on Your Preferred Platform
- InformaticaEnterprise data integration, quality and governance.Explore
- AtaccamaData quality, governance and master data in one platform.Explore
- MicrosoftPower BI, Power Platform, Fabric and Dynamics 365.Explore
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · SaaS
AI support assistant and smart escalation for a global SaaS company
A project-management SaaS provider receiving thousands of support requests a day needed faster answers without growing the support team. We built an AI assistant grounded in its knowledge base, with escalation to human agents.
Read the Full StoryMore Success Stories
HR tech
80%faster hiring processAI candidate screening and matching for an HR tech startup
An HR tech startup processing thousands of applications a month needed recruiters to spend less time screening CVs. We built resume parsing, job matching and an automated shortlisting workflow.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Build Trust in Your Data and AI
Tell us about your data landscape, AI initiatives and compliance concerns. We will reply within 24 hours to arrange a free consultation and suggest a practical starting point.
Data and AI Governance FAQ
Where should we start with data governance?
Start where poor data has a visible business cost, such as inconsistent customer data, unreliable financial reporting or a regulatory requirement. We select one or two priority domains, appoint owners and stewards, define critical data elements and quality rules, and implement catalog and monitoring for that scope. Demonstrating value in a focused area builds support for expanding governance, rather than launching an enterprise-wide program that takes years to show results.
How does AI governance relate to the EU AI Act?
The EU AI Act classifies AI systems by risk level and sets obligations for providers and deployers, with stricter requirements for high-risk systems, phased in over several years. An AI governance framework helps you identify which of your systems are in scope, classify them, and implement the required risk management, documentation, human oversight and monitoring. We work with your legal advisors, who interpret the regulation, while we design and implement the processes and tooling.
Which frameworks do you align with?
For data governance we draw on DAMA-DMBOK concepts. For AI governance we commonly use the NIST AI Risk Management Framework, which offers practical guidance, and ISO/IEC 42001, which defines requirements for an AI management system and can be certified by accredited bodies. We map controls to the EU AI Act and data protection rules such as GDPR where relevant, so one set of controls supports several requirements.
Which data catalog or governance tool should we choose?
The best fit depends on your data platform, cloud strategy, budget and governance scope. Microsoft Purview suits Microsoft-centric estates; Databricks Unity Catalog and Snowflake Horizon govern assets within those platforms; Collibra, Alation, Informatica and Ataccama offer broad enterprise capabilities across many sources. We compare tools against your requirements with a short proof of concept on your own metadata before you commit.
How long does it take to establish governance?
An assessment and framework design usually takes six to ten weeks. Implementing tooling and governing the first priority domains typically takes a further two to four months. Governance is then extended domain by domain. We define clear milestones and KPIs, such as the share of critical data elements with owners and quality rules, so leadership can track progress objectively.
Will governance slow down our analytics and AI teams?
Done well, it speeds them up. Certified datasets, clear definitions and automated lineage reduce time spent finding and validating data. Pre-approved patterns for AI tools and data use let teams move forward without case-by-case negotiation. We embed controls into pipelines and deployment workflows rather than adding manual approval steps, and we focus strict oversight on genuinely high-risk data and AI use cases.