Advisory Services — Data Governance
Data Governance. The Foundation of Sovereign AI.
From data sovereignty to privacy and protection—we build the governance foundations that make AI adoption safe, compliant, and sustainably competitive.
Our Service Offering at a Glance
To gain productivity through AI without compromising on security, privacy, quality, and ethics, Data Governance is the strategic foundation. Our four practice areas work in concert to build the sovereign data ecosystem your organization needs.
Data Sovereignty
Own Your Data. Secure Your Future.
Current Challenges
- Dependence on foreign cloud providers creates geopolitical and supply chain exposure.
- Confidential data—including trade secrets and IP—is exposed to foreign intelligence laws.
- Technology lock-ins reduce flexibility and negotiation leverage.
- Data residency ≠ data sovereignty: data stored abroad may be legally accessible to foreign jurisdictions.
Target State
- Sovereign data and infrastructure ensure operational independence under any scenario.
- Confidential data and personal information are safe from foreign intelligence.
- Technology choices are strategic, compliant, and diversified—reducing concentration risk.
Our Approach
- 01Rapid assessment of data flows, vendor dependencies, and legal exposure.
- 02Define sovereign target architecture options, financing models, and transition planning.
- 03Execute migration, encryption, and continuity operations using local and open-source solutions.
Timeline →Sovereignty AssessmentTarget ArchitecturePilot ImplementationMigration
Data Governance
Trusted Data. Aligned to Business and AI Strategy.
Current Challenges
- Data fragmentation and inconsistent quality slow AI adoption and undermine decision-making.
- Unclear governance and insufficient controls increase regulatory, security, and operational risks.
- Siloed models and legacy technologies limit visibility, agility, and enterprise integration.
Target State
- Data is trusted, accessible, and aligned to business and AI strategy with strong accountability.
- Modernized data architecture enables interoperability, automation, and secure sharing.
- Clear operating model empowers teams with defined stewardship roles and lifecycle controls.
Our Approach
- 01Conduct data maturity assessments to identify critical gaps in governance, quality, and compliance.
- 02Design a data strategy and roadmap aligned to business and AI outcomes, including the target operating model.
- 03Deploy metadata, lifecycle management, and stewardship processes that deliver direct business value.
Timeline →Maturity AssessmentRoadmap & TOMPilot ImplementationProcess Implementation
Data Privacy
Privacy by Design. Compliance by Default.
Current Challenges
- Organizations face legal and reputational risks from privacy breaches.
- Lack of clear rules on personal data use hinders innovation, data sharing, and AI adoption.
- Privacy-enhancing technologies are underdeveloped, causing reliance on manual processes that create bottlenecks.
Target State
- A transparent, risk-based privacy program enables responsible use of personal data while supporting innovation.
- Data subjects' rights are continuously respected and efficiently managed at scale.
- Privacy requirements are embedded by design across processes, products, and technologies.
Our Approach
- 01Perform privacy assessments and a risk mitigation roadmap calibrated to regulatory exposure.
- 02Design the privacy operating model and implement privacy-by-design practices across key workflows.
- 03Deploy processes and tooling for inventory, DPIA, rights management, third-party risk, and breach response.
Timeline →Privacy AssessmentRoadmap & TOMPilot ImplementationProcess Implementation (DSR, PbD, IR)
Data Protection
Crown Jewels Protected. Zero Trust Enforced.
Current Challenges
- Organizations often fail to prevent data breaches from internal or external sources.
- Inability to manage identities and access, and misunderstanding data assets, are key obstacles.
- Security measures lack prioritization based on business value; inconsistent protection measures create gaps.
Target State
- Crown Jewels are identified and protection measures prioritized based on their classification.
- A zero-trust identity and access management policy is in place.
- Automated, state-of-the-art data protection technologies enable seamless cybersecurity.
Our Approach
- 01Perform Crown Jewels Inventory, NIST/ISO 27001 assessment, and data discovery to locate vulnerabilities.
- 02Define data protection strategies focused on business strategy and crown jewels protection.
- 03Integrate data protection as part of the organization's business-as-usual processes.
Timeline →Crown Jewel Inventory & AssessmentStrategy & RoadmapPilot ImplementationProcess Implementation (IAM, DLP)
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