Data Governance & Compliance Services

You're ready when you have executive sponsorship and at least one concrete business problem that data governance can solve - whether that's failing compliance audits, spending excessive time on data preparation, or inability to trust analytics for decision-making.

We don't recommend starting with enterprise-wide frameworks. Instead, we assess your Data Maturity level and identify high-value pilot use cases that deliver ROI within 90 days, then scale from there.

No. Tools don't create governance, people and processes do. We start by assessing your existing technology stack (data catalogs, lineage tools, quality platforms) and leverage what you already have.

Many organizations waste 6-figure investments on governance platforms that sit unused because they lacked the foundational policies and organizational buy-in first.

We recommend reaching Data Maturity Level 3 before significant tool investments. When tools are needed, we provide vendor-neutral assessments and implement only what directly supports your use cases.

We've implemented compliance frameworks across GDPR (EU), CCPA/CPRA (California), HIPAA (healthcare), GLBA (financial services), SOC 2, ISO 27001, PCI-DSS, and industry-specific regulations like FDA 21 CFR Part 11 for life sciences and FINRA for financial services.

Our DPO-partnered approach includes automated compliance monitoring, right-to-erasure workflows, consent management, and audit trail automation. We also help navigate emerging AI regulations including the EU AI Act and state-level AI governance requirements.

Sustainability is built into our approach from day one. We transfer knowledge continuously through embedded coaching, documentation, and hands-on training for your data stewards, owners, and governance council.

Many clients start with our "Team as a Service" model where we fill interim roles (Head of Data Governance, Data Stewards) while hiring and training permanent staff. We provide playbooks, policy templates, and runbooks so your team can operate independently. We also offer ongoing advisory retainers for strategic guidance as your program matures.

Our AI Governance practice extends traditional data governance into model development, training data lineage, feature store management, and model monitoring.

We implement MLOps governance that tracks model versions, training datasets, bias metrics, and inference decisions for regulatory explainability.

For generative AI, we establish acceptable use policies, prompt injection controls, training data compliance verification, and output monitoring.

We also help organizations govern RAG (Retrieval Augmented Generation) systems by ensuring the underlying knowledge bases meet data quality and compliance standards.

Our Launchpad Program delivers measurable results in 8-12 weeks for focused pilot implementations. Full enterprise rollouts typically span 6-18 months depending on organizational complexity, number of domains, and regulatory requirements.

We structure engagements with clear phase gates and success criteria, allowing you to validate ROI before expanding scope. Investment levels vary based on your specific needs, existing infrastructure, and objectives - we'll provide a tailored proposal after our initial assessment.

Most CTOs and DPOs see significant returns within the first year through reduced compliance penalties, improved data quality, and accelerated time-to-insight.

We design governance as enablement, not bureaucracy. Our Data Mesh 2.0 approach decentralizes ownership to domain teams while automating policy enforcement through computational governance, meaning your engineers get faster access to trusted data through self-service platforms with embedded guardrails.

We implement "just enough" governance: clear data contracts, automated quality checks, and policy-as-code that runs in CI/CD pipelines. Teams move faster because they spend less time questioning data reliability and more time building features.

We establish KPIs tied directly to your business objectives before starting. Common metrics include: reduction in time-to-insight (often 40-60% improvement), decrease in data quality incidents, growth in self-service analytics adoption, reduction in compliance violations, and increases in data-driven decision velocity.

We also track leading indicators like business glossary coverage, data lineage completeness, and automated policy compliance rates. Every metric connects to either revenue generation, cost reduction, or risk mitigation, making governance value tangible to your board.

We specialize in federated governance architectures that span AWS, Azure, GCP, on-premise data centers, and SaaS applications. Our approach uses cloud-agnostic metadata management, cross-platform lineage tracking, and unified policy enforcement regardless of where data lives.

We implement data fabric patterns that provide consistent governance, security, and access controls across your entire data estate. For organizations in cloud migration, we establish governance guardrails that enable safe, compliant data movement while maintaining business continuity.

We lead with value, not compliance fear. In our kickoff workshops, we identify each stakeholder's pain points, whether that's slow report generation, inconsistent metrics, or inability to trust customer data, and demonstrate how governance directly solves their problems.

We secure executive sponsorship by tying governance to strategic objectives in your board deck language: revenue growth, market expansion, operational efficiency. Quick wins in the first 90 days build credibility.

We've found that when business users see governance as "faster access to better data" rather than "more approval processes," adoption accelerates dramatically.