Together, we will design an actionable roadmap for implementing a system of standards and metrics that gives you real-time visibility into your data processes and complete control over your data assets. This is how we ensure that your data is accurate, secure, high-quality, and easily accessible by those who need it.
Build a comprehensive data governance framework to maximize insights and experience business growth.
Our data governance services are founded on the principles of ethics and responsible data use. Trust, privacy, security, and fairness are the pillars that underpin our governance frameworks, which are built upon basic concepts of data ethics:
Our solutions are clear on what data is used for, how it is stored, and who has access to it.
Our data governance frameworks comply with laws and regulations such as the CCPA, the GDPR, and the EU AI Act.
In our work, we always make sure private data remains in the possession of its owners and that consent is explicitly obtained for its use.
We only collect data that is necessary for and relevant to our purposes and remove it when it is no longer needed.
We establish a clear chain of reporting in all matters relating to data management.
In our data analytics, we make sure data sets are bias-free and contain no discriminatory elements.
Our AI-driven solutions are developed with clarity and transparency to boost trust in the generated insights.
Our data governance frameworks can effectively identify and mitigate possible risks related to inaccuracy or bias of AI solutions.
There is no cookie-cutter data governance solution that fits all. We will help you design data governance strategies and methodologies that work for you and your organizational structure.
How we work with clients to create a data governance strategy
Choose the plan that suits your comfort level:
Assessment and establishment of a foundational strategy to address critical needs
Timeline: 4–8 weeks depending on infrastructure complexity
Key outcomes:
A comprehensive program covering all domains and processes
All activities in Lite data governance services, plus:
Timeline: 6–12 months for the planning phase, with additional time for implementation
Key outcomes:
You don’t necessarily need to invest in your own data governance software and integrations. We can provide them for you through Data Governance as a Service (DGaaS), taking over the implementation of custom data governance frameworks that enable ongoing management, monitoring, and enforcement of data governance policies. Our DGaaS solutions include automated reporting, data lineage tracking, and data governance health checks.
Our strong partnerships give you the full freedom to opt for the best providers and tools for the job.
With a focus on results, we help you choose the right technology to get you there faster.
Eugene Berko
Director of Technology Practices
Director of Technology Practices at Intellias with 13+ years of experience in software engineering, management, and consulting. The area of Yevhen’s responsibility is the company’s technology offering in the fields of Data & AI, Cybersecurity, IoT, Cloud & DevOps, Support, Intelligent Automation, Business Applications.
Driven by passion for learning and making a difference, Yevhen is also a firm believer in flexibility and providing a unique solution to each problem rather than following 300+ page frameworks. He often speaks publicly and is interested in the topic of using Generative AI to solve real-world business problems as well as the art and science of governing AI.
Common challenges include unclear ownership, inconsistent data definitions across departments, and low buy-in from business users. Another trap is trying to implement everything at once. A phased, use-case-driven approach usually works best for enterprise data governance initiatives.
Early results, like improved reporting accuracy and reduced compliance risks, are often visible in 3–6 months. Larger benefits, such as consistent enterprise-wide data standards, better decision-making, and smoother AI adoption, may take 12–18 months depending on scope.
KPIs include higher data quality scores, reduced time spent fixing or preparing data, improved compliance audit outcomes, and lower risk exposure. Many organizations also track adoption metrics, for example how many teams actively use governed data assets in analytics and decision-making.
Strong governance ensures AI models are trained on accurate, unbiased, and compliant datasets. This process reduces the chance of biased predictions, regulatory violations, or reputational damage. With data governance services in place, AI initiatives become explainable, ethical, and aligned with business goals.
Data governance frameworks are designed to evolve. Regular audits and automated compliance monitoring help spot changes in GDPR, HIPAA, PCI DSS, or other regulations. Governance policies are then updated to keep data use compliant, without slowing down operations or innovation.
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