Australia EN

Use Your Data Governance Framework to Boost Practical AI Governance

Marc van Dongen

Associate Director – Technology , Synechron, The Netherlands



By 2025, it is projected that the majority of financial services industry businesses worldwide will incorporate artificial intelligence (AI) into their operations. This would mark an unprecedented rate of adoption for such a transformative technology.

This trend is particularly remarkable considering the significant financial, legal, and reputational risks associated with AI. Despite its current nascent stage, the application of AI is expanding swiftly. Yet, the absence of a standardized framework for managing AI usage poses a challenge. What regulations should govern AI, and how can businesses ensure its safe and effective deployment?

A solution may already exist. Synechron's AI Governance proposition centers on expanding the existing data governance framework to rapidly establish dependable governance of AI.


Data Governance Frameworks

AI governance effectively extends the principles of data governance, which is the management of data availability, usability, integrity, and security within enterprise systems. Both frameworks share a commitment to policies, procedures, and practices that ensure proper data handling throughout its lifecycle. Since AI technologies are deeply rooted in data, the fundamentals of data governance play a critical role in shaping the development, deployment, and operation of AI systems. A robust data governance framework guarantees the accuracy, consistency, and security of the data used by AI algorithms, thus improving the reliability and performance of AI applications.

Nevertheless, while the two frameworks share similarities, responsible AI usage introduces new governance standards, expanding on data governance to cover ethical AI use, algorithm transparency, sustainability, and accountability. From a strategic standpoint, AI governance requires closer alignment with an organization's ethical beliefs and innovation goals to harness AI technologies effectively.

On the financial front, AI governance necessitates intricate risk evaluations concerning algorithmic bias, decision-making clarity, and adherence to AI-specific regulations, such as the recently approved EU AI Act from the European Parliament. FinOps - an operational framework and cultural practice that maximizes the business value of the cloud, enables rapid data-driven decision making, and creates financial accountability through collaboration between engineering, finance, and business teams - already concerned with optimizing business value, will place greater emphasis on Return on Investment (ROI) due to AI's higher energy demands. This shift is not just for budgetary reasons but also to meet regulatory sustainability goals outlined in Environmental, Social & Governance (ESG) criteria.


Key Dimensions of AI Governance

Organizations must consider these additional dimensions of AI governance -- affecting staff, processes, and technology -- using their data governance practices as a base. The focus should be on six key target areas:

  1. Data Lifecycle Management: Vital for AI's reliance on quality data, ensuring accurate, consistent, and secure data management.
  2. Foundation for AI Governance: Establishes the groundwork for AI governance through clear data management policies and procedures.
  3. Ensuring Reliability and Relevance: Directly impacts AI system effectiveness by guaranteeing data integrity and relevance.
  4. Compliance and Ethical Considerations: Incorporates legal and ethical compliance, crucial for ethical AI use and decision-making.
  5. Risk Management: Identifies and mitigates risks related to data and AI, including privacy and security breaches.
  6. Scalability and Futureproofing: Ensures AI systems can adapt and scale with organizational and technological growth.


Importance of Data Governance for AI

The interdependence of data and AI governance underscores the significance of comprehensive data lifecycle management and the establishment of a robust foundation for AI governance. As AI becomes more entrenched in organizational processes, the importance of data governance in ensuring trustworthy and efficient AI systems will only grow.

The expanded AI-data governance framework should show immediate value. Instead of a sweeping implementation, a gradual roll-out, beginning with high-priority areas is advisable, particularly in regulatory compliance. From a business adoption point-of-view, taking on hot topics like use case rationalization and data set selection can be interesting, as they need motivated business involvement. Starting with a readiness assessment will help prioritize key AI items. Performing a gap analysis on them and defining relevant measures will stepwise integrate AI governance into existing data governance practices effectively.



  1. AI Governance Readiness Assessment

Before embarking on the journey from data governance to AI governance, organizations need to assess their current capabilities and readiness levels. Synechron employs a combination of proven qualitative and quantitative techniques to perform this comprehensive assessment. This involves:

- Surveys and Interviews: Engaging with a wide range of stakeholders within the organization is key to gathering insights into current data and AI practices, challenges, and aspirations.
- Technology Audits: Reviewing existing IT infrastructure, software, and data storage solutions enables evaluation of how well they can support AI initiatives.
- Skills and Competency Evaluation: Assessing the current level of AI and data science expertise within the organization allows for identification of skill gaps and planning for necessary training or hiring.
- Ethical and Legal Review: Examining current policies and practices for adherence to ethical standards and legal requirements related to AI and data usage is important.
  1. Gap Analysis

The readiness assessment culminates in a detailed gap analysis that highlights both the strengths and weaknesses of the organization's current state in relation to AI governance. This analysis is pivotal for identifying critical areas for improvement and involves:

- Identifying Technological Shortfalls: Pinpointing limitations in the existing IT and data infrastructure that could hinder AI deployment, such as inadequate data quality, lack of scalable storage solutions, or insufficient computing power is critical.
- Policy and Framework Evaluation: Assessing existing data governance policies to identify areas where they may fall short in addressing AI-specific concerns, including data privacy, model transparency, and accountability measures is valuable.
- Expertise Mapping: Highlighting areas where personnel expertise needs to be bolstered to support AI initiatives, whether through training current staff or recruiting new talent is important.
- Ethical Considerations: Identifying any ethical blind spots in current practices, such as biases in data sets or algorithms, and proposing strategies to address them is a savvy step.
  1. Transitioning from Data Governance to AI Governance Framework

With the insights gained from the gap analysis, organizations can then develop a tailored AI governance framework that builds upon their existing data governance foundations. This involves:

- Risk Management Protocols: Establishing clear guidelines for identifying, assessing, and mitigating risks associated with AI projects, including the implementation of robust data quality controls and ethical guidelines for AI development and use is necessary.
- Policy Development and Update: Crafting or revising policies to address AI-specific challenges, ensuring that they cover data privacy, security, ethical AI use, model governance, and transparency is key.
- Personnel Development Plan: Creating a comprehensive plan for upskilling existing staff and attracting new talent with the necessary AI expertise should not be overlooked. This may include partnerships with academic institutions, participation in industry conferences, and investment in online and in-person training programs.
- Technology Upgrade Plan: Outlining a roadmap for technology investments needed to support AI initiatives, such as advanced analytics platforms, cloud computing resources, and AI development tools is smart.
- Ethical AI Guidelines: Developing specific guidelines for ethical AI use, including fairness, transparency, accountability, and privacy considerations should be done. This might involve setting up an ethics board or committee to oversee AI projects and conduct regular reviews.

The Author

Rachel Anderson, Digital Lead at Synechron UK
Marc van Dongen

Associate Director – Technology, Enterprise Architect

Marc van Dongen is an Associate Director of Technology at Synechron, The Netherlands.

Marc is a Senior Enterprise Architect with expertise in various areas including Enterprise AI Governance, Data & Analytics (D&A), improving architectural efficiency, and enhancing Security Architecture, among others. He is a trusted partner for client challenges related to regulatory compliance, evidence reporting & lineage, Governance, Risk, and Compliance (GRC), agile architectures and efficient architecture strategies. Marc holds a range of certifications such as a Master of Science in Informatics, AI Oversight, AI Analytics Lead, The Open Group Architecture Framework (TOGAF), Data Management Association (DAMA) Associate, Open Group’s IT4IT Service Management, Open Agile Architecture, certifications in Cloud & Security Architecture, Lean Six Sigma (Black Belt), and Scaled Agile Architecture.

For more information, reach out to him at:

Or connect with him on LinkedIn:  linkedin

See More Relevant Articles