Confidence in Data Leadership Training

Lead with clarity, control, and foresight. This training enables leaders to unlock business value, ensure compliance, and build trusted foundations for AI adoption in a rapidly evolving global data ecosystem.

Data has been dubbed as “the new oil”, employing a metaphor to highlight its importance to power businesses as well as its risks to get burned and pollute its environment. While there is an awareness of the value of data, especially in the age of AI, it also has been recognised that data can be sensitive, costly to maintain, and negatively impact the interests of individuals, business, and society. It is estimated that data accounts for 40% of overall data centre costs, while over 80% of that managed data is considered stale as it does not yield any business value.

 

While data legislation has historically been perceived as an economic burden and innovation inhibitor, recent strategic policy shifts aim to reduce barriers of data sharing to benefit society. For example, the EU Data Act, which was enforced in September 2025. It increases the incentives of data holders to voluntarily enter into data sharing agreements, reduces the legal uncertainty, levels imbalances in power, and mandates technical interoperability.

Objective

This course condenses the knowledge leaders need to navigate data strategy in the age of AI. It provides a concise overview of the global data ecosystem, current and emerging policies, and practical guidance to identify business opportunities, assess risks, and validate strategic decisions. The focus is on strategic decision-making, not technical execution, empowering executives to guide their organisations with confidence.

Training Outline

Data Strategy and AI Governance Training Agenda
Topic Description
Introduction and motivation (20 minutes)
  • Why this matters now?
    • Revenue enablement vs. regulatory friction
    • How data laws impact GTM, EBIT, and AI
  • Understand the opportunity
    • Dataspaces as controlled ecosystems for trusted data sharing across organizations
    • Market drivers and business models
Core concepts of the data ecosystem (30 mins)
  • Ecosystem stakeholders
  • Data Technology
  • Trusted Data Architecture
  • Interoperability (Discovery, Negotiation, Data sharing)
  • Intermediary (Rights holder, Producer, Provider)
  • Data product (Digital product passport, Metadata, Operations)
Regulatory landscape, policy approaches and industry best practices (120 mins)
  • EU regulatory requirements and compliance obligations (AI Act, Data Act, GDPR, Cyber Resilience Act, Free Flow of Non-Personal Data, NIS2 Directive, Digital Strategy)
  • USA patchwork of state and sectoral federal legislation (CCPA/CPRA, HIPAA, GLBA Safeguards Rule)
  • China governance of personal information (PIPL), Data Security Law (DSL), Cybersecurity Law (CSL), Cross-border data transfer regime, Standard contract measures, and recent friction reduction exemptions
  • Singapore's data governance and policy approaches (PDPA, Cybersecurity Act)
  • International best practices (ISO/IEC Data Standards, OECD Privacy Guidelines, APEC Privacy Framework)
Data opportunities and costs (40 mins)
  • Data intent
  • Data management best practices
  • Cost reduction
The data trust architecture (30 mins)
  • Data quality and assurance
  • Security frameworks
  • Operational aspects: Participant onboarding, contract enforcement, dispute resolution
Data risk mitigation (30 mins)
  • Quantify risk appetite and strategic alignment
  • Incident response and crisis management
Practical data governance use-case exercise (40 mins)
Develop and validate a compliance strategy (30 mins)
  • Gap assessments
  • Maturity model self-assessment
  • Internal audits
  • External validation
Summary and Q&A (20 mins)

What You Will Earn

  • Clear understanding of key roles, responsibilities, and terminology related to data and dataspaces

  • Strategic awareness of data ecosystems and business value of trusted data sharing

  • Insight into regulatory principles and global best practices

  • Actionable strategies to assess organisational maturity, investment priorities, and risk

  • Practical guidance to draft, validate, and improve your data strategy

Who Will Benefit

This training is ideal for leaders shaping data and AI strategy in their organisations:

  • CIOs, CDOs, and Chief AI Officers

  • AI governance leads, compliance officers, and risk managers

  • CTOs, DPOs, and data protection leads

  • Strategy, procurement, and transformation executives

Practical Training Set-up

  • Duration: 1 full day or 2 x half days

  • Delivery Options: Virtual instructor-led or on-site in Singapore

  • Led by Senior Thought Leaders: Active contributors to the global regulatory and data governance ecosystem

Learn from the People Who Help Build AI Governance

This course is taught by specialists who work with AI governance, quality, and risk every day. They contribute to international standards, assess high-risk systems, and support organisations that need quality, reliant, and compliant AI initiatives.

Dr Martin Saerbeck

CTO and Co-Founder of AIQURIS with long experience in AI system safety, quality, and regulatory compliance. He contributes to ISO and IEC committees and has led assurance work within TÜV SÜD. His work supports sectors that rely on strict requirements for audit readiness, data quality, and accountable AI deployment.

Dr Yao Cheng

Principal AI Expert with a background in cybersecurity and trustworthy AI. She is a qualified TÜV SÜD AI Quality Trainer and an IEEE CertifAIEd Lead Assessor. She evaluates AI systems for ethical and technical criteria and participates in Singapore’s national AI standards committees.

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