🧭 Are you a CDO, Data Governance/Management Lead, or Data Architect working to strengthen data lineage—while still facing questions about where to start, how to implement it realistically, or which tools truly match your maturity?

After the success of the second Q&A session, we’re introducing a new format: we will select one or two real challenges from participants and work together to find solutions.

This interactive session focuses on the issues practitioners raise most often—practical starting points, feasible implementation options, and how tool choices influence both hop-based and end-to-end lineage.

🎯The goal of this session is:

To reflect on how organizations approach the early steps of lineage, choose feasible implementation methods, and evaluate tooling when metadata is fragmented, automation is limited, or expectations exceed reality.

🔊 This is an interactive session—not a one-way presentation.

Participants are expected to come prepared with questions and be ready to share their own experiences.

The value of this session comes from open exchange, not passive listening.

💬 Proposed discussion topics include:

• Practical starting points for lineage, including MVP approaches and selecting the right level of detail

• How organizations implement lineage in real conditions—fragmented metadata, limited automation, and complex environments

• Why hop-based lineage creates blind spots and how teams move toward clearer end-to-end visibility

🎯 Expected key takeaways:

• A clearer understanding of where to begin the lineage journey, using MVP-level approaches and realistic detail levels that fit organizational maturity

• Practical insight into implementing lineage when metadata is fragmented, automation is limited, or environments are complex—focusing on what works in real conditions

• A better grasp of the limitations of hop-based lineage and how organizations progress toward end-to-end visibility that supports governance, risk, and business needs

Organised by Data Crossroads

🔎 AI regulations are multiplying—fast—but clarity isn’t.

Every major jurisdiction defines, scopes, and enforces AI differently. The result? A complex maze of overlapping, sometimes conflicting requirements that challenge even the most mature governance teams.

📘 In this Live Talk, we’ll explore how organizations can make sense of today’s fragmented AI regulatory landscape—and define a realistic path to compliance.

We’ll look at how regulations differ in their scope, legal force, and compliance models, from the EU AI Act to frameworks across North America, Asia, and beyond.

📈 You’ll gain a clear picture of:

• The key regulatory differences shaping global AI governance

• What “risk-based” and “principle-based” compliance really mean in practice

• How to align your internal governance framework to manage diverse legal expectations

🧭 Whether you’re defining your AI policy or adapting existing data governance frameworks, this session will help you find a balanced, practical route through the complexity of AI compliance.