🧭 Are you a CDO, Data Governance/Management Lead, or Data Steward working with Critical Data—and still struggling with where to start, how to implement realistically, or how to connect lineage, metadata, and tooling without overengineering?

After the strong engagement in previous DC Q&A sessions and multiple community polls on Critical Data and lineage challenges, we’re continuing with the next interactive Q&A session—focused on real-world Critical Data problems and feasible solutions.

We will focus on one or two real challenges submitted by participants, which will be selected and explored together during the session.

🎯The goal of this session is:

To reflect on how organizations identify and manage Critical Data, design feasible lineage and metadata approaches, and make informed tooling decisions—especially when metadata is fragmented, automation is limited, and expectations exceed what is realistically achievable.

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

Participants are expected to come prepared with questions, examples, or challenges from their own organizations.

💬 Proposed discussion topics include:

• Practical starting points for identifying and managing Critical Data, including MVP-style approaches and realistic scoping

• How organizations work with Critical Data in real conditions—fragmented metadata, partial lineage, limited automation, and complex landscapes

• How lineage approaches (hop-based vs. end-to-end) affect Critical Data visibility, governance decisions, and risk management

🎯 Expected key takeaways:

• A clearer understanding of how to start working with Critical Data using pragmatic, maturity-aligned approaches

• Practical insight into managing Critical Data when tooling, metadata, and lineage are incomplete or inconsistent

• A better grasp of where hop-based lineage falls short for Critical Data—and how organizations gradually move toward more meaningful end-to-end visibility

💬 If you’re dealing with Critical Data in the real world—not in idealized frameworks—this session is designed for you.

Organised by Data Crossroads

🔎 AI frameworks are everywhere—but direction is missing.

Organizations are confronted with a growing number of AI industry frameworks, guidelines, and best-practice models, each promising responsible, trustworthy, or scalable AI.

📉 The real challenge is not the lack of frameworks—it’s the overload.

Most frameworks focus on specific angles such as risk, ethics, controls, or lifecycle management. They overlap, use different terminology, and assume different levels of maturity, leaving teams uncertain about where to start and how to combine them meaningfully.

📘 In this Live Talk, we’ll explore how to navigate the AI framework landscape with purpose.

The session will clarify how major industry and policy-driven frameworks differ, how they relate to regulations and internal governance, and how organizations can select and position frameworks based on their context, ambitions, and maturity.

📈 You’ll gain clarity on:

• The role AI industry frameworks play alongside regulations and internal policies

• How to assess frameworks without trying to adopt everything at once

• How to build a coherent governance path instead of a patchwork of controls

🧭 This session is designed to help you move from framework confusion to informed choice—and define a practical path that fits your organization rather than copying someone else’s model.