Mandy Chessell

As organisations accelerate the adoption of Agentic AI, governance has become more than a compliance exercise—it is now the foundation for building AI systems that are trusted, scalable and capable of delivering real business value. But as AI moves from experimentation to enterprise-wide deployment, are today's governance models still fit for purpose? This panel brings together leading experts in data governance, AI governance, analytics and enterprise data strategy; to explore how organisations can build trust by design. From human oversight and data quality to semantics, context and organisational culture; the discussion will examine what it really takes to govern AI in complex enterprises. Attendees will gain practical insights into balancing innovation with accountability, enabling AI through governance rather than restricting it, and creating the conditions for trustworthy decision-making at scale. Key Takeaways: • Understand how governance must evolve to support Agentic AI, moving beyond compliance to become a strategic enabler of innovation. • Explore the role of trust, context and data quality in creating AI systems that are reliable, explainable and fit for enterprise use. • Learn practical approaches to scaling AI responsibly, balancing human expertise, governance and business value in complex organisations.

So this is pretty cool. You have just created a set of data products. The pipelines are running and the data is ready ... but no-one is using them. Why not? Taking the consumer's perspective, I will show you 10 top tips on how to attract more people to your data.

When data users choose to circumvent official sources of data, it increases the risks to the organisation. How do you encourage teams to share data widely, effectively and legally? Are data products the answer? We explore the data sharing user journey to highlight the key features of a data product strategy that significantly improves the effectiveness of your data.

Following on from the Building consumable data products keynote, we will dive deeper into the interactions around the data product catalog, to show how the network effect of explicit data sharing relationships starts to pay dividends to the participants. Such as: For the product consumer: • Searching for products, understanding content, costs, terms and conditions, licenses, quality certifications etc • Inspecting sample data, choosing preferred data format, setting up a secure subscription, and seeing data provisioned into a database from the product catalog. • Providing feedback and requesting help • Reviewing own active subscriptions • Understanding the lineage behind each product along with outstanding exceptions and future plans For the product manager/owner: • Setting up a new product, creating a new release of an existing product and issuing a data correction/restatement • Reviewing a product’s active subscriptions and feedback/requests from consumers • Interacting with the technical teams on pipeline implementations along with issues and proposed enhancements • For the data governance team • Viewing the network of dependencies between data products (the data mesh) to understand the data value chains and risk concentrations • Reviewing a dashboard of metrics around the data products including popularity, errors/exceptions, subscriptions, interaction • Show traceability from a governance policy relating to, say data sovereignty or data privacy to the product…