Conference Day 1: Tuesday 13th October 2026
8:00 am - 8:45 am
Registration & Breakfast
For many data leaders, the challenge is no longer building data products but ensuring they deliver consistent value at scale. As definitions vary and demand grows, questions remain around ownership, reuse, and how to avoid creating more complexity instead of less.
This panel brings together senior data leaders to define what truly differentiates high-performing data products, focusing on the decisions, trade-offs, and structures that enable scale, trust, and long-term impact.
Defining the characteristics that make data products scalable, usable, and trusted across the enterprise
Aligning ownership and product thinking across data, platform, and business teams
Connecting platform, governance, and ownership decisions to measurable business value
8:45 am - 9:00 am
Chair's Opening Remarks
9:00 am - 9:30 am
Opening Panel Discussion: Embedding Data Product Thinking Across the Business to Drive Adoption and Better Decisions
Elcio Abrahao -
Platform Director - Head of Data,
Syngenta
Yang Zhou - Head of Data Engineering & Platforms, DFDS
Many organisations invest heavily in platforms and tools but struggle to realise value due to cultural resistance and misaligned incentives. This panel explores how enterprises manage the cultural shift required to support data products, AI adoption, and new ways of working, particularly across functions that have not traditionally engaged deeply with data.
Aligning incentives and behaviours to support product thinking
Embedding data and AI into everyday decision making
Supporting organisation wide adoption through culture and leadership
9:30 am - 10:00 am
Designing Data Products for a World of AI Agents
Data platforms weren't built for agents that act, decide, and operate on sensitive data. Yet
across many organisations, that is already the reality: data products are being consumed
not just by people, but by AI systems working across domains like people and finance. At
Roche, Rafael Laffarga is rethinking how data products and platforms are designed so they
can support this shift. With a growing catalogue of governed data products and federated
ownership in place, the focus is moving towards making these products usable, traceable,
and safe for both human and agent-driven consumption, without slowing down value
creation across the business. This includes tackling new questions around ownership,
lifecycle, and cost, as well as understanding what agents are interacting with data, how
they behave, and how their actions can be tracked and improved over time.
• Designing data products and platform capabilities that support both human and
agent-driven use cases
• Aligning federated ownership, governance, and lifecycle practices to ensure
consistent, reusable data products
• Implementing observability and identity mechanisms that make AI-driven interactions
traceable and accountable
10:00 am - 11:00 am
Morning Coffee Break & Speed Networking
11:00 am - 11:30 am
Decentralized Ownership, Centralized Trust: Scaling Data Product Management Across a Federation
Sonja Galkin -
Head of Data Excellence & AI Transformation,
Länsförsäkringar
Shifting a decentralised, federated enterprise towards cohesive data platform practices is as
much a cultural and organisational challenge as it is a technical one. To empower business
domains without creating new data silos, organisations must balance global alignment
with local autonomy. True transformation depends on trust. When data consumers trust the
assets available to them, adoption grows; when they do not, teams revert to building isolated
and redundant solutions. Drawing on leadership experience across dynamic customer
channels and highly regulated industries, Sonja explores the realities of scaling data
platforms, establishing trust, and driving data excellence across the organisation.
• Architecting federated governance frameworks that preserve local domain
independence while enforcing global interoperability standards
• Designing for trust: utilizing data catalogues and structured information modelling to
build an intuitive internal data marketplace
• Navigating the human side of scale: transforming privacy, risk, and regulatory
compliance guardrails into proactive, automated platform enablers
11:30 am - 12:00 pm
Making AI and Data Products Work in a Regulated World: Privacy, Risk, and Real-World Trade-offs
Orsolya Salla -
Director - Head of Data Privacy,
Independent
As organisations accelerate AI adoption and scale data products globally, data privacy
and regulatory pressure are moving from a compliance exercise to a core part of how
these systems are designed and operated. What works in one market may not be viable
in another, and decisions made centrally must still be implemented in a way that meets
local legal and regulatory requirements. Orsolya Salla shares how large organisations are
navigating this complexity, working across business, HR, legal, and data teams to enable
AI-driven use cases while managing risk. With evolving frameworks such as GDPR and the
EU AI Act, the challenge is no longer whether to act, but how to balance innovation with
accountability in environments where the consequences of getting it wrong are significant.
• Aligning global data and AI initiatives with local privacy, regulatory, and compliance
requirements
• Embedding privacy, legal, and governance considerations into data product and AI
development from the outset
• Managing risk, accountability, and communication as AI use cases expand across the
organisation
12:00 pm - 1:00 pm
Lunch
1:00 pm - 1:30 pm
Trusted Intelligence at Scale: The New Architecture for AI in the Enterprise
Steve Morgan -
Senior Data Platform Architect,
Starburst
AI models are commoditizing faster than most enterprises expected. The race that
matters now is not which model is best - but for the enterprise intelligence the model can
draw on.
Most organisations operate across data estates that were never built to converge:
mainframes, cloud warehouses, LOB platforms, regional systems, regulatory data stores.
Centralizing everything first is not just slow - it's often structurally impossible due to
technology incompatibility, effort to move & duplicate data, and constraining regulation
such as DORA and GDPR.The instinct to migrate and centralise data before activating AI is
not only stalling effort, it's often causing it be abandoned long before production.
The path forward is different: connect to data where it lives, organize it into trusted,
governed business context, and activate it for every agent, every analyst, and every
analytics workflow that depends on a defensible answer. Gartner named this architecture
at their Data & Analytics Summit in London, May 2026 as the "Context Layer". Not only
is it a core data & analytics infrastructure requirement, but it also foresees demand for
semantic transparency as a governance obligation.
In this discussion, we'll unpack what that looks like in practice:
• How zero-data-movement federation unlocks AI without touching data residency and
sovereignty constraints
• Why organized, governed business context — not raw data access — is what separates
AI at scale from AI in pilot
• How accountability travels with the data: governance embedded at the intelligence
layer, not applied at the storage layer
• What a 45-to-90-day path from conversation to live enterprise capability looks like for
an enterprise ready to move
Steve Morgan
Senior Data Platform ArchitectStarburst
1:30 pm - 2:00 pm
What Makes Data Products Truly Scalable and Worth the Investment
Juan Carlos Vázquez -
Director, Data Platform,
Delivery Hero
Moulisa Bhowmik - Global Head - Master Data Products & Platforms, The Kraft Heinz Company
Sonja Galkin - Head of Data Excellence & AI Transformation, Länsförsäkringar
Jürgen Pannek - Senior Executive — Digital, Data & Commercial, B2B Distribution
For many data leaders, the challenge is no longer building data products but ensuring they deliver consistent value at scale. As definitions vary and demand grows, questions remain around ownership, reuse, and how to avoid creating more complexity instead of less. This panel brings together senior data leaders to define what truly differentiates highperforming data products, focusing on the decisions, trade-offs, and structures that enable scale, trust, and long-term impact.
• Defining the characteristics that make data products scalable, usable, and trusted across the enterprise
• Aligning ownership and product thinking across data, platform, and business teams
• Connecting platform, governance, and ownership decisions to measurable business value
2:00 pm - 2:30 pm
Presentation: From Data Chaos to AI that is Auditable, Explainable & Trusted at Scale
Gabor Harsanyi -
Head of M&CR Data Office,
Ericcson
AI is accelerating across organisations, but data foundations are not keeping up. Tools multiply, teams move independently, and ownership remains unclear, leaving gaps in metadata, quality, and accountability that quietly undermine trust. Gabor Harsanyi explores the reality inside Ericcson in navigating fragmented platforms and parallel data environments, where instead of slowing innovation with AI, the focus shifted to strengthening metadata, embedding governance into everyday workflows, and making ownership visible across teams.
Connecting fragmented data sources through practical metadata and catalogue approaches that expose context and ownership
Embedding governance, privacy, and data quality into workflows so accountability sits with the teams using the data
Shifting conversations with leadership from AI capability to AI accountability through clear, outcome-led storytelling
2:30 pm - 3:00 pm
Presentation: From Teams to Products: Redefining Ownership, Flow, and Collaboration at Just Eat Takeaway
Theo Gough -
Data & Analytics, Head of Strategy, Governance & Experimentation Enablement,
Just Eat Takeaway.Com
At Just Eat Takeaway, data products have become more than a delivery model, they are a way to redefine how teams work together. As demand for data grows, the challenge shifts from building solutions to managing ownership, flow, and accountability across engineering, analytics, and business teams. Theo Gough shares how applying product thinking to data is reshaping the operating model, introducing clearer boundaries, reducing friction, and enabling teams to move from reactive delivery to more structured, collaborative ways of working.
Defining ownership and accountability by introducing data products as bounded units with clear responsibilities and service expectations
Enabling collaboration across engineering, analytics, and business teams through shared contracts and product-led workflows
Managing demand and improving delivery flow by shifting teams from reactive execution to structured, product-driven approaches
Theo Gough
Data & Analytics, Head of Strategy, Governance & Experimentation EnablementJust Eat Takeaway.Com
3:00 pm - 3:30 pm
Afternoon Coffee Break
3:30 pm - 4:00 pm
Panel Discussion: Maintaining Trust, Security and Control as Data Products, Self Service and AI Scale
Agata Reistenbach Oleksy -
Associate Director of Data Engineering,
Brenntag
Gabor Harsanyi - Head of M&CR Data Office, Ericcson
As data products, self service access, and AI adoption expand, governance complexity increases significantly. This panel brings together data, governance, and risk leaders to discuss how governance models evolve to support scale, automation, and regulatory pressure while still enabling teams to deliver at pace.
Scaling governance across products, platforms, and AI workloads
Aligning data, risk, and compliance teams around shared objectives
Maintaining trust and control as data usage expands
4:00 pm - 4:30 pm
Presentation: Data Products as the Foundation for Federated Governance
Andreas Madsen -
Director, Head of Enterprise Data Platforms,
Lundbeck
Investing in modern data platforms to accelerate analytics and AI is now a must, but enterprises are struggling to scale governance as platforms, domains, and teams multiply. The result is often fragmented ownership, duplicated logic, inconsistent KPIs, and limited trust in data.
At Lundbeck, Andreas Madsen is leading the evolution of a federated enterprise data ecosystem built around governed data products rather than platform consolidation. Instead of forcing all workloads onto a single technology stack, Lundbeck is establishing shared governance, semantics, and discovery across SAP, Snowflake, and Microsoft Fabric through a product-centric operating model, enabling federated platforms to operate as a single governed ecosystem.
Establishing clear ownership and accountability by defining data contracts and product boundaries
Enabling shared semantics and reusable logic across domains to reduce inconsistency and duplication
Creating unified discovery and cross-platform governance through marketplace and lineage capabilities
4:30 pm - 5:00 pm
Who Owns the Data? Understanding Key Parameters of Successful Data Products to Improve Quality and Margins
Most data product initiatives do not fail on the platform. They fail quietly on a question
nobody settles at the start: who may use which data, for what purpose, and on what
contractual basis. In a federated distribution network of around 1,200 independent
distributors and more than 3,000 suppliers — none of whom can be instructed — that
question has to be answered before anything can be sold. Jürgen Pannek shares how
a proprietary product classification was developed into a commercial data product
whose revenue and earnings more than doubled, built on a federated architecture with
an explicit chain of rights between data providers and data recipients. The focus is on
what made it sellable: ownership that holds up legally, a value exchange suppliers and
distributors were willing to pay for, and governance measured in margin rather than in
completeness scores.
• Defining data ownership and usage rights that make monetisation possible in the first
place
• Designing data products suppliers and distributors will actually pay for — and
recognising early the ones they never will
• Building governance and platforms that lift margins, not just data quality
