Conference Day 2: Wednesday 14th October 2026
8:00 am - 8:45 am
Registration & Breakfast
8:45 am - 9:00 am
Chair's Opening Remarks
Sonja Galkin -
Head of Data Excellence & AI Transformation,
Länsförsäkringar
9:00 am - 9:30 am
Opening Panel Discussion: Improving Productivity using AI without Increasing Risk
Rafael Laffarga Leo -
Head of Product Data, AI & Digital Employee Experience,
Roche
Elcio Abrahao - Platform Director - Head of Data, Syngenta
Gabor Harsanyi - Head of M&CR Data Office, Ericcson
Juan Luna - Manager IT - Data Analytics, FedEx
Jean-Georges Perrin - Chief Data Strategist, Actian
Data teams are increasingly using AI tools and agents to accelerate development, documentation, and analysis. While productivity gains are real, they introduce new challenges around governance, security, context, and cognitive load. This panel explores how organisations enable data teams to use AI effectively while maintaining quality, control, and sustainable ways of working.
Applying AI safely to improve data team productivity
Managing risk, access, and oversight when using AI tools
Scaling AI usage across teams without increasing fragility
9:30 am - 10:00 am
Presentation: Building Data Products for AI Agents: Lessons from Syngenta's Field-to-Platform Ecosystem
Elcio Abrahao -
Platform Director - Head of Data,
Syngenta
At Syngenta, data products are being produced, enriched, and consumed by AI agents across the full lifecycle, from data captured in the field to insights delivered in real time. This shift is changing how data is structured, how pipelines are built, and how products are ultimately used. Elcio Abrahao explores how AI-driven workflows are being introduced alongside existing data products, including spec-driven development, agent-enabled pipelines, and real-time applications in agriculture. As this evolves, new challenges are emerging around cost, trust, and variability, particularly where non-deterministic AI outputs replace traditional, rule-based approaches.
Applying AI agents across the data product lifecycle to generate, enrich, and consume data in real-world use cases
Managing trade-offs between speed, cost, and control as AI-driven pipelines scale
Evolving data products to support non-deterministic outputs while maintaining trust and usability
10:00 am - 10:30 am
Presentation: Beyond Dashboards: Navigating the Shift from BI to AI-Powered Data Products at IKEA
Tim Stierman -
Digital Product Leader Global CX Platform,
IKEA
IKEA is leading the evolution of customer experience data from traditional dashboards into AI-powered data products that can interpret, surface, and act on insight at scale. Built on multiple data sources including surveys and open text feedback, the challenge is no longer collecting or visualising data, but transforming it into something that can be consumed and trusted in more automated, AI-driven ways.
This speaker dives into the realities of this transition, from inconsistent AI outputs and rising costs to varying levels of adoption across teams. As expectations increase, his focus is on determining where AI genuinely enhances the product, how to maintain quality at scale, and how to move forward without overengineering or overinvesting too early in the maturity curve.
Transitioning from BI-led reporting to AI-powered data products that deliver insight more dynamically
Managing the trade-offs between AI capability, cost, data quality, and user adoption
Evolving data products to remain usable, trusted, and scalable in an increasingly AI-driven landscape
10:30 am - 11:00 am
Morning Coffee Break
11:00 am - 11:30 am
Presentation: Proving the ROI of Data Products in a World That Demands Results
Nick Zervoudis -
Data Product Management Consultant & Coach,
Value From Data & AI
Data teams are under increasing pressure to justify their work as organisations demand clearer links between data investment and business outcomes. Yet many teams still operate in delivery mode, measuring success through outputs rather than impact, leaving CDOs and product leaders struggling to demonstrate tangible ROI or secure continued investment.
Nick Zervoudis explores how to shift from activity to value by identifying high-impact opportunities, translating data initiatives into financial terms, and embedding commercial thinking into data product strategy so teams can consistently prove their relevance and influence.
Quantifying financial value by linking data products to revenue, cost, and risk outcomes
Prioritising opportunities based on measurable ROI rather than technical feasibility
Positioning data teams as commercially focused partners driving business performance
11:30 am - 12:30 pm
Interactive Workshop: From Data Product to Business Value
Alexandre Sidler -
Senior Presales Engineer,
Actian
Enterprise data is only valuable when people can find it, trust it and put it to work. In this interactive, hands-on workshop, participants will explore the journey from discovering data across a complex enterprise landscape to creating and consuming trusted, governed Data Products that support real business needs.
Working through realistic banking scenarios, participants will tackle practical challenges around data discovery, ownership, lineage, trust and impact analysis. Rather than watching a traditional product demonstration, they will work directly with the platform and collaborate to define what makes a Data Product both valuable to the business and trustworthy from a governance perspective.
The workshop will also explore how governed Data Products can provide the trusted context needed for AI-powered data consumption. Participants will leave with a practical understanding of how Data Products can make enterprise data easier to find, understand and reuse, helping bridge the gap between complex data environments and measurable business value.
Please note that this workshop will require participants to bring their own laptops.
12:30 pm - 1:30 pm
Networking Lunch
1:30 pm - 2:00 pm
Fireside Chat: What Breaks at Scale: Lessons from Growing Data Product Portfolios
Agata Reistenbach Oleksy -
Associate Director of Data Engineering,
Brenntag
Sonja Galkin - Head of Data Excellence & AI Transformation, Länsförsäkringar
Scaling data products exposes issues that rarely appear early on, from ownership gaps and governance strain to cost visibility and cultural resistance. This intimate fireside chat brings together enterprise leaders to share practical lessons from scaling data products and platforms, focusing on what breaks first, what requires redesign, and how organisations adapt operating models as scale increases.
Identifying early warning signs that limit scalability
Adapting ownership and governance models as portfolios grow
Learning what successful organisations change as scale increases
2:00 pm - 2:30 pm
Presentation: Generative AI in Data Products: Redefining Roles, Workflows, and Talent
Stefania Bonà -
Senior Director, Open Banking Payment Products,
VISA
As generative AI becomes embedded into data products and development workflows, the boundaries between engineers, analysts, and product teams are quickly starting to blur. Tasks that once required specialist skills are becoming easier to execute, enabling teams to prototype faster, iterate more frequently, and experiment at scale, but also creating new complexity around quality, consistency, and ownership. Stefania Bona shares how generative AI is being integrated into the data product lifecycle, from accelerating development to reshaping how teams work together, and how organisations are navigating the shifting expectations around roles, skills, and talent in an AI-driven environment.
Applying generative AI within data product development to accelerate prototyping and iteration
Navigating the shifting boundaries between engineers, analysts, and product teams as roles evolve
Rethinking talent strategies and team structures to balance speed, experimentation, and quality
2:30 pm - 3:00 pm
Presentation: Moving Beyond Legacy: Evolving Data Platforms to Support Scalable Data Products
Agata Reistenbach Oleksy -
Associate Director of Data Engineering,
Brenntag
Many enterprises operate across fragmented data landscapes shaped by legacy systems, regional solutions, and inconsistent standards. As demand for data products and AI grows, these environments make it harder to deliver data that is consistent, reusable, and scalable.
Agata Oleksy explores how data engineering teams are introducing more structured data platforms, moving towards standardised, self-service capabilities while redefining what a data product means in practice. This includes aligning ownership with business context and creating platforms that are both usable and sustainable at scale.
Standardising data platforms and engineering practices to reduce fragmentation across regions
Defining data products in a way that reflects real business use cases rather than theory
Enabling self-service adoption by balancing platform design, usability, and behaviour change
3:00 pm - 3:30 pm
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 customchannels 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
3:30 pm - 4:00 pm
Afternoon Coffee Break
4:00 pm - 4:30 pm
Data Products Don't Live in the Cloud: Building Them from the Factory Floor
Most data product conversations start in the cloud. In manufacturing, they start next to a machine, in MES, SCADA, historians and sensor data that was never designed to be shared, governed or understood outside the plant. This session covers what it really takes to turn operational technology (OT) data into trusted, reusable data products: contextualising machine data, bridging OT and IT ownership, and designing a data foundation that lets AI move from isolated plant pilots to governed, scalable production. Drawing on 28 years across PepsiCo, P&G and HP, and on current advisory work in Industry 4.0, Mario Morales will share a practical framework and the common failure points that keep industrial AI stuck in pilot mode.
4:30 pm - 5:00 pm
Roundtable Discussion: From Discussion to Action, Turning Data Product Strategies into Real Change
Nick Zervoudis -
Data Product Management Consultant & Coach,
Value From Data & AI
Over the past two days, leaders have explored how organisations are building data products, scaling platforms, embedding governance, and enabling AI. The challenge now is turning these insights into something tangible, translating ideas into actions that can be applied within real organisational constraints.
Led by Nick Zervoudis, this roundtable brings together participants to reflect on key learnings from the Summit and identify what this means for their own teams, platforms, and operating models. Through structured discussion and peer exchange, the focus is on cutting through complexity and leaving with a clearer sense of direction.
• Translating key insights from the Summit into practical actions relevant to your organisation
• Identifying priority areas for change across data products, platforms, and operating models
• Sharing approaches with peers to validate ideas and challenge assumptions before implementation
