Day 1 - Tuesday 19th January 2027

Dashboards, datasets and models create little value when they sit outside the moments where work happens. Learn how organisations are designing data products around decisions, operational processes and customer outcomes rather than technical deliverables.

• Starting with the decision, process or outcome the product must improve

• Embedding products into established workflows and digital experiences

• Measuring impact through changed behaviour and business outcomes

Kate Sargent

Chief Data Officer
Financial Times

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Low adoption is often blamed on culture, but the real causes may include weak product discovery, unclear value, poor workflow fit and insufficient ownership. This panel explores how teams are earning sustained use rather than simply driving launch activity.

• Identifying the organisational and product barriers behind low adoption

• Treating internal users as customers without oversimplifying enterprise needs

• Building feedback, enablement and continuous improvement into the lifecycle

David Pires

Director, Analytics Platform and Products
Expedia Group

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As Data Science and AI Innovation Lead at Just, Merina Su is helping the business pursue new AI opportunities while its data estate, governance and federated delivery model are still evolving. Drawing on the practical challenges of connecting central technology teams with business-led delivery, Merina will explore what it takes to move beyond promising pilots and embed data and AI into lasting business workflows.

• Breaking down organisational and data silos to enable trusted enterprise use cases

• Aligning central standards and platforms with the needs of business-led teams

• Moving AI and data products beyond proofs of concept into production

• Defining success through operational efficiency, business growth and measurable adoption

Merina Su

Data Science & AI Innovation Manager
Just Group

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Calling an internal platform a product is easy. Operating it like one requires clear customers, service expectations, prioritisation and evidence of value. Platform leaders share what changes when product management becomes part of the platform model.

• Defining platform customers, journeys and service promises

• Balancing enterprise standards with domain and engineering needs

• Measuring adoption, developer experience, reliability and business impact

Supraja Russell

Former Global Product Director, Data Catalogue & Metadata
P&G

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Moving thousands of products across hundreds of suppliers and more than 100 markets requires a connected data ecosystem that can turn changing demand into better decisions.

Drawing on a portfolio of 50+ analytical applications and use cases, Kiran will share how IKEA is building trusted, reusable data products that connect forecasting, inventory, supply planning and fulfilment.

• Moving beyond dashboards to support operational workflows and decisions
• Connecting demand, inventory, availability and fulfilment across the supply chain
• Applying AI and advanced analytics to improve forecasting and accelerate decision-making
• Balancing availability, cost, speed, capacity and sustainability
• Building reusable products that can scale across markets and adapt to disruption

Kiran Dulal

Data Product Leader
IKEA

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As the number of products grows, governance must extend beyond compliance to investment and lifecycle decisions. This panel explores how enterprises balance local demand with portfolio value, risk, duplication and long-term maintenance.

• Establishing transparent decision rights and prioritisation criteria

• Comparing persistent product value with cost, risk and strategic importance

• Making difficult consolidation and retirement decisions

Saif Arshad

Director, Commercial Finance Data Governance
S&P Global

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Chris Mackenzie Ross

Data Product Manager & Delivery Lead
LSEG

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AI is making it faster than ever to prototype bespoke analytics experiences and show customers new ways of understanding their supply chains. But a compelling front end is not yet a scalable data product. Behind every personalised view sit difficult questions around data availability, engineering capacity, governance, security and whether the insight can improve real operational decisions.

Drawing on experience developing customer-facing products at Maersk, Parth will explore how teams can harness AI-enabled prototyping without overpromising, and turn individual customer ideas into governed analytics products that can scale.

• Using AI to accelerate customer discovery and prototype bespoke analytics experiences

• Creating safe environments for experimentation without generating an unmanageable backlog

• Balancing customer customisation with reusable product foundations, governance and security

• Scaling data engineering when demand for personalised products grows faster than delivery capacity

• Building applied analytics that helps customers anticipate disruption and move goods more reliably and cost-effectively

Parth Kapoor

Data Product Lead
A.P. Moller – Maersk

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