CDAO Nordics 2025 schedule

As the industry shifts from the era of massive foundation models toward smaller, domain-specific systems, small language models (SLMs) are emerging as a powerful alternative — offering efficiency, control, and scalability for real-world enterprise use. 

• Understanding what small language models (SLMs) are and how they differ from large-scale LLMs. 

• Why SLMs may be the key to more efficient, private, and enterprise-aligned AI. 

• Insights from recent NVIDIA research and what it signals for the future of AI infrastructure. 

• How executives can prepare their organisations to leverage SLMs for competitive advantage. 

This panel explores how enterprises can align AI, digital transformation, and data strategy into a unified engine of business growth and resilience. 

• Rethinking the role of data in a platform-led enterprise 

• Breaking down silos to drive cross-functional collaboration and shared KPIs 

• Ensuring responsible and explainable use of AI in complex environments 

• Enabling real-time, insight-led decision-making at all levels 

• Creating a scalable, data-driven culture in legacy-rich industries 

Moderator: Divya Panicker, Head of Data Analytics and AI – ORIOLA 

Luba Weissmen, AI & Data Director – EPIROC 

Henrik Tingwall, Head of Lean PMO and Digital – APOTEKET

Everyone’s talking about AI, but few organizations have truly succeeded in embedding it into mission-critical enterprise systems. Drawing on years of experience integrating AI capabilities into master data management, Eric Melcher, Chief Technology Officer at Profisee, offers an honest, practitioner-level perspective on both the promise and the reality of enterprise AI adoption. 

This keynote shares candid insights on: 

• Lessons learned — relevant to any data leader — from deploying AI across the product and engineering lifecycle. 

• The gap between boardroom AI expectations and operational reality, and why Profisee isn’t focused on measuring productivity gains (yet). 

• The critical missing pieces between where enterprises are today and the “agentic” AI future everyone is talking about. 

• How master data and structured data are becoming essential prerequisites for many AI initiatives. 

Rather than a vendor pitch or philosophical discussion, this is a peer-to-peer session connecting the dots between Master Data Management (MDM) and real-world GenAI results, offering practical lessons and reflections for data and technology leaders shaping their own AI strategies. 

Retail and consumer-facing organisations are experimenting with AI and GenAI to enhance customer experience, streamline operations, and accelerate decision-making. This discussion will explore practical examples and lessons learned from early adopters — across industries — using AI to deliver measurable business impact. 

Key Discussion Points: 

• Personalising customer experiences at scale 

• Optimising supply chains and operations with predictive analytics 

• Using GenAI for smarter content, marketing, and engagement 

• Balancing innovation with governance and privacy 

• Scaling AI use cases beyond proof of concept 

Rakesh Rana, PhD, Head of Data – HAYPP GROUP 

This session explores how organizations are empowering business units to make informed, agile decisions by embedding data across all workflows. 

• Aligning data with shifting business priorities in uncertain times 

• Creating fast feedback loops between insight and action 

• Making data accessible to decision-makers beyond the data team 

• Enabling line-of-business leaders with guided analytics 

• Identifying and scaling “quick-win” use cases 

Erica Campbell, Global Consumer Data & Advanced Analytics Director – Electrolux

Every organisation is trying to balance speed, trust, and cost as data environments become more complex. This discussion explores how leaders are modernising their data foundations to deliver reliable insights at scale — without getting lost in the technical weeds. 

Key Discussion Points: 

• Building a flexible data foundation that can evolve with business needs. 

• Making data architecture decisions that balance cost, performance, and agility. 

• Embedding governance and trust without slowing down innovation. 

• Lessons learned from moving to modern, cloud-optimised platforms. 

• The leadership challenge: aligning business priorities with technical execution. 

Moderator: DREMIO 

Yamur Al-Douri, Senior Project Manager Research and Development – SCANIA 

Roundtable: The Role of Data Mesh in Enabling Scalable, Cross-Functional Innovation 

Explore how enterprises are transitioning to domain-driven, decentralized data architecture models while maintaining alignment and governance. 

• Implementing domain-oriented ownership of data products 

• Overcoming cultural barriers in adopting a federated model 

• Aligning data mesh with legacy systems and cloud migrations 

• Setting standards for interoperability and discoverability 

• Lessons from pilot programs: metrics, adoption, and resistance 

Ulf Holmstrom, Lead Data Scientist – SCANIA 

Hajar El Hanaf, Data Transformation Lead – ELECTROLUX GROUP 

Mohammad Reza Babaee, Senior Product Manager, Data Products & Platform – DELAVAL

To drive consistent business impact, data teams need more than just a modern stack — they need a clear, structured operating system. In this session, Pier Martin will explore how embedding human-centered principles and soft skills into your data culture can radically improve ROI. From real-world day-to-day team challenges to practical leadership strategies, he’ll offer insights and immediate actions for building stronger, more effective data functions. 

• Data Teams Need an Operating System—Not Just a Stack: Why focusing only on tools and technology misses the mark, and how process, mindset, and communication routines create consistency across data functions. 

• Soft Skills as a Multiplier for ROI: How skills like storytelling, empathy, and influence-building amplify technical output and help data teams embed more deeply into business strategy. 

• Prioritization as a Leadership Capability: Why leaving prioritization to tooling or backlog grooming is a mistake—and how data leaders can guide their teams by aligning decisions to business impact. 

• From Insight to Action: Common roadblocks data teams face and practical changes leaders can implement immediately to improve alignment, output, and morale. 

• About Pirkanmaa Wellbeing Services County: Serving over half a million citizens with health, social, and rescue services, supported by 20,000 employees. 

• Data at the Core: These services generate vast and diverse data that, when managed effectively, can unlock real value for both operations and citizens. 

• Enabling Data-Driven Operations: Success requires addressing the full chain — from data collection to refinement, governance, and meaningful utilization. 

• Knowledge Management in Action: Practical examples and approaches for turning raw service data into actionable insights that improve decision-making, efficiency, and service quality. 

• What leaders must do now to future-proof AI and analytics teams 

• Embedding risk management into AI-driven transformation 

• Striking the balance between innovation and accountability 

• Managing AI risks and compliance in highly regulated industries 

• Building governance frameworks that enable innovation without losing control 

• How evolving regulation impacts the structure and role of data/AI teams 

Ashish Soni, Head of Data Science & Analytics, Financial Crime Prevention – SEB 

Gabor Harsanyi, Head of M&CR Data Office – ERICSSON 

• Initial scepticism and what changed your mind on Data Governance lead role 

• The hiring process and role definition for the data governance lead  

• Concrete examples of how this integration improved organizational awareness  

• The cohesive relationship between data, architecture, and business outcomes  

• Key lessons learned and recommendations for other organizations Challenges faced and how you overcame them 

Jessica Mellgren, Chief Enterprise Architect – BILLERUD

Building a Centralized Data Lakehouse in the Cloud at NOBA Bank Group 

• Our Merger Challenge: Integrating and harmonizing data assets from two organizations. 

• Why the Cloud: Drivers for moving to a scalable, secure, and flexible cloud-first architecture. 

• Centralized Data Lakehouse Approach: Balancing structure (data warehouse) with flexibility (data lake) to meet diverse business needs. 

• Moving to the cloud to unify and modernize the data foundation. 

• Ensuring quality, governance, and scalability in the process. 

• Preparing the groundwork for advanced analytics and AI. 

Description: 
As GenAI becomes embedded across organizations, it’s redefining how data is owned, accessed, and acted upon. This roundtable brings together data and AI leaders from across sectors to discuss how to balance innovation with accountability, ensure ethical ownership of insights, and futureproof governance structures. 

Discussion Points: 

• Who owns the value created by GenAI? Business, tech, or the customer? 

• How is GenAI shifting the role of the CDO, CIO, and business leaders? 

• Empowering product and domain teams to responsibly use LLMs 

• The rise of internal data marketplaces — trend or transformation? 

• How to avoid decentralisation chaos: platform strategy, trust, and standards 

Ramakrishna Poojari, Head of Group Data Strategy and Data Transformation Office – SWEDBANK 

Luba Weissmen, AI & Data Director – EPIROC 

Hajar El Hanaf, Data Transformation Lead – ELECTROLUX GROUP 

Divya Panicker, Head of Data Analytics and AI – ORIOLA