The data product challenge - Feeding AI with trusted, industrial-scale data

Presentation: From data platform to data products: Making data usable at scale 
As CSPs scale AI across operations and enterprise services, the focus is shifting from building data platforms to delivering trusted, reusable data products. This session explores how operators are structuring, governing, and operationalizing data to enable AI at scale. 

• Build data products that are owned, governed and trusted by design 
• Move from fragmented data lakes to data value chains 
• Standardise data across domains to enable interoperability at scale 

Case study: From siloed data to AI-ready products: Lessons from production deployment 
More details coming soon. 

Panel: Fix the data layer or fail at AI: What needs to change now? 
Despite major investment in AI, many operators still struggle to scale due to fragmented, inconsistent, and untrusted data. This panel explores what is really holding CSPs back - and what it takes to build data foundations that support AI-native operations at scale. 

• What ‘trusted data’ looks like in practice 
• Where fragmentation still breaks cross-domain orchestration 
• Who owns the data – and who is accountable for quality 
• What changes when data becomes a product, not an asset 

Case study: Build vs federate: sourcing and sharing data across ecosystems 
​​​​​​​More details coming soon.