Trends, insights, and practical guidance on how trusted data and AI shape decision-making.
Gartner® identifies the absence of a dedicated context layer as a major contributor to the value gap between AI investments and outcomes. Find out what they are and why real-world context is necessary to drive reliable and trusted decisions from AI agents.
Long before 'context' had a name, we were building the entity resolution and knowledge graph technology that makes AI trustworthy, at the scale the world's most regulated institutions demand.
Overcome fragmented, siloed customer data challenges and reach AI ambitions with our practical blueprint for placing master data at the centre of your bank to support priorities across operations, compliance, and growth.
Fragmented data is banking’s biggest obstacle to delivering trusted AI outcomes. A pragmatic master data approach is the foundation that changes everything.
Why the real gap in enterprise AI isn't model intelligence but the context layer underneath it.
AI sovereignty is no longer just an infrastructure debate. It’s about how institutions strengthen and leverage control, governance, and trust in the data that drives critical decision-making.
As organizations accelerate into an agentic future, governed and reusable data products are what ensure AI delivers consistent value, not inconsistent results.
With modern master data management (MDM), enterprises can drive continuous value from M&A opportunities with context-rich data products that enable faster decisions and smoother integrations.
Data products are transforming organizations for the better. Leave traditional MDM behind and use contextual data products to move faster, innovate more, and deliver continuous business improvement.
