DIGITAL TRANSFORMATION
“ It’ s less about volume and more about fragmentation and context,” he says.
“ Financial institutions don’ t lack data – they lack clean, connected, real-time data that agents can reason over. Much of that data sits in silos – core banking, payments, risk systems – and often in unstructured formats. Turning that into something agents can act on requires data normalisation, governance and real-time access layers.
“ The goal is to build what we think of as a‘ hill-climbing machine’, which are systems that continuously learn from proprietary data, improve through feedback, and operationalise that learning across workflows.”
To bridge these institutional silos, organisations are turning to centralised data fabrics to synthesise their assets without undergoing risky, multi-year migration projects.
“ This is where we see platforms like Microsoft Fabric playing a critical role,” adds Bill.“ They bring data together across the enterprise, unifying structured and unstructured sources, and making it accessible in a governed way so AI systems can operate with the right context.
“ The institutions that get this right are treating data as a product – curated, governed and continuously refreshed – so agents can move from insight to action in real time.”
Bill points to real-world deployment to illustrate how this data unification directly translates into competitive agility:“ As well as getting insights, getting your data into shape can accelerate your product development. LSEG used Fabric to consolidate petabytes of data across 30 systems and 1,200 datasets.
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