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Manish Sood , CEO at Reltio their data sources are fragmented and incomplete .
So , how can banks ensure their journey to effective AI usage is not delayed or derailed ?
The obstacles to effective AIdriven enterprise data integration
Firstly , business and technology leaders within banks must acknowledge the barriers to AI deployment . Whilst it is one of several issues , including low trust , a lack of explainability in the technology , and integration issues , siloed data that is poor quality and incomplete is the most fundamental problem to correct .
How data is siloed compounds the challenges of banks getting their AIdriven enterprise data right . A data silo is a collection of information that is stored within a specific application , department , or system that is not easily accessible or shared across an organisation . Data silos often emerge as financial services organisations add specialised tools to solve specific business challenges . Here , each tool generates and stores its data , leading to greater fragmentation and more data that is held in silos .
As well as this , as banks scale in size and add more applications to their backend systems , it creates even more data stored in its hubs within the business . This continues to snowball as financial organisations rely on multiple third-party
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