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F O R E C A S T platforms , all of which create their own data and store it in their systems . There is a lack of inter-collaboration between all parties , the bank itself and external partners . Reltio ’ s 2024 Data Leader Survey found that 82 % of respondents said that over 40 % of their organisation ’ s data is derived from over 50 applications .
E X P E R T
F O R E C A S T platforms , all of which create their own data and store it in their systems . There is a lack of inter-collaboration between all parties , the bank itself and external partners . Reltio ’ s 2024 Data Leader Survey found that 82 % of respondents said that over 40 % of their organisation ’ s data is derived from over 50 applications .
Consequently , data silos prevent banks from gaining a complete , and therefore accurate , view of their operations and their customers . As such , this makes it challenging for these financial services organisations to harness data for AIdriven insights , efficient decision-making and seamless customer experiences . financial services organisations must also ensure that the data , which is fed into these models are high quality , complete and trusted .
Therefore , financial services organisations must have a strong data unification strategy , or they will be at risk of their data silos becoming that much more troublesome as they continue to scale and grow . It will also have a significant impact when it comes to providing service to customers . This is because data which is stored in silos will not give that customer service agent a full view of the customer and so will not be able to offer personalised or proactive
AI IN BANKING IS TO IMPROVE OPERATIONS AND CUSTOMER SERVICE BY COLLECTING AND ANALYSING DATA , THE REALITY IS THAT FOR TOO MANY BANKS , THEIR DATA SOURCES ARE FRAGMENTED AND INCOMPLETE . service . As such , all data about each customer should be stored in one place which is easily accessible .
Banks should focus on driving customercentric initiatives , with a strong focus on customer 360 views to enhance experiences and create operational efficiency . So , it is gratifying that most of the respondents in the aforementioned survey of data leaders across all sectors , including banking , have plans to upgrade data architecture within the next 12 months to improve the unification and management of data across their enterprise .
As financial services organisations continue to grow , scale and add more third-party applications , their siloed data issues will only continue if banks scatter valuable information across increasingly fragmented sources . With a modern data unification tool , banks can consolidate data and deliver trusted and timely insights to drive best-in-class customer service . As banks continue to further their AI-driven digital transformation efforts , only an integrated data unification solution will unlock AI ’ s full potential and empower banks to thrive . �
Overcoming the challenges with a unified data strategy
No matter how ambitious a bank ’ s AI plans are , they often find their organisations are stalled by inconsistent and fragmented data . So , whilst financial services organisations pursue AI-driven transformation efforts , many remain entangled in a web of disconnected data , jeopardising their success . Even those businesses with mature data governance frameworks face persistent data silos that hinder their AI initiatives .
So , as banks continue to spend their ever-tightening budgets on digital transformation , they must ensure that they are making the most of these efforts . The Global Banking Benchmark Study uncovered that 32 % of executives said budget constraints are a significant barrier to digital transformation . So , it is important for banks to break down these data silos to improve their AI-driven initiatives . Not only that , but
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