Technology Solutions for the Data-driven Bank

A Buyer’s Guidebook for Necessary Technology in the Data-driven Bank

A new report by GreySpark Partners discusses the technology side of a successful approach to data strategy in investment banking. The report reviews the technology choices and the technology implementation processes that make up this data strategy. This report is the third part of GreySpark’s research series examining the business and management principles underlying the data-driven banking, which institutions can use to advise them on how to create competitive advantages through the utilisation of data.

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Data is the life blood of any financial organisation, wherein data integration infrastructure – the veins and arteries of the company – flows into the organisation’s heart, which pumps blood through its systems. A five-layer, data-centric architectural model is necessary to support an effective data strategy, although specific technology choices must be made according to each firm’s unique requirements. The full value of this data strategy and integration model is only realised through the pervasive application of relevant technology services that are made available at every tier within the architectural model. GreySpark Partners has seen many companies that have patchy – or, worse, conflicting – technology architectures and decision-making pressure points at various points within their data management models; for example, when they are siloed between the different parts of a bank. This type of uneven strategic planning is a telltale sign of weak architecture function and it can seriously erode the efficiency of the firm’s data strategy. A company with a mature organisational strategy for data will be supported by a complete architectural vision of data-related services that transcends business siloes, unlocking maximum utility from all of the available data.

Creating a sustainable data management strategy within a bank often requires a rethink of the technology architecture in the bank that is currently associated with data management. This report guides the reader through the exercise of creating a sustainable data management strategy, presenting the importance and role of five major categories of technology in data-driven banking.

Building a business case in a bank for financing technology investments and the implementation processes for that technology are challenging tasks. In the same way as there is no one-size-fits-all technology stack, there is no universal path to data maturity.

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