The evolution of the modern data platform

The modern data platform does not seek to define a golden hammer to replace all the different stages of legacy architectural designs. It ensures that we now put focus on the domain itself.

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Our six steps

Data as a product

Build and manage data product outputs which are a collection of data assets.

Data marketplace

Add a layer to trade data products. Adding this will clearly show integrity across data products, adding confidence to domains.

Domain level architecture

Focus more on building data solutions for each domain which will add value per domain. Different domains are more or less and as complicated as one another.

Front to back design

Leading on from domain level architecture, put the end consumer first. How are they going to use the data?

Metadata management

Management of metadata produced through all these products is critical to track not only redundant data, but to govern the landscape.

Enhanced stream-driven

Remove constant dependency on end-of-day processing when streams are available. Enhance the platform by supplying up to-date data so that information value may be gained sooner.

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Be a step ahead

AI in banking: GFT's perspective

The banking industry is embracing AI to deliver change - at different maturities and speeds from one organisation to the next - but it is everywhere! GFT recognises that effectively, ethically and safely implementing AI will be critical to support the evolution of the banking industry.

Corporate Banking

This paper highlights the changing needs of corporate banking clients, the challenges faced by banks in meeting them and how GFT and 10x Banking can help the banks overcome those challenges and meet their customers’ expectations.