GFT GROUP
able to store all that data . But if you expect a lot from the data over time , building a decomposable architecture with , for instance , a layer that has schema bound API ’ s , will give a certain degree of surety on the data as it moves down your pipeline .”
GFT addresses data governance and compliance issues , especially in industries with strict regulatory requirements .
“ If you consider each layer of a modern data platform , you will inherently need to adhere to regulatory compliance ,” Tuppen explains . “ For example , if you look at BCBS239 , and consider Principles 1 and 4 .”
Principle 1 : Aggregated data and reporting need to have governance .
Principle 4 : Accuracy and integrity of data .
“ Focusing on the integrity of the input data means that insights created from the applied AI will be much more trustworthy ”
DAVID TUPPEN HEAD OF DATA AND AI ,
GFT
“ If you have a data governance model in place and data management in place across your pipeline , you will be following Principle 1 and Principle 4 , which includes accuracy and integrity of data ,” Tuppen advises . When an organisation moves their data into their data models , that will inherently force integrity on that data and ensure the accuracy of it .
He continues : “ So as soon as you start building that layered approach in your architecture , you will start becoming regulatory compliant inherently , rather than having to build it from scratch .”
aimagazine . com 131