The financial services industry is often early to adopt massive-scale data processing and management technologies, given the highly data-driven nature of the sector and intense competitive pressures.

However, the highly regulated nature of the vertical and inherent sensitivity of financial data puts exceptional requirements on businesses. Data Operations (DataOps), with its emphasis on the automation of data management functions to facilitate core business objectives, is of particular interest to financial services. Challenges remain, however, in the sector’s implementation of DataOps strategy, which strives to ensure governance, reduce costs and risks, and increase productivity to drive business value of data.given the highly data-driven nature of the sector and intense competitive pressures.

However, the highly regulated nature of the vertical and inherent sensitivity of financial data puts exceptional requirements on businesses. Data Operations (DataOps), with its emphasis on the automation of data management functions to facilitate core business objectives, is of particular interest to financial services. Challenges remain, however, in the sector’s implementation of DataOps strategy, which strives to ensure governance, reduce costs and risks, and increase productivity to drive business value of data.