Using Data Modernisation to Transform Banking Compliance

Data modernisation is important to implement to keep up with constant regulatory changes. It helps in reducing costs, minimising risks, and identifying opportunities to create effective products and services for consumers.

FREMONT, CA: Regulatory change is not a new concept in the world of banking and financial services (BFS), but the recent shifts have increased the level of granularity and the frequency required for regulatory requirements and have altered the approach for the same. This is the result of the execution of new regulatory guidelines, in line with cloud adoption and digital schemes, which are building scope within BFS companies and are forcing new perspectives on handling data. As a result, the implementation of data modernisation in banks is widely significant.

Reducing costs, minimising risks, and identifying opportunities to create effective products and services for consumers fall under the umbrella of data modernisation.

In the context of banking, however, the focus data modernisation of data modernisation is on risk and compliance is focused on improving the delivery of data and also on designs of operating models and system architecture that conduct end-to-end processes, such as processing, sharing, and collecting data seamlessly, which in turn improves innovation and increases efficiency.

As a rule of thumb, modern data modernisation must execute organisational and technical guidelines, which can be deposited, analysed, and shared in various forms for reporting compliance. Implementation of data modernisation comes along with multiple modern data architecture elements.

DataOps

This architecture element involves applying DevOps and agile methodologies to the data pipeline, from data ingestion and processing to data delivery and consumption. By incorporating DataOps practices, companies can enhance the quality, agility, and reliability of their data pipelines, and consequently drive better business outcomes.

The foundational principles of DataOps include continuous integration and delivery of data pipelines, automated testing and validation, version control of data artefacts, and observability and monitoring of data processes.

DataOps aims to expedite the time-to-value of data by boosting automation, collaboration, and feedback loops between cross-functional teams, such as data scientists, business analysts, and data engineers.

Data Fabric

It is a system that allows companies to integrate and manage data from numerous sources across multi-cloud and hybrid environments. Data fabric offers a singular, consistent view of data, which enables organisations to access and utilise the data in real time, irrespective of its location or format. These solutions commonly comprise features such as data transformation, data quality, data integration, data security, and data governance, which ascertains that the data is trustworthy, accurate, and most importantly, sure. The objective of data fabric is to offer a consolidated view of a company’s data, which makes it easy for users to access, analyse, and gain insights from the data.

Data Responsibility

Data responsibility refers to the accountable and ethical management of data by companies, governments, and individuals. It consists of principles and practices of data security, privacy, governance, and transparency, and focuses on the need to make use of data, adhering to the rights and interests of individuals and society.

Data responsibility ensures that individuals have control over their data and are aware of how their information is being used. It also encompasses taking responsibility for any damages that may ensue from the use of that data and taking steps to eliminate risks regarding data breaches or the misuse of said data.