Data governance and operations

Trend 6. Proactive smart governance through AI-first

There is a shift from reactive and rule-based data governance to an end-to-end autonomous “no governance” ecosystem, leveraging the principles of AI-first, cognitive, and governance by exception. Smart data discovery, data tagging, DQ assessment, DQ rule discovery, relationship discovery, and automated cleansing enable smart governance. For instance, smart data discovery amplifies the value of data lakes and unstructured data on platforms such as Twitter and Facebook, providing a blueprint of the data in these environments by uncovering complex data relationships.

A U.S.-based financial services company was facing challenges in managing DQ, with 80+ analysts involved in data cleansing activities across 17 applications. Infosys helped the client implement a comprehensive and cognitive data governance platform. The solution discovers and recommends appropriate cleansing rules by analyzing data patterns and relationships, reducing the client's data stewardship efforts.

Data governance and operations

Trend 7. Intelligent cloud-based data operations to increase operational efficiency

As increased cloud adoption has scaled up on-demand infrastructure, the focus is now around intelligent orchestration of infrastructure-as-code capabilities to drive operational efficiency. Capabilities such as leveraging ML-based techniques to predict capacity needs, identifying anomalies, and self-healing platforms will define the route of future data operations. These capabilities provide flexibility, responsiveness, and agility that businesses need to change course quickly during future business disruptions.

A leading payment card services company was looking to optimize the effort around platform operations. They had a large data platform that required considerable administration and monitoring effort for smooth business operations. Infosys implemented a Splunk-based platform management solution that improved the client's operational efficiency by 30%. Predictive analysis and anomaly detection were applied for capacity planning and identifying anomalies, enabling proactive planning. Further, the implementation of realtime email alerts and self-healing improved platform stability and reduced manual efforts.