Time FIs unlocked data mesh potential

The financial industry has undergone a significant transformation in its data management approach over the years. From traditional legacy databases to the advent of big data and data lakes, the way financial institutions manage and utilise their data has changed dramatically. The industry’s initial response to the exponential growth of data was to adopt big data solutions, which enabled the storage and processing of large volumes of data. However, as the complexity and variety of data continued to increase, data lakes emerged as a solution to store and manage raw, unprocessed data. Despite these advancements, the industry soon realised that these solutions were not sufficient to meet the growing demands of regulatory reporting, risk calculations and customer analytics.

Challenges with current data management solutions: As the financial industry continued to evolve, several problems arose with the data management solutions. The centralised data platforms, traditional enterprise data warehouses and lakes were unable to cope with the growing data volume, velocity and variety. The rigid extract, transform and load (ETL) frameworks made it difficult to onboard new data sources or adjust pipelines quickly to meet new regulatory needs. Moreover, the lack of self-service capabilities, cost and performance constraints, and governance complexity hindered the industry’s ability to adapt to changing business needs. The emergence of advanced analytics and artificial intelligence/machine learning (AI/ML) demands necessitated scalable, domain-aligned and high-quality data availability, which the current solutions were unable to provide.