| dc.description.abstract | The banking sector faces integration challenges with traditional data systems, such
as data warehouses and data lakes, which hinder real-time analytics and actionable
insights. This review addresses a critical research gap in the adoption of Data Lakehouse
architectures within financial institutions. A systematic literature review of empirical
studies from five major databases including IEEE Xplore, SpringerLink, ResearchGate,
Semantic Scholar, Google Scholar spanning 2015–2024 highlighted that Data Lake houses can enhance analytics speed by up to 30%, improve data governance by 25%, and
reduce operational costs by 20%, compared to legacy systems. By seamlessly integrating
structured and unstructured data, while ensuring Atomicity, Consistency, Isolation
and Durability (ACID) compliance, Data Lakehouses eliminate data silos and enable
real-time decision-making. These improvements directly translate into faster decision making, more accurate risk assessments, and better customer experiences, giving banks
a competitive edge. However, further empirical research, particularly longitudinal case
studies, is required to validate these findings and optimize implementation strategies
within the banking sector and beyond. This study underscores the strategic value of
adopting Data Lakehouse platforms to modernize data infrastructure and enhance
operational efficiency in a rapidly evolving market. | en_US |
