Enterprise-Wide Financial Data Lake Implementation: From Ingestion to Analytics

Financial institutions such as banks, insurance companies, and even fintechs have started leveraging data lakes as a powerful analytics tool to aggregate various data sources. A financial data lake provides an all-encompassing view of customer behavior, operational risks, fraud, and even regulatory measures by integrating both structured and unstructured data in near real-time. In this paper, we describe a potential implementation of a financial data lake that includes ingestion, governance, security, and analytics. We cover the latest publications on data lake architectures, explain the important factors that need to be considered in the design of a data lake for the financial industry, and capture enterprise-scale deployments through case studies. Challenges and advantages of such implementations are discussed. The evidence suggests that something as simple as ingesting data through multiple structured data sources can determine a firm’s success alongside governance guidelines, defined metadata, and real-time analytics. Lastly, we discuss new developments and trends like lakehouse architectures, AI-enabled data gathering, and metadata orchestration that will make deploying financial data lakes more feasible in the future.