This paper investigated the capabilities of data lakes in enterprises. An exploratory study was conducted tounderstand data lake technologies and provided insights into the perceived benefits and purposes of data lakes. Thisstudy found that data lakes integrate seamlessly with a variety of data sources and data warehouses. Though datawarehouses continue to meet users’ information needs and provide important value to enterprises, data lakes offerrich sources of data for data scientists, analysts, and self-service data consumers, while also serving the needs of BIand big data. This paper makes three contributions to the BI literature: data lakes are used as a staging area for datawarehouse; data lakes serve as a platform for experimentation for data scientists and analysts; and data lakes can beused as a direct source for self-service BI. The bottom line is that data lakes do not replace data warehouses; rather,they augment or complement the data warehouse architecture. Hence, data lakes should be considered extensions of the BI architecture. The study also identified several challenges related to data lakes. A deeper awareness of thesechallenges could benefit organizations seeking to embark on data lake projects.Like any study, this study has some limitations. Although this exploratory study drew on experts with knowledgeand experience in data lakes, the experts came only from large enterprises. Therefore, all the results are based on theexperiences of experts from large enterprises. Furthermore, this research represents only one exploratory study;therefore, it has limited generalizability. Despite these limitations, however, the findings of this study can provideimportant inputs for future empirical research on data lakes.