A modern organization's approach to managing growing volumes of enterprise data is evolving from centralizing it in a data lake to adopting a distributed data mesh architecture. Data lakes are centralized repositories that organize and protect large amounts of structured, semi-structured, and unstructured data from multiple sources, while data mesh architectures treat data as a product and support self-service, with data remaining within different databases rather than being consolidated into a single data lake. Both approaches can coexist and offer complementary solutions to achieve faster time-to-analytical-insights, with the right tools and organizational strategies in place.