Today an enterprise has multiple sources of data – customer, supply chain, sales, finance, employees. But mostly this exists in silos making cross-fertilisation of data for rich business insights almost impossible: they are even on different hardware and environment.

Traditionally it required considerable efforts to transform and move entire blocks. Now with Data Lakes disparate but relevant data can be brought together in the Data Lake, without going through a massive data transformation process, for cross-functional applications across the enterprise.

Data scientists can access, prepare, and analyse data faster and with more accuracy using data lakes. For analytics experts, this vast pool of data — available in various non-traditional formats — provides the opportunity to access the data for a variety of use cases like sentiment analysis or fraud detection.