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“A semantic layer is a business representation of corporate data that helps end users access data autonomously using common business terms,” says the definition, which seems a plausible solution to data lake intricacies. However, from the context of business, the semantic data layers tailored to serve specific BI tools have created siloed data repository that has restricted data analysts across all organizations to access data from all source and formulate strategies, thereof. Initially, when the concept of Data Lake–a repository of corporate data stored in native format–surfaced, a promise to view all the data through BI or analytics tool seemed impressive. However, this objective hasn’t yet materialized; instead, it has contributed to more challenges by creating fragmented data architecture.