Semantic Data Layer: The Necessary Evil
“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.
Having said that, semantic layers do provide tangible solutions to data lake intricacies by extracting meaning from the humongous data pool. It allows business and operations team to function smoothly. In the past, semantic data layer has been utilized as a point solution; however, universal semantic layer mitigates this challenge by combining the key business metrics, existing definitions in BI tools in a single abstraction layer that serves as a one-stop shop for managing and controlling data. This minimizes data movement and replicates data across the organization, thereby bolstering the security framework.
All in all, semantic data layer with its new improvisation is extremely crucial for businesses these days. Not only it allows a better understanding of data across all departments in the organization, but it also boosts data security. Since an all techie corporate is implausible, semantic data layer remains as one of the major tools to streamline data analysis.