This scenario is often seen in large global corporations which have a diverse and distributed workforce. Say, marketing calls a business a prospect while managing the leads in Salesforce CRM, sales might call this same business a client as orders are managed in SAP ERP, and finance could call the same business entity a counterparty as the invoicing process is managed in Oracle EBS. So, in this complex environment, how do you get a report that aligns all three data elements into one? In the lebanon whatsapp number data data landscape that is siloed, it is extremely difficult to get a single “Lead to Cash” report. Functions such as Finance need enterprise-wide reporting to meet the IFRS/GAPP compliance needs. So, if your reporting needs to be enterprise-wide while still addressing inconsistent data definitions and data silos, then the semantic layer is the option.
Organizations that aspire to become AI-ready need the data catalog, semantic layer, and data warehouse. The generative AI rush is everywhere. The executive management is excited about the opportunity. data quality, including the semantic uniform definitions of structured data.
When Are a Data Catalog, Semantic Layer, and Data Warehouse Not Required?
Here are the situations where companies might not need the data catalog, semantic layer, and data warehouse.
A semantic layer (and data catalog and data warehouse) is recommended for:
Enterprise-wide reporting when there are data silos with inconsistent and multiple data definitions, and
To serve many diverse analytics users.