Semantic layers stand out as a key approach to solving business problems for organizations grappling with the complexities of managing and understanding the meaning of their data. A semantic layer, also called a context layer, is a business representation of data that allows organizations to quickly map various data definitions, from multiple data sources to familiar business terms, offering a consistent and consolidated view of data.
A semantic layer enables the ability to ask questions and receive answers back in context. It can also increase access and trust in data, breaking down silos so more people within the organization can grab the information they need that may be further hidden in areas believed inaccessible. It can help solve the challenges around data lineage, accuracy, and integrity.
The underlying drivers of a semantic layer include content (knowledge, data, and information) is managed and accessible; data is connected across repositories, databases, and applications; and context and meaning are embedded with source data, making common understanding of data machine-readable.
In other words, a semantic layer is a standardized framework that organizes and abstracts organizational knowledge and data (structured, unstructured, semi-structured) and serves as a connector for all organizational assets, a central utility that can serve downstream use cases.”
A semantic layer solves several problems:
• Inefficient data analysis
• Non-intuitive user interactions
• Inconsistent metadata
• Poor data quality and governance
• Vendor lock
• AI hallucinations
The components within a semantic layer include metadata, a business glossary, taxonomy/information architecture, ontology, and a knowledge graph. AI comes into play during the data source and semantic layer stage. It can extract entities and relationships; summarize unstructured data; recognize patterns and curate facts.
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