Disparate BI, analytics, and data science tools lead to discrepancies in data interpretation and definitions, which a universal semantic layer can effectively resolve.
A semantic layer hides the complexity of raw data and maps it to business definitions, improving self-service analytics for users and ensuring consistency in reporting.
After the advent of multiple BI tools, organizations face challenges with differing definitions and interpretations, breeding mistrust among teams regarding data-driven insights.
The goal of having a single source of truth for BI and analytics has been undermined by many separate semantic layers leading to confusion and inconsistency.
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