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It ensures consistency, integrity, and clarity across disparate data sources.
It prevents data silos by providing a governed structure, facilitating seamless integration.
It enables organizations to generate accurate insights by maintaining a unified view of data.
Challenges in Traditional Data Modeling
Despite its importance, data modeling remains a complex and time-consuming process, fraught with challenges:
- Scale and Complexity: Enterprise data models can contain thousands of entities and attributes. For example, a Consumer Packaged Goods (CPG) industry model may have over 4,000 entities and 28,000 attributes, making comprehension and maintenance arduous.