- 1 September, 2017
Enterprise Data Modeling Made Easy
From Analytics Week:
Enterprise data modeling has remained an arduous, time-consuming task for myriad reasons, not the least of which is the different levels of modeling required across an organization’s various business domains.
Data modelers have to consider conceptual, logical and physical models, in addition to those for individual databases, applications, and a variety of environments such as production and post-production. Oftentimes, the need to integrate new sources or to adapt to changing business or technology requirements exacerbates this process, causing numerous aspects of it to essentially begin all over again.
Enterprise data modeling is rendered much more simply with the incorporation of semantic technologies—particularly when compared to traditional relational ones. Nearly all of the foregoing modeling layers are simplified into an evolving semantic model that utilizes a standards-based approach to harmonize modeling concerns across an organization, its domains, and data environments.
Moreover, the semantic approach incorporates visual aspects that allows modelers to discern relationships between objects and readily identify them with a degree of precision that would require long periods of time with relational technologies.
“Semantics are designed for sharing data,” Franz CEO Jans Aasman reflected. “Semantic data flows into how people think.”
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