--- title: Five Things Enterprise Architects Get Wrong About... description: Enterprise architects commonly make five critical mistakes when building semantic models for AI-ready data platforms. These include: starting from scratch... canonical: https://daily.dev/posts/five-things-enterprise-architects-get-wrong-about-semantic-modelling-and-how-to-fix-them-8kroyyliw twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Five Things Enterprise Architects Get Wrong About Semantic Modelling — and How to Fix Them | daily.dev og:description: Enterprise architects commonly make five critical mistakes when building semantic models for AI-ready data platforms. These include: starting from scratch... og:url: https://daily.dev/posts/five-things-enterprise-architects-get-wrong-about-semantic-modelling-and-how-to-fix-them-8kroyyliw og:image: https://api.daily.dev/og/posts/8KrOyyliw.png og:image:alt: Five Things Enterprise Architects Get Wrong About Semantic Modelling — and How to Fix Them og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Five Things Enterprise Architects Get Wrong About Semantic Modelling — and How to Fix Them **[Snowflake Community](https://daily.dev/sources/snowflake_comm)** · 14 min read · 1 upvotes · 0 comments ## Summary Enterprise architects commonly make five critical mistakes when building semantic models for AI-ready data platforms. These include: starting from scratch instead of consolidating existing assets, treating semantic modelling as purely an engineering problem rather than a shared business-technical responsibility, building monolithic central models that become bottlenecks, failing to decide where context lives across semantic models, skills, and system prompts, and treating the semantic model as a one-time deliverable rather than an operational system requiring CI/CD, testing, versioning, and named ownership. A hub-and-spoke tiered architecture, clear governance roles between semantic architects and business stewards, and operational discipline around schema drift detection are recommended as solutions. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Making AI Real Is Not a Technology Choice. 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