Why Data Models Matter in Digital Engineering (Now More Than Ever)In this episode, Juliann Grant and Jonathan Scott dive deep into the growing conversation around data models in digital engineering. With increasing pressure to enable the digital thread, digital twins, and emerging AI capabilities, understanding how data is structured and why it varies across systems is more critical than ever.Together, they unpack:What a data model really is and why “model” is the key wordWhy every engineering and business system represents data differentlyThe mounting challenges created by siloed, mismatched data structuresHow digital twin initiatives have heightened the urgency for clean, connected dataReal-world examples showing why context, meaning, and structure matterThe risks and limitations of approaches like data lakesHow manufacturers can begin evaluating, modeling, and aligning their data for desired business outcomesWhy there will never be a universal data model — and why that’s okayBest practices for getting started, staying adaptable, and keeping data meaningful as technology evolvesThis episode is especially relevant for anyone interested in:Digital TransformationPLM / PDM ModernizationDigital Thread InitiativesDigital Twin StrategyAI Readiness in Engineering and ManufacturingNotable Quote:"If the AI doesn't understand the data, and it's just doing statistical prediction, the predictions can be junk. In a safety-critical situation, that's not cool." – Jonathan Scott Have questions or thoughts on this episode? Leave a comment or email [email protected]. Music is considered “royalty-free” and discovered on Story Blocks.Technical Podcast Support by Jon Keur at Wayfare Recording Co.© 2024 Razorleaf Corp. All Rights Reserved.

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