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#112: The Data Models Dilemma in Digital Engineering
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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 word
- Why every engineering and business system represents data differently
- The mounting challenges created by siloed, mismatched data structures
- How digital twin initiatives have heightened the urgency for clean, connected data
- Real-world examples showing why context, meaning, and structure matter
- The risks and limitations of approaches like data lakes
- How manufacturers can begin evaluating, modeling, and aligning their data for desired business outcomes
- Why there will never be a universal data model — and why that’s okay
- Best practices for getting started, staying adaptable, and keeping data meaningful as technology evolves
This episode is especially relevant for anyone interested in:
- Digital Transformation
- PLM / PDM Modernization
- Digital Thread Initiatives
- Digital Twin Strategy
- AI Readiness in Engineering and Manufacturing
Notable 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].
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