DataOps for digital twins
summary
Digital twins depend on real-time trusted data and close-loop feedback from sensors plus enrichment sources; the argument is that scalable deployments require a data fabric (APIs, ML for automation, hybrid/multi-cloud access) and a data catalog with automated metadata management and lineage to track quality and drift. Examples and milestones cited include manufacturing origins, 2018 DoD definitions, and Singapore’s 2022 national digital twin, which runs at scale but surfaces data security and integration challenges.
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chapters (12)
- 0:00Introduction and background
- 1:26What are digital twins
- 5:54History and evolution
- 9:49Analytics categories
- 12:17How digital twins work
- 15:08Data challenges in digital twins
- 19:25Data fabric solution
- 21:35Data fabric explained
- 24:05Data mesh vs data fabric
- 25:29Data curation and governance
- 27:18Tracking data quality
- 29:05Summary and Q&A
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