Ownership assigns a named party responsibility for a digital twin’s components, decisions, changes, and consequences. It is not enough to name an owner for the twin as a whole: connected components need accountability at the level where decisions are made. section 10.5.2
Make ownership explicit for:
- Physical data sources and ingestion pipelines, including who has authority to resolve conflicting reports of an asset’s state. section 4.5.4 section 10.5.2
- Data models and schemas; simulation and analytics logic; machine learning models; and the business rules that turn insight into action. section 10.5.2
When sources disagree
A home twin can have one person owning every data source. An industrial asset may receive data from contractors, vendors, and internal teams. If three systems report different machine states, clearly defined source owners establish who can resolve the conflict. Traceable provenance then records which system produced a data point, how it was transformed, and who validated it. section 4.5.4
Treat ownership as a production requirement, not an afterthought. Unclear responsibility slows failure diagnosis and makes changes risky. Before deployment, assess it alongside data quality, model validation, governance, and cross-team workflows; technical feasibility alone does not make a twin ready to influence real-world decisions. section 10.5.2 section 1.5.8
