They include the following:
Ensure cross-team engagement
Build a strong, balanced data model
Establish governance and benefits attribution from the start
Build a strong, balanced data model
Establish governance and benefits attribution from the start
Ensure cross-team engagement
Base the data model on the most demanding use cases and aggregate the data at the lowest levels of granularity. Strive for the appropriate level of accuracy in the data model. Higher accuracy comes with higher marginal costs.
Set up the right data governance and ownership model for using the digital twin, implement the process for attributing its impact, and manage the economics of all use cases built on top of the twin.
Dedicate people from business, IT, and data science teams, and ensure that technical and business teams create solutions and visualizations together. Assign a strong product manager who can lead the build team in an agile manner and persuade it and other stakeholders to accept an iterative approach.