4 Data integration and management
This chapter covers
- The types of data typically used by digital twins
- Sources of data and how they are integrated into a digital twin
- Data storage solutions
- Managing data governance and compliance
Data is the lifeblood of a digital twin. Sensors, enterprise systems, external APIs, and human inputs all generate data that must come together in a coherent way if the twin is to reflect reality and provide actionable insights. Without careful attention to how data is collected, cleaned, combined, and stored, a digital twin risks becoming fragmented, inconsistent, or untrustworthy. Data integration and management are therefore not just technical necessities but the foundation upon which a successful digital twin is built.
We’ve examined how to construct digital representations of physical systems and use sensors to gather data about system changes and feed it into your digital twin. Now we look at the ways in which we can integrate data into the twin from the myriad of places it may be produced, and how we can store and manage this data so that is available in an accurate and timely manner for the twin to consume. Together, these capabilities are what allow a digital twin to evolve from a collection of raw signals into a trusted representation that supports decision-making, prediction, and optimization.