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 information that must be combined to form a coherent view of the physical world. If this data is fragmented or inconsistent, the digital twin quickly loses credibility as a decision-support tool.
Building a reliable digital twin therefore depends on effective data integration and management. Data must be collected, validated, transformed, and stored in ways that preserve accuracy while making it accessible for analytics, monitoring, and automation.
In earlier chapters you saw how digital representations of physical systems are created and how sensors capture signals from the real world. In this chapter we expand that view by examining how data from many different sources flows into the digital twin and how it is stored and managed once it arrives. These capabilities allow a twin to evolve from a collection of raw signals into a trusted system for monitoring, prediction, and optimization.
We begin by examining the different types of data used by digital twins. Understanding these categories helps you design architectures that support their distinct characteristics and processing requirements.