Data ingestion & integration that turns messy operational data into twin-ready intelligence
Connect OT, IT, and engineering data—reliably and securely—so your digital twins stay synchronized with reality. Normalize and contextualize signals from machines, buildings, grids, and networks into a single decision-ready foundation.
What is data ingestion & integration in an intelligent digital twin platform?
Data ingestion and integration is the process of continuously collecting data from operational and enterprise systems, standardizing it, adding context (which asset it belongs to, where it is, and what state it represents), validating data quality, and making it available to models, simulations, analytics, and applications. In an intelligent digital twin, integration isn’t just about moving data—it’s about making data trustworthy and usable for real-world decisions.
Connect the systems you already run
Operational (OT) sources
SCADA / DCS
PLC data streams
Historians
BMS (building management systems)
Enterprise (IT) sources
ERP
EAM / CMMS
MES
Asset registries / master data
IoT and streaming sources
Sensors and gateways
Event streams and telemetry
Edge devices and on-site collectors
External and contextual sources
Weather and tariff signals (when relevant)
GIS / geospatial layers
Vendor or partner feeds
Built for operational reality
1.
Real-time + batch ingestion
Support continuous monitoring where seconds matter, and batch ingestion where systems update on schedules.
2.
Semantic mapping and contextual models
3.
Event handling and change tracking
4.
Data governance and access control
5.
Monitoring and reliability
Integration Approaches Teams Use in Practice
Multi-site standardization
Normalize naming, units, and asset models so insights can scale across plants, campuses, regions, or networks.
Read-only first (recommended start)
Validate data quality and prove value before enabling any write-backs or automation.
Phased rollout by use case
Start with one line/site/building/node that ties directly to a measurable outcome.
When data is twin-ready, everything downstream gets easier
Faster time-to-value for predictive maintenance, optimization, and simulation
Fewer data disputes ("Which dashboard is correct?") because definitions and context are consistent
More accurate predictions because models see the full operating picture, not isolated signals
Better what-if simulation because relationships between assets and constraints are captured
Stronger operational resilience through continuous monitoring and early detection
Frequently Asked Questions
Turn your data into a foundation for real operational decisions
Start with one system, one site, or one use case—and build a twin-ready data layer that scales.