Synthetic Data
Definition
Synthetic Data is data that is artificially generated — through simulation, statistical modeling, or generative AI techniques — rather than directly captured from real-world sensors, events, or transactions, used as a substitute or supplement for real data in training and testing machine learning models.
Key Points
- Synthetic data is artificially produced rather than collected from real-world observation, generated via simulation, statistical modeling, or generative AI techniques.
- The primary industrial use case for synthetic data is compensating for the scarcity of real failure examples — mining, energy, and maritime equipment fails rarely by design, leaving genuine ML training datasets thin on the exact events most valuable to predict.
- Synthetic data reduces the need to transmit large volumes of raw operational data off-site for model training when that data is sensitive, proprietary, or subject to data-sovereignty constraints, since realistic substitute data can be generated locally or centrally without exposing real records.
- Synthetic data quality is bounded by how well it captures the statistical properties of the real-world domain it approximates; poorly generated synthetic data can degrade rather than improve downstream model performance.
- Synthetic data generation is commonly a centralized, batch process, whose output is then distributed to distributed or federated training pipelines — creating a distinct, schedulable data-transfer requirement separate from live operational telemetry.
Concept
Synthetic data generation typically works by learning the statistical structure of a smaller real dataset and then producing new, similarly structured examples, including rare event types that occur too infrequently in real operational data to train a robust model. This is particularly valuable in industrial machine learning contexts, where failure events — the exact examples an anomaly detection or predictive maintenance model most needs — are rare precisely because equipment is engineered to fail infrequently. Generating realistic synthetic failure scenarios allows model training to proceed without waiting for enough real failures to accumulate, and without requiring an operator to induce artificial failures for data-collection purposes.
Explainer
For ConnectedEarth's audience, synthetic data addresses a specific data-availability gap that recurs across mining, energy, and maritime predictive maintenance programs: the assets being monitored are, by design, reliable, which means real failure data needed to train effective anomaly-detection or predictive-maintenance models accumulates slowly. Generating synthetic failure signatures — plausible sensor patterns representing bearing wear, corrosion progression, or engine degradation — allows model training to proceed on a realistic but artificially augmented dataset. Once generated, this synthetic training data is distributed to wherever model training occurs, whether centralized cloud infrastructure or distributed federated learning nodes, as a scheduled data-transfer task distinct from the continuous flow of live operational telemetry.