Sustainable value creation in the smart factory transformation is built upon a solid data architecture. The ability of SCADA, MES and ERP layers to operate without redundancy directly determines the overall production efficiency and decision quality of the entire system.

The primary question when designing a data architecture is: which decisions will this data support? Real-time sensor streams for operational decisions, trend analysis for maintenance planning, and aggregated data for financial reporting all require a shared underlying model to function coherently.

Pushing raw field data to the reporting layer without any normalization stage substantially degrades decision quality. Dashboards built without data cleansing, contextual enrichment and a defined semantic hierarchy present misleading snapshots.

The accurate and low-latency transfer of process data from PLC, SCADA and HMI systems to the MES layer directly affects the reliability of OEE (Overall Equipment Effectiveness) calculations. Without this reliability, production optimization decisions lack a solid foundation.

In the Industry 4.0 paradigm, machine learning models and predictive analytics applications yield meaningful outputs only when operating on sufficiently deep historical data that is consistently labeled. Inadequate data modeling renders even the most sophisticated algorithms valueless.

Failure to properly reflect field protocols in the data layer deepens information asymmetry between maintenance planning and operations teams. In industrial automation projects, a unified data model structurally eliminates this asymmetry.

At Hermes Technology, we design data architectures in smart factory projects to simultaneously support operational monitoring, maintenance planning and cost analysis. A single data backbone feeds all decision layers with consistent intelligence.