solution.data_collection()
Data collection looks like a physical connection problem, but is really about semantic quality: arriving data is not enough — it must be interpretable.
Each line has arbitrary tag names, different units and inconsistent frequencies. Downstream systems do not know what they are reading.
Without a shared tag dictionary, analytics and MES build parallel maps that break at the first PLC change.
When the network or gateway goes down, data is lost. Nobody flags the gap and historical records cannot support retrospective decisions.
Store-and-forward and explicit gap markers are needed: a declared missing value beats a fabricated number.
Each plant has its own implementation: different brokers, protocols, security. Maintenance and rollout become impossible.
A single collection pattern with controlled variants is the only way to make an industrial data platform scalable.
Asset inventory, available protocols, tag lists and required sampling frequencies.
Design of the collection layer: edge gateway, protocols, local buffering and data store connection.
Driver setup for each machine: OPC-UA, Modbus, S7, MQTT. Read testing and validation.
Time-series database configuration, retention policy and data access API.
The difference is not only in connecting to the machine, but in the quality of the data reaching downstream systems.
Design of edge, gateways, brokers, historians or APIs based on continuity and security requirements.
Retry, buffering, store-and-forward and consistency checks to avoid gaps or duplications.
Tag dictionary, resilient architecture, shared semantics — data arrives correct and interpretable.
