service.data_engineering()
Industrial data almost always exists, but it is scattered across systems, local queries and spreadsheets. The result is a fragile base on which every MES, analytics or AI initiative stays limping.
Each system has its own language, nobody owns end-to-end quality, and the gaps only surface when a report breaks.
Without explicit source ownership, every data-quality discussion turns into a blame game between departments.
Scripts were written to unblock a specific case and now support operational decisions, but nobody ever put them under version control or monitoring.
Repeatable patterns are needed: ingestion, transformation and serving defined as stable contracts, not personal scripts.
KPIs, events and units are defined differently across systems; dashboards and ML models produce numbers that do not speak to each other.
A single semantic layer is the only way to make MES, analytics and AI share the same operational vocabulary.
Census of data sources, quality, frequency, format and ownership.
Design of a unified data model based on ISA-95: equipment, materials, operations, KPIs.
Development of ingestion, transformation and loading pipelines with scheduling and monitoring.
Operational and executive dashboards, API for programmatic access, data catalogue.
We make the data chain explicit so value does not depend on artisanal queries or tacit knowledge held by a few people.
Ingestion, normalization, transformation and serving patterns designed for continuity and maintenance.
Shared definitions for KPIs, events, timings, orders, machines and plant hierarchies.
Mapped sources, repeatable pipelines, shared semantics — the foundation for MES, analytics and AI.
