Data Sovereignty in Manufacturing: Putting Data Control into Practice

Data control is not a setting in a cloud dashboard. It is an architecture decision. Anyone who sends machine data, quality records, and process parameters to external services – even if only for analysis – gives up part of their control. This affects not only data protection but also operational security: what happens to your production data if the vendor changes its terms, gets acquired, or simply goes offline?

What true data control requires in practice

• Physical control: The data resides on systems in your own facility – not in a data center you have never seen.
• Structured data management: A central data lake with clear data paths, access rules, and quality criteria – as the foundation for every AI use case.
• Traceability: Every data access, every model query, and every result is documented and auditable – without a vendor ticket.
• Independence from the vendor ecosystem: No proprietary formats, no lock-in, no data loss when switching vendors.

OPAIRS: Data lake and AI inference under one roof

The OPAIRS system integrates an industrial data lake directly with the SLM inference layer – all local, all under your control. Production data does not flow through third-party APIs. Models run locally. Results land directly in the connected BI dashboard. Own your Data again – that is not a core message, it is a technical fact.

Governance is the organizational framework that makes this technical foundation usable: clear roles, defined access boundaries, measurable quality criteria. With OPAIRS, you build both at the same time – infrastructure and governance foundation – instead of one after the other.