# Manufacturing Data Governance: Ownership, Quality and Lineage > Industrial digitalization projects collect more data every year — historians, MES, quality systems, energy meters, edge devices — and then struggle with the… - Canonical URL: https://wiki.aspotomasyon.com/article/manufacturing-data-governance - Publisher: ASP OTOMASYON A.Ş. (ASP OTOMASYON Wiki) - Category: Digital Transformation - Published: 2026-08-11 · Updated: 2026-09-13 - Reading time: 4 min · Language: English Industrial digitalization projects collect more data every year — historians, MES, quality systems, energy meters, edge devices — and then struggle with the same symptoms: three different systems report different OEE, the KPI dashboard's numbers change depending on who answers, and nobody owns the definitions. **Manufacturing data governance** is the discipline that fixes this: clear ownership, agreed definitions, measured quality, and documented lineage for the data that decisions depend on. It is less glamorous than AI, and it is what makes AI work. ![An industrial data workflow. Each handoff needs an owner and a documented data contract; collecting more signals alone does not establish business value.](https://wiki.aspotomasyon.com/assets/diagrams/digital-transformation-context.svg) *An industrial data workflow. Each handoff needs an owner and a documented data contract; collecting more signals alone does not establish business value.* ## What Data Governance Means on the Plant Floor Governance is not bureaucracy; it is answering five questions for every important data asset: 1. **What is it?** — an agreed definition. "Downtime" means what, exactly? Planned vs unplanned? Does a changeover count? The definition lives in one place and is used everywhere. 2. **Who owns it?** — a named accountable person per data domain (production data, quality data, energy data, maintenance data). Ownership means they decide definitions, access, and quality priorities. 3. **Where does it come from?** — the authoritative source system for each fact. The historian is authoritative for machine states; the MES for order progress; the CMMS for work orders. Conflicts are resolved by source, not by convenience. 4. **How good is it?** — measured quality: completeness, timeliness, accuracy, consistency. "Good enough for monitoring but not for billing" is a legitimate, documented classification. 5. **Who may use it, and how long do we keep it?** — access rules (who sees downtime by operator?) and retention (how long does process data stay before archival/deletion?). ## Building the Governance Foundation Start small and formalize incrementally: - **Business glossary** — a document (wiki page is fine) with the definitions of the top 50 terms: OEE, availability, yield, downtime classes, scrap, RFT. Each term has one definition, one formula, and one source system. - **Data owners and stewards** — an owner per domain; a steward does the operational work (definitions maintenance, quality monitoring). This is a role assignment, not a new department. - **System-of-record map** — one page listing every system and which facts it owns. When two systems disagree, the map says who wins. - **Quality monitoring** — automated checks on critical data flows: historian gaps, negative values where impossible, stale timestamps, duplicate events. Alert the steward, not the whole plant. - **Change control** — definitions change (a new product type changes "yield"); the change is recorded with an effective date so history stays comparable. ## Data Lineage and Traceability **Lineage** is the record of where a number came from: which sensor, which calculation, which system, which transformation. It matters most when data crosses system boundaries: - Every KPI on a dashboard should be traceable to its raw data and its calculation (with version). - Regulated industries (pharma, food) already demand audit trails; lineage makes them *usable* for analytics instead of an afterthought. - Machine learning models trained on plant data need lineage to answer "what changed between model version 2 and 3?" — the most common production ML failure. Practical lineage tools: the historian's tag metadata (source, engineering unit, last calibration), ETL/edge pipeline logs, and — for the important flows — a simple data catalog that records pipeline versions and refresh times. ## Governance and the Automation Layer The automation department is both a data producer and a data consumer, and governance friction concentrates at its boundaries: - Tag names change during commissioning — the historian consumers downstream break silently. Govern tag renames with a notice period and a mapping table. - PLC programs are the de facto definition of machine behavior; the governance model must include the control narrative and interlock documentation as reference data. - OT data flowing to IT systems needs contracts (units, quality, timestamps) — the same discipline as any interface, but now with governance consequences. ## Making Governance Stick Governance fails when it is a document exercise. The practices that survive: - Governance reviews attached to real decisions (monthly performance review walks the glossary; disagreements escalate with evidence). - Data quality KPIs in the same dashboard as production KPIs — quality is not a separate report. - One owner per KPI, visibly named on the dashboard. - Annual data inventory review — systems and facts change faster than documents. ## Summary Manufacturing data governance is the operating system for industrial analytics: agreed definitions, named owners, authoritative sources, measured quality, and documented lineage. Build the glossary, the system-of-record map, and the quality monitoring first; attach governance to real decisions; and make owners accountable in the visible KPI process. Without it, every analytics project is an argument about whose numbers are right. ## Apply this to your project - Name the operational decision, its owner, and the evidence required to judge whether it improved. - Document asset identifiers, units, timestamps, quality rules, and definitions shared between OT and business applications. - Compare the pilot against a recorded baseline before extending it to other machines or sites. [Follow an ASP project guide](https://wiki.aspotomasyon.com/guides) to connect the fundamentals with implementation and acceptance. ![Engineering review sequence: Question → Baseline → Pilot → Scale. Keep the recorded evidence with the accepted configuration.](https://wiki.aspotomasyon.com/assets/diagrams/digital-transformation-review.svg) *Engineering review sequence: Question → Baseline → Pilot → Scale. Keep the recorded evidence with the accepted configuration.* ## Primary references and further reading Use the original specifications and product documentation for implementation details. The examples in this guide are illustrative engineering scenarios, not published project results. - [ISA — ISA-95 enterprise/control system integration](https://www.isa.org/standards-and-publications/isa-standards/isa-95-standard) - [OPC Foundation — Information modelling](https://opcfoundation.org/about/opc-technologies/opc-ua/) --- Cite the canonical article: https://wiki.aspotomasyon.com/article/manufacturing-data-governance (Manufacturing Data Governance: Ownership, Quality and Lineage, ASP OTOMASYON, updated 2026-09-13).