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Industrial DataOps applies the operational discipline of DataOps — automated validation, continuous monitoring, and governed pipelines — to the engineering and asset data generated across capital projects and industrial operations. Rather than treating data handover as a one-time migration event, industrial DataOps treats asset data as a continuously flowing, continuously validated stream: from engineering and procurement, through construction and commissioning, into operations and maintenance.
The term is most relevant to owner-operators and EPCs in oil, gas & LNG, chemical & process, utilities, pharmaceutical, shipping, and mining & metals who need engineering and asset data to stay reliable as it moves between contractors, disciplines, and systems — not just at project handover.
Industrial DataOps borrows its principles from software DataOps: automate the checks that used to be manual, monitor data quality continuously rather than at fixed milestones, and give every stakeholder visibility into the state of the data pipeline. Applied to capital projects and operations, this means automated validation of tag attributes, documents, and equipment data as they are submitted — rather than discovering gaps only when a handover package is reviewed at the end of a project.
Most capital projects still manage data handover as a single, end-of-project event: documents and tag data are collected, reviewed, and transferred in one large batch. This approach concentrates risk at the worst possible moment — when the project team is demobilizing and there is little time or budget left to fix quality problems. An industrial DataOps approach instead validates data continuously throughout the project, so that by the time handover is due, the data has already been checked, corrected, and is ready to use.
Traditional document control focuses on managing revisions, approvals, and transmittals of individual documents. Industrial DataOps sits alongside and above document control: it treats the structured data inside and around those documents — tag attributes, equipment classes, technical specifications — as a governed pipeline with its own validation rules, monitoring, and quality metrics, independent of any single document’s approval status.
An effective industrial DataOps practice combines automated data validation against defined rules and reference data (such as CFIHOS equipment classes), continuous data quality monitoring rather than milestone-based checks, clear data ownership and change control for master data, and structured integration paths into downstream systems such as CMMS and ERP.
Sharecat gives owner-operators and EPCs a platform where tag, document, and equipment data is validated automatically as it is submitted — rather than being checked only at the final handover review. Data quality dashboards give real-time visibility into completeness and conformance across the project, and validated data flows directly into operational systems (SAP, IBM Maximo, IFS) with the underlying context and lineage intact. This turns data handover from a one-time, high-risk event into a continuously managed pipeline.
How is industrial DataOps different from regular DataOps?
Regular DataOps typically applies to software and analytics data pipelines. Industrial DataOps applies the same continuous-validation, continuous-monitoring principles specifically to engineering and asset data — tags, documents, and equipment attributes — across the lifecycle of physical capital projects and facilities.
Do I need dedicated software for industrial DataOps?
You need a platform capable of validating data against defined rules automatically and continuously, rather than relying on manual spot-checks at project milestones. Sharecat provides this specifically for engineering and asset data in capital projects and operations.
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