Data in manufacturing
manufacturing data: sensor and machine data (IoT), OEE and quality metrics, supply-chain and traceability data, and MES/ERP integration.
Manufacturing runs on data that spans the plant floor and the boardroom: sensor streams from PLCs and SCADA systems, MES production records, quality inspection logs, supply chain and inventory feeds, and equipment maintenance histories. This block builds fluency in how manufacturers structure, source, and evaluate this data, from OEE calculations to defect tracking to predictive maintenance datasets. You will examine the metrics that define data quality and reliability in industrial settings, understand the regulatory and cybersecurity frameworks governing industrial data (including IP protection and OT security), and learn the audit practices that keep data trustworthy across interconnected factory systems. The focus stays on data infrastructure, measurement, and governance, not general financial analysis.
What you'll master
- Identify and map the core data sources across a manufacturing operation, from IIoT sensors to ERP and MES systems
- Calculate and interpret key manufacturing analytics metrics such as OEE, first-pass yield, and MTBF/MTTR
- Assess data quality and governance maturity across production, quality, and supply chain data pipelines
- Apply industrial data privacy, IP protection, and OT security checks to identify governance gaps
Key terms
Modules
Applies core data concepts to real manufacturing use cases like sensors, OEE, traceability, and system integration.
Covers the manufacturing data landscape, master data, quality metrics, governance, and analytics maturity.
Covers industrial data regulation, worker and process privacy, supplier data sharing, and governance audits.
Latest articles
Recent articles from the blog that apply to Manufacturing.
- Track Boeing's component data the way their regulators now demandWhen a 737 MAX fastener is installed without a traceable birth record, the liability lands on the assembler, not the tier-3 supplier who made it. Boeing's multi-year effort to close that gap shows what end-to-end traceability actually costs to build, and what it costs more to ignore.
- How Siemens built a unified manufacturing data backbone by integrating MES and ERPWhen Siemens restructured its Amberg electronics plant around a tightly integrated MES and ERP stack, the payoff was not just faster reporting, it was a fundamental shift in how production decisions get made. Here is what they actually did, what the numbers show, and what CDOs in discrete manufacturing can take from it.
- From data asset to data product: the CDO's most urgent strategic shiftMost organizations are sitting on data goldmines they've never learned to extract value from. The shift from managing data as an internal asset to engineering it as a monetizable product is redefining what CDO leadership actually means.