Why fixing unreliable product data needs raising of digital maturity?

In an ideal world, a company that manufactures equipment for factories, forestry, mining, or logistics has a perfect data value chain paired with a fully functional supply chain. Its product team creates new product variants into PLM (Product Lifecycle Management), defining the structure, materials, functionality, and performance characteristics. Additionally, the Engineering BOM (Bill of Material) is validated and transformed automatically to ERP.

Based on PLM and BOM, sourcing teams assign suppliers and manage purchasing details. Manufacturing receives the correct configuration through MES (Manufacturing Execution System), ensuring the assembly line builds exactly what the engineering intent was. After delivery, the service organization sees the specific variant installed on the customer’s site and identifies correct spare parts within seconds when need arises.

In the real world, these data value and supply chains are often less controlled. The engineering BOM is exported manually and re-entered into ERP. Naming conventions differ between systems, and duplicate items appear in ERP that never appear in PLM.
Next, service teams maintain their own spreadsheets to track variants delivered to customers. Finding the right replacement part demands sifting through dozens of similar items, each created by a different project lead. Mistakes accumulate. Lead times stretch. Customers lose trust.

Why do irregularities in product data occur?

In many companies, both versions of this story – low and high interoperability – happen simultaneously depending on the business or product line. The difference is not technology. The real root cause is low digital maturity, reflected in missing data governance and loopholes in master data management.

For any discrete manufacturing company, master data forms the foundation on which the business operates, succeeds, or fails. The quality of master data directly affects what becomes possible later: how quickly the right data is found, how reliably equipment can be maintained, how accurately products can be configured, how effectively supply chains run, and how confidently leaders make decisions.

Master data in PLM, ERP, and MES ensures operational consistency. On top of data consistency, modern companies need data intelligence to create value from data. For this purpose, a dedicated data platform or data fabric is necessary. It allows unifying, enriching, and analyzing data from multiple sources without disrupting operational systems. Such a platform creates governed, reusable data products that serve engineering, sourcing, service, and finance with actionable insights.

Moreover, raising the quality of master data management is essential for regulatory compliance. New and evolving EU regulations demand a level of traceability, documentation, and transparency.

The EU Digital Product Passport (DPP) is a major example of how regulation is pushing companies toward greater transparency. It requires companies to gather, preserve, and share defined product information across the entire lifecycle. This information must be structured, traceable, and interoperable across systems and suppliers. Without strong master data, DPP compliance becomes unmanageable.

The best way to avoid unreliable product data in the long run is raising the digital maturity, the invisible foundation for success in modern industrial business. Only with a good maturity level, the company can derive value from data, comply with regulations, and fully benefit from AI.

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