How to keep factories and their assets in good shape with master data management and digital maturity?

Master data forms the foundation for how plants are run or how products are built and serviced. It defines how information and decisions are taken in the organization, from investment planning to customer acquisition and maintenance. The right data helps in developing predictive or preventive maintenance services based on the real need, replacing reactive and corrective maintenance.

Maximizing uptime with artificial intelligence also depends on master data. Industrial operators want to use AI for predicting breakdowns, generating maintenance tasks, lifecycle analytics, and many other tasks. With AI-powered diagnostics, plant operators have a better visibility of assets and maintenance breaks and can plan production in a more predictable way. This is feasible only if master data is structured and complete.

The core of master data consists of relatively stable, most business-critical information. For instance, a cardboard manufacturer needs documentation and specifications of its cardboard machines. It also needs process parameters, asset locations, spare parts information, and information of raw materials and its suppliers. This area of master data is usually referred to as Enterprise Asset Management (EAM).

However, simply having digital systems such as EAM and ERP doesn’t mean they will include good master data by default. Master data itself needs proper management and digital maturity to make it work as it should.

If master data is poor and not interoperable with other systems, the negative consequences can be expensive. There can be plenty of unplanned downtime caused by wrong parts, wrong instructions, or incomplete asset records. Critical spares can be missing due to duplicate or inconsistent item masters. If design information does not match reality, it causes costly rework in engineering and investment projects.

Master data does not need to be perfect, but it must consistently meet the needs of its consumers, especially engineers, service personnel, operators, planners, analysts, and automated systems. Global standards provide a good tool for improving data and raising digital maturity. For instance, the ISO 55000 standard for asset management reinforces the principle that asset information must be accurate, complete, and usable throughout the lifecycle. Another relevant standard is the ISO 8000 for data quality. It is not mandatory to obtain formal certification to selected standards.

Instead, it is highly relevant to follow the underlying guidelines they define:
• Data is relevant and suitable for its intended use
• Data complies with defined formats, structures, and business rules
• Data is structured for easy exchange and integration across systems
• Roles and responsibilities, and lifecycle management are clearly defined

Furthermore, data quality should be controlled constantly to prevent the build-up of non-qualified data. Improving master data and raising digital maturity can feel like a large undertaking, but the path forward is more manageable than most leaders expect. Companies that succeed do not aim for perfection or wait for perfect conditions. The key is to translate strategic vision into practical steps.

Are you curious to know more and start the journey toward strong master data? Find a roadmap in our e-guide “Master data and digital maturity for industrial operators” to get on the path forward!