Master Data Management: The foundation of efficient industrial operations

“From product development to production operations and aftermarket services, data is everything, and its importance is growing due to AI and new business models. Data serves as the lubricant of business. Without it, friction will occur,” says Juha Nieminen, Head of Offering and Operating Model Development at Etteplan.

Master data connects entire value chains, ensuring a smooth flow of information across departments, suppliers, and business ecosystems. Master Data Management (MDM) minimizes errors, enhances data accessibility, supports informed decision-making, ensures regulatory compliance, and establishes a foundation for digital services.

Master data should be clearly defined and standardized. It needs to be managed with a centralized approach; otherwise, inconsistencies tend to multiply. If master data is fragmented or outdated, it can cause development and production delays, procurement issues, and unhappy customers. Production optimization might become impossible.

In an industrial company, master data can consist of static data about products, assets, fleets, materials, suppliers, and customers. Connected to this, there can be highly transactional operational and maintenance data.

“Master data can include product data, specifications, design data, Bill of Material (BOM), material and parts data, supplier data and so forth. Master data standards may have hundreds of attributes. To keep data in order, you must have proper master data tools and systems”, Nieminen describes.

Producing and shipping one complex product to customers can require a massive data chain. For instance, product master data for one elevator, paper machine, mining equipment, or EV charging station must include every component as well as the complete system release including software specifications and supporting documentation.

However, many organizations struggle with poor data governance and unclear responsibilities. One of the most common issues is the existence of data silos, where different systems store conflicting versions of the same information.

“Quite often, roles and responsibilities over data are unclear and confusing. There must be someone who looks after all data and defines data policies. This requires a robust data governance model and early engagement of stakeholders,” Nieminen emphasizes.

Without clarity over data ownership, responsibility is scattered between IT, business units, and individual employees. This lack of accountability leads to problems with master data, making it difficult to support business operations effectively.

Another challenge is using incompatible systems that cannot effectively integrate or maintain master data. Lack of the right tools makes it harder to leverage automation and analytics.

If data is siloed or fragmented, it needs to be consolidated and harmonized to prevent long-term inefficiencies and reduce expensive rework. This involves removing duplicates or inconsistencies based on common master data standards – AI tools can be of great help.

“Typically, harmonization projects are associated with ERP projects, migrations from various on-premises systems to a single cloud platform or occur after a company acquisition. There’s no benefit in executing a cloud implementation with poor data only to find out later that we have a problem,” Nieminen notes.

Harmonization projects can be time-consuming due to the sheer amount of data. According to Nieminen, the minimum duration is at least several months. Combined with an ERP project and a simultaneous cloud migration, the project will take much longer. The better the existing master data has been managed, the faster and easier a project is ahead.

A product master data lifecycle starts in R&D, where product models with materials, components, and specifications are established. Correctly defining required items, attributes and characteristics from the beginning ensures accurate and consistent data throughout the entire value chain. When R&D has done this well, the transfers to manufacturing and procurement run smoothly. Structured master data is equally essential for digital products.

Well-organized master data ensures efficient management of updates, maintenance, and compliance requirements. The accuracy of this data is also crucial for creating product documentation, maintenance manuals, and aftermarket services.

In asset-intensive industries, asset data is critical for tracking, maintaining, and optimizing physical assets – from single machines to complete plants. An Enterprise Asset Management (EAM) system is helpful because it centralizes asset data. If master data of assets is spread across multiple systems and is inconsistent, it increases the risk of unexpected failures and costly downtime.

A well-structured MDM strategy enhances efficiency, reduces risks, and provides a foundation for future innovations, product management, asset operations, and aftermarket services. Prioritizing master data enables a significant advantage in increasingly data-dependent industries.

Are you interested in learning more about MDM and data governance, Asset Operations and Management, or R&D? Feel free to reach out to us using the form below!