How Companies Can Build a Solid Foundation for the Data-Driven Economy

Master data forms the foundation of the data-driven economy. Its management is a fundamental prerequisite for coordination and decision-making within the context of Industry 4.0 and Supply Chain 4.0. Nevertheless, many companies only recognize its importance when errors in master data management become a serious threat to core business operations. But how do such frictions arise?

Master Data Management and Globalization

As companies increasingly globalize and integrate their value chains, the understanding of what master data is can vary significantly within a network. This is particularly relevant in procurement. Which input fields are maintained? Are values entered in kilograms or other units of measurement? Is length calculated in meters or millimeters? Such seemingly trivial issues escalate, for example, when orders are placed from regions that do not use the metric system exclusively. Another challenge arises from country-specific keyboards, especially when defining address fields or article names, or when forwarding data to warehouses for accurate processing. The results of these seemingly minor deviations can include miscalculated truck load capacities, leading to severe delays, delivery failures, and high costs.

Strategic Relevance and Managing Complexity

It is not just about the operational and tactical problems caused by poor data in a data-driven supply chain. Companies today must make decisions in ever shorter timeframes—based on data that must be both fast and accurate. Poorly maintained master data distorts the picture: incorrect priorities for customers, inaccurate revenue figures, false service levels, and unreliable lead times or quantities. This not only leads to lost speed and savings potential but also undermines trust, which is crucial in a highly integrated supply chain.

To address these challenges, consistent complexity reduction is necessary. For master data management, this means harmonizing and standardizing the entire data flow—from procurement through warehousing and production to customer interfaces—so that valid and up-to-date data is available at all times across all departments for authorized users. Our project experience shows that this process is repeatedly “corrupted” by errors such as misplaced commas, format inconsistencies, or text entries in numeric fields. Therefore, it is critical to define and systematically implement global guidelines for the structure and quality of master data.

Guidelines for Master Data Management

Developing master data management guidelines typically starts with defining uniform and binding processes. Based on this foundation, specific roles and rules are derived. Consistent and cross-departmental data field implementation is only possible if tasks and responsibilities are clearly defined. The next step involves precisely defining and describing relevant master data to eliminate or significantly reduce the errors mentioned earlier. Once these steps are implemented—ensuring the process remains streamlined, logical, and user-friendly—master data must be integrated into an overarching IT architecture, usually an ERP system. This eliminates the proliferation of storage systems, such as Excel spreadsheets, warehouse management systems, or personal notes by individual employees.

The Golden Balance Between Strategy and Execution

Despite the business-critical importance of current, valid, and integrated master data, the overall process must not be viewed solely from a strategic perspective. Doing so ignores the concrete steps and iterative loops that form the foundation of effective master data management. The focus should be on using effective tools and methods to analyze and prioritize master data, making the process transparent and developing pragmatic, scalable solutions—from master data maintenance and process optimization to comprehensive system-wide solutions.

This also includes defining the role of a Master Data Officer and equipping this position with the necessary resources, expertise, and authority. In practice, the interplay of clear structures, clean, integrated, and governance-based processes, an optimal IT structure, and a dedicated process owner is essential. Only this combination ensures that achieved improvements are maintained. Otherwise, data quality can deteriorate over time, as master data becomes outdated or neglected. Master data management must therefore be designed as a continuous, integral process—not as a limited strategic initiative.

AI as a Master Data Manager

Systems utilizing artificial intelligence (AI) now provide effective support in this process. For instance, paper invoices can be read automatically, with the information in the invoice text (including footnotes) compared to existing system data to identify and report inconsistencies. Another crucial function lies in validating the plausibility of entries. Here, AI often works with predefined thresholds to allow for minor variations while preventing severe inaccuracies that could have damaging consequences. For example, entering “11” instead of “10” may be acceptable, but entering “100” instead of “10” is not.

Another example is the management of email-based orders, which often still require manual transfer into ordering systems—a highly sensitive source of potential errors and inefficiencies. Here, AI systems intercept emails, extract relevant information such as customer data or order quantities, and automatically enter it into the system, which is connected to the ERP system. Based on this, each order undergoes a data comparison with the company-wide master data database, with automatic corrections made if discrepancies are identified.

Legal Framework: Master Data, Data Protection, and Traceability

Holistic master data management must also consider overarching legal frameworks, not just technical and process-related factors. Data protection regulations, particularly the EU GDPR, play a critical role, as illustrated by this example: in many warehouses, shipping labels containing address and customer data must be printed. According to GDPR regulations, however, such data may only be accessed and managed selectively by clearly defined personnel.

Further complications arise in global system architectures, where master data stored somewhere on servers worldwide and made available via cloud applications must now comply with legal location-specific requirements. This may require a “data relocation” from abroad to Europe. Such developments necessitate entirely new processes and rules that previously played little role in IT.

Master Data as a Hygiene Factor

Master data management is not a bureaucratic task but a business-critical process. Precisely because of its apparent triviality, it requires active backing from management. Significant resources must be allocated, and the value of master data must be communicated clearly and transparently within the company. Experience shows that master data initiatives often fail because the strategic value of valid, current, and legally compliant data is not recognized. Master data, at its core, is a hygiene factor—its value often becomes evident only when problems arise.

Particularly in the context of digitalization, cross-company networking, and the implementation of Industry 4.0, master data is becoming both more complex and more critical. A data economy cannot function without reliable data—nor can AI solutions built on flawed data foundations. In the end, it’s more than just a misplaced comma…