What is master data?

Master data is the linchpin of every modern, digitalised company.

This article looks at their definition, the importance of master data management and data governance and the challenges involved in managing them. If you know these basics, you can play an active role in shaping your company's success.

Master data is fundamental, usually long-term information that serves as the basis for almost all of a company's business processes and decisions. It forms the backbone of the digital infrastructure.

It is also crucial for the identification, classification and characterisation of facts. Often referred to as static data or basic data, it rarely undergoes changes, unlike transaction data.

Typical master data includes the following:

• Customers: Name, address, contact details
• Materials: Number, description, price, technical data
• Suppliers: Name, address, conditions
• Employees: personal details, department, position

Information of this type is usually managed in ERP, CRM or special master data management systems so that it is centrally available and consistent. It is essential for processing orders, warehouse transactions, production processes, purchase orders, invoices and personnel management.

It is important that the content of the data records is correct and up-to-date. Otherwise, errors will occur in the processes. High data quality in master data is therefore a decisive competitive factor.

Master data is important in all areas of a company.

Some examples are

• Sales: Basis for customer approach and order processing
• Production: definition of the characteristics of products to be manufactured
• Purchasing: essential information for managing suppliers and procurement
• Financial accounting: essential for charts of accounts, basis for financial transactions with debtors and creditors
• Human resources: management of employees, basis for payroll accounting

Master Data Management (MDM) is a comprehensive approach to managing a company's master data. The aim is to create a centralised, consistent and accurate view of the most important data across all systems and departments - a "single source of truth".

Master data management coordinates and harmonises fragmented and redundant data to ensure high data quality. The underlying process controls the collection, storage, maintenance and distribution of master data throughout its entire life cycle.

In order to implement master data management professionally, companies usually use a specially designed system. This approach has several advantages:

• Improved data quality: consolidations and cleansing reduce errors and inconsistencies. They also lead to more reliable data for analyses and decisions.
• Increased efficiency: The centralised master data basis enables smoother processes and reduces redundant data entries.
• Better compliance: Master data management helps to fulfil legal requirements through transparent master data management.
• Improved customer and supplier relationships: A unified view of customer information enables personalised offers. An accurate master data basis for suppliers ensures optimised procurement processes.

Implementing master data management is initially an organisational challenge for many companies. However, it is particularly worthwhile for large and internationally active groups that work with large volumes of data on a daily basis.

This is because a standardised data structure reduces costs in the long term and improves data quality - even if it initially requires a certain investment of time and resources.

Data governance is a comprehensive framework that defines guidelines, processes, roles and metrics for the effective management and utilisation of company data. It ensures that data is treated as a valuable asset by delegating responsibilities and setting standards for data quality, security, protection and availability.

A robust data governance programme creates transparency about data responsibilities and usage, ensures data integrity and controls the entire data lifecycle. Without clear data governance, however, inconsistencies, security gaps and compliance issues can arise. Establishing this is of great importance for success in the digital age.

The quality and consistency of master data directly influence the efficiency of processes and the reliability of information. Data governance has a significant influence on these aspects. For example, it ensures that the master data models are clearly defined.

Such models determine who owns the master data, who is authorised to change it and which approval processes are required. This prevents unauthorised and incorrect changes to critical data.

Another key aspect of data governance in the context of master data is ensuring data quality. This is guaranteed by defined rules and processes that ensure correct, complete, consistent and up-to-date master data. Corresponding measures include data cleansing, validation during data entry and regular checks.

Data governance also plays a decisive role in data protection and data security for master data. Clear guidelines for access, storage and processing are essential, especially for sensitive customer or personnel data.

Data governance defines access rights and security measures to protect this information. This is important to comply with legal regulations (e.g. GDPR), to maintain customer trust and also to protect against cyberattacks.

Finally, data governance promotes transparency and traceability in the handling of master data. It clearly documents where the data comes from, how it has changed and who was responsible for changes. This is important for audits, troubleshooting, error analyses and continuous improvement.

The management of master data is a complex task that presents companies with a variety of challenges. These can be of a technical, organisational or procedural nature. An inadequate approach can have far-reaching negative consequences for efficiency, data quality and business success. A proactive approach is therefore of great importance.

The three biggest challenges in master data management are

• Quality and accuracy of master data
• Data protection and security aspects
• Data migration

These aspects are described in more detail below.

Quality and accuracy of master data

The quality and accuracy of master data is a key challenge for companies. This is because master data can lose quality over time due to manual errors, a lack of standards or insufficient maintenance. Poor data quality leads to the following problems, for example:

Wrong decisions: inaccurate analyses and reports

• Inefficient processes: Delays in ordering, incorrect deliveries or incorrect invoices
• Increased costs: unnecessary stock levels, excessively expensive procurement of materials
• Loss of reputation: damage to customer trust

To avoid such scenarios, data quality must be continuously optimised. This requires continuous maintenance measures, data quality rules, employee training and special tools.

The responsibility lies not only with IT, but also with all departments involved. This is because departments such as sales, purchasing and accounting use different systems or modules, but access the same information. Only if maintenance is organised jointly can the database remain reliable.

Data protection and security aspects

Cyber threats are increasing and data protection laws are becoming ever more restrictive. Data protection and security aspects are therefore of great importance in master data management. After all, master data often contains sensitive information that must be protected against unauthorised access, misuse or loss. Typical challenges in this area can be outlined as follows:

• Compliance with legal regulations: Compliance with data protection laws (e.g. GDPR) and industry-specific regulations through clear guidelines for data collection, storage, processing and deletion
• Access management: control which employees or systems have access to which master data (using role-based access controls)
• Data security: robust security measures (encryption, firewalls, intrusion detection systems, regular audits) to protect against cyberattacks, data leaks and internal misuse
• Data resilience: strategies for backing up and restoring master data in the event of system failures or disasters, including regular backups and disaster recovery plans

Overcoming these security challenges is a technical and organisational task that requires the awareness and responsibility of all employees. Constant adaptation to new cyber threats and legal requirements is also necessary.

The aim is to ensure the conformity, integrity and confidentiality of important data at all times. This is important for legal protection on the one hand and for stakeholder trust on the other.

Data migration

Another challenge in master data management is data migration.

It is necessary when companies introduce new systems, consolidate existing systems or when mergers and acquisitions take place. In such scenarios, large amounts of old data often have to be transferred from heterogeneous systems to new environments.

This process is risky, as data is often stored in different formats, is inconsistent or incomplete.

Careful planning and implementation of the data migration is essential to avoid data loss, damage or the transfer of incorrect information. Appropriate measures include a detailed analysis of the source systems, a definition of the target structures, the cleansing of data, its transformation and extensive testing.

Errors in this process can lead to considerable problems in the operational business and to a lasting impairment of data quality. A professional, structured approach is therefore recommended. Sufficient budget and time should also be planned for data migrations.

In the digital age, master data is the foundation of every company's business activities. Its quality, consistency and accessibility are crucial for efficient business processes, precise analyses and well-founded strategic decisions.

Overcoming challenges such as data quality and data protection requires a strategic approach, supported by master data management and data governance. These disciplines ensure a reliable "single source of truth" for critical information.

The continuous maintenance and protection of important data ultimately ensures the functionality, resilience and future viability of an organisation.