Häufig gestellte Fragen
What is master data maintenance in ERP?
Master data maintenance covers all organisational and technical activities by which a company's central, long-lived data records — customers, suppliers, articles and materials, employees and ledger accounts — are created, checked, updated and cleansed across their entire lifecycle. The goal is for this data to remain permanently correct, complete, unambiguous and free of contradictions. Since master data forms the basis of almost all ERP processes, from purchasing through manufacturing to invoicing, its quality directly determines the reliability of reports, plans and postings. Unlike transactional data such as orders or postings, master data changes only rarely and is used across processes.
Who is responsible for master data maintenance?
A proven approach is a role model with one data owner per data domain, who carries the functional responsibility along with standards and approvals, and data stewards who handle the day-to-day maintenance. In mid-sized companies, maintenance is often handled by the respective departments such as purchasing, sales or accounting, while larger companies often set up central master data teams. Surveys show that missing or unclear responsibilities are among the most common causes of poor data quality — around one third of companies state that processes, rules and responsibilities are not sufficiently defined. Master data maintenance is therefore primarily an organisational task of the business departments and not a pure IT topic.
What are the consequences of poor master data maintenance?
Deficient master data leads to misdeliveries caused by wrong addresses, distorted reports caused by duplicates, duplicate vendor accounts, incorrect stock levels, and accounting and compliance problems caused by missing or incorrect tax IDs. Studies put the economic damage at a considerable level: research points to annual costs in the billions caused by inadequate data quality, and only around one in six companies achieves an error rate below ten percent in its material master data. In highly interconnected system landscapes, such errors multiply across interfaces to web shops, logistics providers or public authorities. Poor maintenance thus undermines the ideal of a single source of truth and makes data-driven decisions unreliable.
How can master data be maintained efficiently?
Efficient maintenance rests on clearly defined roles, strictly enforced mandatory fields and naming conventions, and approval workflows in which, for example, a newly created supplier only becomes productive after review by accounting. Automatic validation against external sources — such as address checks or VAT ID verification — and consistent duplicate detection prevent errors right at the point of entry. Regular data quality audits uncover gaps, duplicates and outdated records, since depending on the data set around ten percent of addresses become outdated each year. An audit trail also traceably logs who changed which value and when, and records changes in an audit-proof manner.
What is the difference between master data maintenance and Master Data Management?
Master Data Management (MDM) is the overarching, strategic organisation of data quality: it defines responsibilities, standards and governance, and determines which system is the master for which data and how that data is distributed to dependent systems. Master data maintenance, by contrast, is the operational implementation of these rules in day-to-day business. Large companies often implement MDM with dedicated platforms such as SAP Master Data Governance, Stibo Systems or Informatica, while the actual maintenance takes place directly in the ERP. In short: MDM sets the rules, master data maintenance applies them every day.
What distinguishes master data from transactional data?
Master data is long-lived, cross-process foundational data that changes only rarely — for example material numbers with description and unit of measure, customer addresses with payment terms, or ledger accounts. Transactional data such as orders, delivery notes or postings, by contrast, is created continuously and builds on the master data. A faulty master data record therefore affects not just a single transaction but every transaction that references that record. This leverage explains why the continuous maintenance of the comparatively few master data records is so important for overall quality in the ERP.
What quality characteristics make for good master data?
The central dimensions of data quality are considered to be completeness (all attributes needed for the process are recorded), correctness (the values reflect reality), consistency (no contradictions within a record or between systems), timeliness (the data is valid at the time of use) and uniqueness (no duplicates). Uniqueness is especially critical for customer, supplier and article master data, since duplicates distort reports and duplicate processes. These characteristics can be measured, for example via error and duplicate rates or the share of incomplete mandatory fields, and thus form the basis for targeted data quality audits. It is important not just to establish quality once but to monitor it permanently, since master data continuously becomes outdated without maintenance.