Complete, correct and current data is essential for technical and business processes. If data changes rather slowly, then it’s probably master data. Data Scientists sometimes calls such data dimensional data.
Furthermore, master data volume is often significantly smaller than data sets including e.g. transactions, orders or KPIs. Examples for master data objects are:
- Products and services
- Business Partners like Suppliers and Customers
Master data, rules and data models
To automate master data processes, rules and constraints are widely used. Well-known representatives of such rules and constraints are e.g. existential dependency (referential integrity), mandatory / optional values or list domains like ECLASS or declaration of weight.
A certain complexity is essential for modeling complex relationships. A large number of relationships can already be mapped and monitored using appropriate data models.
Data model quality
For master data management tasks like semantic checks, classifications or duplicate and likeness checks, it’s quite more easy and efficient to use AI instead of rules and constraints.
Master Data Management-Systems
Master data management systems are used to ensure high quality master data and automate processes. Appropriate expertise and intelligent MDM tools create the prerequisites for efficient processes, satisfied customers and reliable decisions.