Development of a Data Ecosystem Model for the Context of Procurement

April 20th, 2022

Thesis Type
  • Master
Sandra Geisler
Soo-Yon Kim

Master data describes the entities on which a company’s business is built on, such as suppliers, retailers, products, or employees. Many business decisions are based on master data. For example, in supply chain management, information on purchased articles and replenishment times of purchased articles is used in production planning and sourcing. Master data also plays a role in both, intra- and cross-company information exchange. E.g., while both the suppliers and the customers maintain their own product master data, when communicating, they need to refer to the same products. While the need for high quality master data is clear, several challenges arise regarding its realization. For one, master data, once entered into the system, is typically not maintained regularly. This may lead to losses in important data quality dimensions such as accuracy or completeness. Another aspect is that there is an interest to extend the notion of data quality, widely understood as the degree to which data is “fit for use”, to account for the degree to which data can be used by various actors, or its “fitness for sharing”.

Our purpose is to develop a model which captures the specific requirements for master data quality in the context of procurement for both, intra- and cross-company purposes. To illustrate this setting, we will adapt the concept of data ecosystems. Data ecosystem models include components such as the resources of interests, the involved actors, and their relationship to each other, and highlights how data is produced, maintained, and used. Interesting questions here are, which master data flow into which procurement decisions; which data quality dimensions are required for which master data class; and which risks and potentials exist regarding cross-company data exchange and what requirements these imply for master data quality.

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