Case Study: Master Data Management
We designed a comprehensive data harmonization and cleansing strategy and implemented transfer programs that ensured the required data quality and included automated routines and validations.
We designed a comprehensive data harmonization and cleansing strategy and implemented transfer programs that ensured the required data quality and included automated routines and validations.
Our client, a medium-sized mechanical engineering company, faces the problem of inconsistent master data due to parallel data storage in different systems. The resulting process inefficiency manifests itself, among other things, in high administrative costs and sluggish processes. For example, duplicates in the vendor master record with different purchasing conditions mean that purchasing has to intervene manually in the ordering process.
To resolve the challenge, the customer plans to introduce a central master data management system (MDM), which will distribute the master data to the operational IT systems in the future. This will be done in compliance with defined master data processes according to a newly developed data governance. During the initial loading of the MDM system, the aim is to achieve a master data quality that forms the basis for future increases in process efficiency.
CLIENT
Detailed planning and implementation of data harmonization as well as the definition of clear processes to ensure data quality standards are essential.
Jointly with our client, we developed concepts for the initial filling of the data management system. The focus was on data harmonization, cleansing and implementation of the required transfer programs. For various master data objects, we named the respective leading systems and defined processes for harmonization. We also provided analyses and reports for data cleansing and decision support in the respective source systems. The implemented data flows contained logics for determining leading data records in case of existing duplicates and automated routines and validations to ensure defined data quality standards.
With our expertise in implementing ETL routes, many of our best practices were able to accelerate the project. Among other things, the reports we created helped our client with the iterative data cleansing of the source systems. As a result, data quality was significantly improved as soon as the system went live. Our rule-based transformation routines also ensured the targeted quality standard. The solutions developed also included the option of mass data upload from flat files, which will enable our customer to continue using the mass transfer option in the future.
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