Data quality is not a purely technical problem, but rather an organizational and procedural one. Due to the cross-departmental and cross-system nature of data, an overarching and transparent responsibility for data quality is required (e.g. in the form of data governance). Clear responsibilities and an escalation function in data management are essential for the efficient generation and use of data by different interest groups.
How can responsibilities in data management be successfully implemented?
- We support our clients by designing and implementing data governance.
Data governance serves as a legislative body in data management and is responsible for data strategy and data quality. It is the design authority for data models, master data maintenance processes and applied validation rules. Data governance regularly monitors data quality and process efficiency in order to identify and launch optimization potential for data quality.
What criteria can be used to measure data quality?
- KPMG has developed the Data Quality Efficiency Index (DAQEI) as a key performance indicator in data management, which takes into account not only the actual data quality but also the effort required to obtain high-quality data. The DAQEI measures whether good data quality was achieved at the cost of poor process efficiency.
How can high-quality master data be achieved without making master data maintenance processes costly and time-consuming?
The right balance between complexity and efficiency is crucial in master data maintenance. Processes for creating, changing and deactivating master data should be as simple and streamlined as possible, but as complex as necessary to ensure appropriate data quality.
- We support our clients in finding the individual balance per data object as well as in the realization and technical implementation of these processes. In addition to our own MDM tool with corresponding RPA functionality, we have partnerships with all leading data management software providers and have the right mix of professional and technical expertise for the efficient implementation of MDM and data integration solutions.
How can companies start optimizing their data management?
We recommend carrying out an initial and quick data management assessment.
We use our maturity assessment for this initial evaluation of the current situation. This examines the current situation at our customers from 7 perspectives (from vision to organization, processes and technical support). A management-oriented presentation is then used to provide an insight into the current problem areas in data management.
This maturity analysis can be accompanied by KPMG's own data analysis tool to analyze data inventories at short notice and make recommendations for data cleansing activities.
