Why is comprehensive data management crucial for corporate success and for increasing competitiveness?
Whether digitalised business processes in purchasing, production or sales – they all process data. However, processes can only be automated and compliantly executed if the data basis is error-free, complete and up-to-date.
Ensured data quality is now the equivalent of a seal of approval that creates transparency and gives users peace of mind about the accuracy of the data.
It promotes faster delivery of data not only through automated workflows and information flows, but also through reliable operational processes . It also forms the basis for decision-making.
Especially in connection with the SAP transformation that many business entities are facing, sustainable data management is leading the way. Recognising the value of clean data early on not only saves time and money, but contributes to the success of current and future projects in which data plays a role.
We therefore recommend that you lay the foundation for your digitisation projects now by taking the following steps:
- Establish of an escalation function for data-related questions early on
- Give preference to and allocate upcoming cost drivers, for example, by setting up a data map or an initial master data cleaning
- Define guiding principles as a basis for quick decision-making
Real-world data management
Data quality is not a purely technical problem, but rather an organisational and procedural one. Due to the cross-departmental and cross-system character of data, there is a need for a higher-level and transparent responsibility for data quality (e.g. in the form of data governance). Clear responsibilities as well as an escalation function in data management is essential for efficient data generation and use by different stakeholders.
How do you successfully implement data management responsibilities?
- 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 for applied validation rules. Data governance regularly monitors data quality and process efficiencies in order to identify and launch optimisation potentials for data quality.
What are the criteria for measuring data quality?
- KPMG has specifically developed the Data Quality Efficiency Index (DAQEI) as a top data management metric, which takes into account the effort to obtain high-quality data in addition to the actual data quality. The DAQEI measures whether good data quality was acquired through poor process efficiency.
How can high-quality master data be achieved without making master data maintenance processes costly and time-consuming?
In master data maintenance, the right balance between complexity and efficiency is crucial. Processes for creating, modifying and deactivating master data are 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 for each data object as well as in deploying and technically implementing these processes. In addition to our own MDM tool with corresponding RPA functionality, we have partnerships with all leading data management software providers. We also have the right mix of professional and technical know-how for an efficient implementation of MDM and data integration solutions.
How can business entities start optimising their data management?
We recommend first briefly taking stock of the current situation in data management.
- For this preliminary stocktaking, we apply our maturity assessment. This examines the current situation at our clients from 7 points of view (including vision, organisation, processes and technical support). Afterwards, a presentation geared toward management will provide insight into the current problem areas in data management.
This maturity analysis can be supported by a KPMG-owned data analysis tool to quickly analyse data assets and make recommendations for data cleansing activities.
Feel free to contact us.
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Stefan Riess
Senior Manager, Consulting
KPMG AG Wirtschaftsprüfungsgesellschaft
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