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Data control: definition & important aspects for buyers
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Data control in a nutshell:
Data control refers to the systematic checking and validation of data with regard to its completeness, accuracy and consistency. In purchasing, it ensures the quality of master and transaction data and thus forms the basis for reliable analyses and decisions.
Example: A purchaser carries out a monthly data check in which he checks 500 supplier data records for completeness and identifies and corrects 15 incorrect bank details and 23 outdated framework agreements.
Contents
What is data control?
Data control in purchasing refers to the systematic management, monitoring and safeguarding of procurement-relevant information. It ensures that all data is correct, complete and up-to-date to enable well-founded decisions to be made. Through effective data control, companies can optimize their purchasing processes, minimize risk management and ensure compliance with legal regulations.
Core elements of data control
Importance of data control in purchasing
Data control plays a crucial role in the procurement process as it forms the basis for strategic decisions. Accurate and up-to-date data enables buyers to evaluate suppliers, analyze costs and identify risks at an early stage. Effective data control also supports compliance, prevents fraud and improves transparency in the supply chain.
Data control: from manual checks to data governance
Building on the theoretical understanding of data control, it becomes clear how central the quality and integrity of data is for procurement. In practice, companies must ensure that all procurement-relevant data is correct and up-to-date in order to make informed decisions. Increasing digitalization and the flood of data require a shift from traditional methods to modern approaches to data management.
Old: Manual data check
Traditional approach:
In traditional data control, data management was often done manually. Purchasing staff used spreadsheets or isolated systems to record and maintain supplier data, purchase orders and contract information. This process was time-consuming and error-prone. Changes had to be updated manually and there was a high risk of inconsistencies and outdated information. The limited transparency also made collaboration between departments difficult and led to inefficient processes.
New: Data governance
Data Governance:
The modern approach relies on holistic data governance. This involves using central data management systems that establish automated processes and standards for data management. The integration of ERP systems and databases creates a uniform database. AI-supported tools enable the automatic detection and correction of data errors as well as real-time monitoring. This significantly increases data quality and consistency. Practical benefits include faster decision-making, improved compliance and a reduction in risks due to incorrect data.
Practical example: Introduction of data governance at an industrial company
An international mechanical engineering company implemented a comprehensive data governance program in purchasing. The introduction of a central data platform and automated data checks increased data quality by 85%. The transparency of supplier information led to a 30% reduction in procurement cycles. In addition, compliance violations due to outdated or incorrect data were completely eliminated, resulting in cost savings of over 2 million euros annually.
Conclusion on data control
Data control in purchasing is a fundamental building block for successful procurement processes. The systematic management and monitoring of data not only enables better strategic decisions, but also minimizes risks and ensures compliance. Data quality can be continuously improved through the use of modern technologies and automated processes. The key to success lies in the combination of robust control systems, trained employees and future-oriented technology solutions.
