Seven key criteria establish data quality
To work efficiently, ERP systems, digitalized processes, and self-learning machines based on AI technology require high-quality data. The ERP experts from proALPHA have revealed what makes for good data quality.09 May. 2019 Kai Tubbesing
According to ERP provider proALPHA, based in Ahrensburg in the German state of Schleswig-Holstein, data quality management is becoming increasingly important. However, many companies do not understand the relevant criteria for evaluating data quality. Poor data management rapidly impedes digitalization. That is why proALPHA has compiled seven key criteria for evaluating data quality in companies.
These criteria include completeness and up-to-dateness, which necessitate optimal wireless or mobile network coverage and a continuous flow of data. Where data is fed into different systems, data consistency must be ensured; so too must data conformity, through the use of standardized formats, for example. The accuracy of measured values should not escalate, but remain based on the actual degree of exactitude required. An automatic data quality manager helps avoid redundant datasets. Finally, the accuracy of business-relevant information, such as data from or about a supplier, needs to be ensured.
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