High data quality through ERP systems.
10 July

High data quality through ERP systems

Many companies still do not know how to maintain high data quality. Yet it is precisely the wrong figures that can quickly shake an already weakening company.


ERP quality This at a time when it is demanded that the right data must be available at all times. Overall, information management is more complex than ever before. Many companies are also unaware that data management is easy to handle with the right ERP system, and that companies can even benefit from it. Prerequisite: All necessary processes are integrated into the ERP system and can be viewed quickly.

Data quality through supporting applications

In addition, suitable masks and modules can ensure the fast processing of data and at the same time reduce the susceptibility to errors. With the right solution and functions, all processes can always be kept in view and the individual steps can be traced from the beginning. Data quality is supported by mandatory fields, predefined selections, validations and guided input processes.

Greater data quality creates trust

A company can always rely on such data without reservation and thus not only satisfy its customers. High-quality data is basically what makes it possible to assess the profitability of a company. Interesting information is gained, e.g. for marketing purposes. Furthermore, wrong business decisions can be avoided because reliable information is always available. In order to understand the advantages of high data quality, here are the essential requirements:

  •  Accuracy: The data must correspond qualitatively and quantitatively to reality
  •  Credibility: A data set should not have any contradictions with other data sets.
  •  Traceability: It must be possible to trace the process by which the data was created.
  • Completeness: Of course, the data set should be complete.
  •  Current: All data should always be adapted to the current state.
  •  Error-free: For example, no data should be duplicated in data sets.
  • Importance: The data should have real relevance to the business
  •  Uniform: The contents of the data should be structured in such a way that an even result is achieved.
  • Comprehensibility: Each record must be easy to understand for the person viewing it.


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