Plausibility checks refer to the process of verifying data for logical consistency and credibility. These checks help identify and correct errors and inconsistencies in datasets to ensure data quality.
Data Validation: Automatic verification of entered data for logical consistency and compliance with predefined rules and standards.
Error Detection: Identification of inconsistencies, missing, or contradictory data within a dataset.
Real-time Verification: Immediate plausibility check during data entry to report errors directly and enable corrections.
Report Generation: Creation of reports on found errors and inconsistencies, including their frequency and nature.
Automatic Correction Suggestions: Proposals for possible corrections based on detected errors and existing rules.
Rule-based Checks: Creation and application of specific check rules based on the requirements and standards of the respective company or industry.
Data Integrity: Ensuring data integrity through regular checks and validations to maintain consistent and reliable data over the long term.
Anomaly Detection: Identification of unusual or striking patterns in the data that might indicate errors or irregularities.