Once you have identified data errors, it is essential to correct them promptly to ensure your data quality, reliability, and usability. To do this, you can implement various correction techniques in your information system. Data cleaning involves removing or replacing invalid, inconsistent, or outdated data values by imputing, validating, reconciling, or refreshing them. Data recovery entails retrieving or restoring lost, corrupted, or distorted data values by restoring from backup, repairing from checksum, or recovering from encryption. Data standardization involves converting or conforming the data values to a common format, range, or rule by formatting, scaling, or normalizing them. Data harmonization requires aligning or integrating the data values from various sources, systems, or formats by mapping, matching, or merging them. Finally, data filtering involves selecting or excluding the data values based on certain criteria by filtering, sorting, or aggregating them.