Data quality processes
Data quality issues can arise for a number of reasons in any system, but especially within spreadsheet-based systems. These can range from data input typographical errors (typos) such as the misspelling of words (Birmngiham instead of Birmingham), incorrectly formatted postcodes or dates, data which has become trans-located under the wrong headings, as well as out-of-date information. All of which can lead to data integrity issues, where data are not fit for purpose.
As part of data merging and cleansing operations, all data will be checked for errors, and the data validated and checked to ensure they meet defined standards and formats. Data can also be updated as required.
A report highlighting the issues identified can be provided and we will agree with you how you would like the data handled, eg provision of an updated data set plus separate exception lists identifying the data anomalies, before proceeding with other agreed data operations.