Data quality assurance is the process of maintaining accurate and consistent data within databases. This involves tasks such as removing outdated information and cross-referencing relevant data. It is important for businesses to engage in this process to ensure data integrity and effective data stewardship. The process involves identifying and correcting discrepancies and converting data into a common format for archiving. In the event of a system crash, qualified data can be used to partially rebuild critical databases.
Data quality assurance is a collective term for the procedures used to maintain the integrity of data hosted within various databases. Often, the data quality maintenance process involves tasks such as removing outdated information, cross-referencing relevant information found in different databases, and generally making sure there are no inconsistencies with information found within a database or a set of databases. This type of data cleansing is an ongoing process that is considered a key element of effective data stewardship.
Businesses of all types engage in the task of data quality assurance. Depending on the operational structure of the company, this may involve simply making sure that the data stored in individual databases, such as the sales database and accounts receivable, is up-to-date and accurate. Other times, the data quality assurance process focuses on qualifying the data before it is archived in some type of backup format, ensuring that the archived data is complete and accurate as of the date the archiving process takes place.
The actual data quality assurance process often focuses on identifying and correcting any discrepancies that may exist in the data managed by a business or other organization. This type of data profiling would mean making sure that similar data in one database is in harmony with data found in another database. For example, proper data management would dictate that the price extended to a particular customer must be the same in both the sales database and the accounts receivable database. This minimizes the possibility of customers receiving inaccurate information about their current pricing structure when speaking to your sales or accounting department.
In some cases, the data quality assurance process involves converting the data into a common format so that the information can be archived or stored. This is not uncommon with data such as year-end debits and credits. By reconciling data before it is archived, the information provides a complete and accurate history for previous calendar years that can be accessed when and if needed.
One side benefit of data quality assurance is that in the event of a system crash, qualified data stored in memory can be used to partially rebuild critical databases. For example, if a corporate server crashes, archived data saved to disks or even an online data storage site can be recovered and uploaded to a new server. This leaves you with the task of reconstructing all the data that has been entered since the last systems rescue, rather than having to reconstruct months of information from manual records or paying big bucks to have a data recovery service attempt to extract data from the crashed server. .
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