Data quality assurance is a collective term for the procedures used to maintain the integrity of data stored in various databases. Often the process of maintaining data quality requires tasks such as removing outdated information, cross-referencing relevant information found in different databases, and generally making sure there are no inconsistencies with the information found. in a database or set of databases. This type of data cleansing is an ongoing process that is considered a key element of efficient data management.
Data quality assurance is a collective term for the procedures used to maintain the integrity of data stored in various databases.
Companies of all kinds are dedicated to the task of ensuring data quality. Depending on the company’s operational structure, this may simply involve verifying that data stored in individual databases, such as the sales and accounts receivable and payable database, is up to date and accurate. At other times, the data quality assurance process focuses on qualifying the data before it is stored in some type of backup format, ensuring that the stored data is complete and accurate on the date it is carried out. the storage process.
The actual data quality control process is often focused on identifying and correcting any discrepancies that may be present in the data held 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 prices offered to a given customer should be the same in both the sales database and the accounts receivable database. This minimizes the chance that customers will receive inaccurate information about your current pricing structure when speaking with sales or accounting.
In some cases, the data quality assurance process involves converting the data into some common format so that the information can be archived or stored. This is not uncommon with data such as year-end accounts payable and receivable. By reconciling the data before it is stored, the information provides a complete and accurate history of prior years that can be accessed when and as needed.
One of the secondary benefits of data quality assurance is that, in the event of a system failure, the qualified and archived data that is stored can be used to partially rebuild critical databases. For example, if a company’s server fails, archived data stored on disks or even on an online data storage site can be recovered and uploaded to a new server. This leaves the task of reconstructing all the data that was entered since the last save was run, rather than having to reconstruct months of manual registry information or pay exorbitant amounts of money to have a data recovery service try to extract it. the data. the server.
