Maintain your information

Data Quality and Integrity

Data Integrity and Quality is a strategy and processes, which involves verifying that the data was not corrupted during its use and remains viable for use in future tasks. Control procedures are usually engaged once the data has gone through a quality assurance process that helps to ensure that the information currently on hand is accurate, complete and consistent. Both data integrity and data quality assistance are important when it comes to maintaining useful information. Analyses performed on data will be only as accurate as the integrity and quality of its underlying data.

When data quality control is not present, the corruption of the information can lead to serious consequences. In terms of a manufacturing operation, the feeding of corrupted data could lead to the production of products that are outside the scope of what customers order, making the finished goods unacceptable to fill standing orders. Corruption in customer data files can lead to double-billing customers or possibly applying payments to the wrong invoices, leading to undesirable interactions with those clients. When maintaining the data, the key tasks are those involving the well-being of individuals, such as patients in a hospital or healthcare facility; data quality control helps to prevent the development of misinformation that could have a detrimental impact on the treatment process, possibly placing the life of the patient in danger.

With data quality control, steps should be taken to make sure data is not damaged or corrupted when it is actually used for some purpose. The idea behind quality control is to protect information so it is not lost or altered during use, making it possible to utilise the data repeatedly for other purposes in the future. Often, the process of maintaining data quality requires such tasks as removing obsolete information, cross-referencing relevant information found in different databases and, in general making sure there are no inconsistencies with the 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 administration.


Donovan Spronk

Donovan Spronk

Partner AI & Data | AWS Alliance Lead CE

Donovan is a Partner in the Consulting department and Leader of the AI & Data team within Deloitte in the Czech Republic. Donovan leads the AWS Alliance in CE, which brings the joint power of Deloitte... More