A data quality assurance program is an explicit combination of organization, methodologies, and activities that exist for the purpose of reaching and maintaining high levels of data quality. The term assurance puts it in the same category as other functions corporations are used to funding and maintaining. Quality assurance, quality control, inspection, and audit are terms applied to other activities that exist for the purpose of maintaining some aspect of the corporation's activities or products at a high level of excellence. Data quality assurance should take place alongside these others, with the same expectations.
Just as we demand high quality in our manufactured products, in our financial reports, in our information systems infrastructure, and in other aspects of our business, we should demand it from our data.
The goal of a data quality assurance program is to reach high levels of data accuracy within the critical data stores of the corporation and then keep them there. It must encompass all existing, important databases and, more importantly, be a part of every project that creates new data stores or that migrates, replicates, or integrates existing data stores. It must address not only the accuracy of data when initially collected but accuracy decay, accurate access and transformation of that data, and accurate interpretation of the data for users. Its mission is threefold: improve, prevent, monitor.
Improvement assumes that the current state of data quality is not where you want it to be. Much of the work is to investigate current databases and information processes to find and fix existing problems. This effort alone can take several years for a corporation that has not been investing in data quality assurance.
Prevention means that the group should help development and user departments in building data checkers, better data capture processes, better screen designs, and better policies to prevent data quality problems from being introduced into information systems. The data quality assurance team should engage with projects that build new systems, merge systems, extract data from new applications, and build integration transaction systems over older systems to ensure that good data is not turned into bad data and that the best practices available are used in designing human interfaces.
Monitoring means that changes brought about through data quality assurance activities need to be monitored to determine if they are effective. Monitoring also includes periodic auditing of databases to ensure that new problems are not appearing.