Data quality assurance is the process of profiling the data to discover inconsistencies, and other anomalies in the data and performing data cleansing activities (e.g. removing outliers, missing data interpolation) to improve the data quality .
These activities can be undertaken as part of data warehousing or as part of the database administration of an existing piece of applications software.
Read more about Data Quality Assurance: Criticism of Existing Tools and Processes
Famous quotes containing the words data, quality and/or assurance:
“Mental health data from the 1950’s on middle-aged women showed them to be a particularly distressed group, vulnerable to depression and feelings of uselessness. This isn’t surprising. If society tells you that your main role is to be attractive to men and you are getting crow’s feet, and to be a mother to children and yours are leaving home, no wonder you are distressed.”
—Grace Baruch (20th century)
“Recent studies that have investigated maternal satisfaction have found this to be a better prediction of mother-child interaction than work status alone. More important for the overall quality of interaction with their children than simply whether the mother works or not, these studies suggest, is how satisfied the mother is with her role as worker or homemaker. Satisfied women are consistently more warm, involved, playful, stimulating and effective with their children than unsatisfied women.”
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“The characteristic of the hour is that the commonplace mind, knowing itself to be commonplace, has the assurance to proclaim the rights of the commonplace and to impose them wherever it will.”
—José Ortega Y Gasset (1883–1955)