Quality is a measure of fit for purpose, the absence of defects (an objective measure) and the possession of the desired features (subjective). Taking a simple example - a new car or a meal at a restaurant - one decides using a range of criteria how the quality of the product is perceived. The criteria and weightings used to determine the quality can differ from person to person and as a result this can be seen as a subjective measure.
The same is true of Data Quality. The quality is determined based on a number of combined criteria. What makes it more difficult to obtain and measure, is the fact that the same data can be used by many different people across the organisation, often for different purposes, at the same time. This is what makes it so important. For Data to meet Quality expectations it needs to satisfy the criteria of purpose and value for each of the different users. The quality of one attribute of data can simultaneously affect the results of many business processes, across the Information Value Chain - so Data Quality must consider the requirements of ALL users of the data.Data Quality is quite simply data that is compliant with the requirements of all the business processes that use that data. The most commonly used formal definitions for Data Quality are:
"Consistently meeting all knowledge workers' and end-customers' expectations". (Larry English - based on TQM)
"Data is of high quality if it is fit for its intended use in operations, decision making and planning". (Tom Redman - based on Joseph Juran)
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