Data acquisition and data quality

Abstract

One must never forget the dubious origin of the source data. The resultant data sometimes takes up a life of its own. Beware, no magic formula exists! (Reeve. 1997) The current situation is one where research is suffering from DRIPS Data-Rich-Information-Poor Syndrome Data is available in vast volumes – whether in book format or in digital real-time format. One must ensure that the data that is being gathered is reliable, has been gathered in the strictest manner and that it pertains to the topic under study and not to an alternative dataset. One is never sure of the problems and issues pertaining to data gathering, however knowledge of what that data holds is necessary. Data has to be turned into information to avoid a DRIPS situation. Again the issue of context comes up. Information needs context so one needs to know which situational background that information was based on. If in default DRIPS occurs. The next section gives an overview of which data forms exist and focuses on the topic of METADATA, which is the process of how one ensures that data has a context within which it was created and that it serves as a veritable ID card/Passport for that particular dataset.