Drowning in dirty data? It's time to sink or swim: A four-stage methodology for total data quality management

Abstract

Information technology (IT) has become all-pervasive. In business, IT systems collect and authenticate data, process payments, allow access and ensure the accurate and timely delivery of stock. Today, systems share and exchange data 24 hours per day, seven days per week. In any system, there has always been 'dirty' - or erroneous - data. Today, however, the effects of 'wrong' data are much more visible and the consequences more serious. Data quality management, meanwhile, has historically been treated as a relatively low priority activity - one that is often adversely affected by budget cuts and looming deadlines. There are, however, early signs that the traditional inertia to data quality management activities is starting to change. This paper sets out a new methodology for data quality management that encourages data to be seen and managed as a corporate resource. At the heart of the methodology is the premise that data quality must be an ongoing, active and preventative process - not just a retrospective corrective activity. [PUBLICATION ABSTRACT]