Data quality parameters

The meaning of ‘quality’ depends on the context in which it is applied. The term is commonly used to indicate the superiority of a manufactured good or attest to a high degree of craftsmanship or artistry. In manufacturing industries, quality is viewed as a desirable goal to be achieved through management of the production process. Statistical quality control has a relatively long history in manufacturing, where it is used to ensure conformity in products by predicting the performance of manufacturing processes. Quality is more difficult to define for data. Unlike manufactured products, data do not have physical characteristics that allow quality to be easily assessed. Quality is thus a function of intangible properties such as ‘completeness’ and ‘consistency’. On further reflection, however, these differences are perhaps not as great as they might initially seem. After all, data are the result of a production process, and the manner in which this process is performed clearly affects data reliability. Data consumers can therefore use the same diligence in selecting a database that they might in purchasing an automobile or a pair of shoes. These comments also apply in the context of geospatial data. Concern for geospatial data quality has burgeoned in recent years for the following reasons: ● Increased data production by the private sector. Historically, mass production of geospatial data was the domain of governmental agencies such as the US Geological Survey (USGS) and the British Ordnance Survey (Rhind, Chapter 56). Unlike these agencies, private companies are not required to conform to known quality standards (Goodchild and Longley, Chapter 40).