The quality of organic market data: providing data that is both fit for use and convenient

Summary in the original language of the document

Members of the organic supply chain need high-quality data to make correct investment decisions, but data with sufficient depth and quality are not widely available in Europe. The quality of available data is a key concern for both data collectors and data users. The aim of this study is to identify whether the commonly used quality attributes (accuracy, coherence, comparability, timeliness, punctuality, accessibility, relevance), which have been developed from the perspective of data collectors, are also appropriate from the perspective of end users of organic market data. A further aim is to assess whether the data quality needs of end users are being met by the existing data. The results of two surveys carried out in Europe, one of data collectors and one of end users, are presented. Sales data at retail level (values and volumes) are used as an illustrative example and the perceptions of end users are compared with the reported data collection approaches, quality checks and availability of data. Correlation analysis and principal component analysis were used to investigate the relationship between users’ perceptions of the data quality attributes and their overall perceptions of
data quality. The findings suggest that data quality checks do help to improve the quality of data as perceived by end users but that people will use whatever data they can get, even if it has poor quality. This could have potentially negative consequences, such as a lack of confidence in the organic market, if important decisions are based on poor quality data. The analysis also suggests that the commonly used attributes represent two dimensions of data quality: ‘fitness for use’ which encompasses accuracy, relevance, comparability and punctuality; and ‘convenience’, which encompasses affordability, comparability, timeliness and accessibility. The attribute of comparability belongs to both dimensions as it contributes to both fitness for use and convenience. Data collectors wishing to improve the quality of their data should focus on enhancing fitness for use first and then on the convenience of their data for users.