Executive summary report on the comprehensiveness and compatibility of organic market data collection methods ( = Deliverable 3.2 of the OrganicDataNetwork project)
Corinna Feldmann, Ulrich Hamm
Abstract
Open-access reader
Corinna Feldmann, Ulrich Hamm
Abstract
Open-access reader
Up to now, organic market data collection has been inconsistent, or data from different organisations and/or countries has not been comparable, because different methodologies have been used. Hence, the organic market suffers from information-asymmetry and a lack of transparency. Interpretations based on incomplete and inconsistent data might lead to wrong decisions and misinvestments. More coherent data collection and thorough data analyses are needed to overcome current dispersion and fragmentation of data sources. \nThis executive summary gives a short overview on the current situation of organic market data collection in Europe by showing some results of a European-wide survey among data collectors. Furthermore the application of quality dimensions for the identification of ‘best practice’ examples is described in order to provide recommendations for data collection and compilation, the evaluation of existing data collection methods, and the assessment of data quality.
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Up to now, organic market data collection has been inconsistent, or data from different organisations and/or countries has not been comparable, because different methodologies have been used. Hence, the organic market suffers from information-asymmetry and a lack of transparency. Interpretations based on incomplete and inconsistent data might lead to wrong decisions and misinvestments. More coherent data collection and thorough data analyses are needed to overcome current dispersion and fragmentation of data sources. \nThis executive summary gives a short overview on the current situation of organic market data collection in Europe by showing some results of a European-wide survey among data collectors. Furthermore the application of quality dimensions for the identification of ‘best practice’ examples is described in order to provide recommendations for data collection and compilation, the evaluation of existing data collection methods, and the assessment of data quality.
Key concepts: Data collection, Deliverable, Data quality, Transparency (behavior), Computer science, Data science, Business, Risk analysis (engineering)