Sustainable Quality in Data Preparation

Data preparation is crucial for achieving good data management following the four foundational FAIR principles—Findability, Accessibility, Interoperability, and Reusability. Processing datasets to achieve high data (and metadata) quality is mandatory in modern applications. However, the data preparation activities that are needed to reach such levels may easily become unsustainable due to, for example, resource intensity or scalability challenges. Moreover, some preparation efforts may become unnecessary if they result in negligible improvements or duplicate actions. This article examines the sustainability aspects of data preparation through the lens of a circular economy. Within the data landscape, this perspective encourages practices that minimize waste, extend the data life cycle, and maximize reuse in alignment with the FAIR principles. We explore these practices and their impact on selecting and configuring effective data preparation strategies to design sustainable, high-quality pipelines. To this end, we propose an evaluation model that integrates data quality metrics with sustainability parameters for human and computational tasks. Finally, we apply the model in a comparative analysis of key data preparation methods, demonstrating its effectiveness in assessing sustainability and quality tradeoffs.