Under the principle of 'data sufficiency,' we understand that data is only collected, transmitted, processed, and stored to the extent necessary to achieve an energy-efficient and economical operation. This concept is categorized under 'software sufficiency' as part of digital sufficiency, according to Santarius et al. (2023). At the same time, resource-saving materials and transmission methods should be selected for data collection, transmission, processing, and storage. This approach aims to minimize the negative ecological impacts associated with the implementation of digital applications, while maximizing the benefit.

This distinguishes data sufficiency from the legal concept of data minimization. The latter is a data protection principle defined in Article 5, Section 1(c) of the General Data Protection Regulation (GDPR) for personal data and aims to reduce the risk to the rights and freedoms of natural persons. The term data minimization from the GDPR replaces the terms data economy and data avoidance previously used in the Federal Data Protection Act (§3a BDSG).This distinguishes data sufficiency from the legal concept of data minimization. The latter is a data protection principle defined in Article 5, Section 1(c) of the General Data Protection Regulation (GDPR) for personal data and aims to reduce the risk to the rights and freedoms of natural persons. The term data minimization from the GDPR replaces the terms data economy and data avoidance previously used in the Federal Data Protection Act (§3a BDSG).

While data minimization prevents risks to fundamental rights caused by extensive, unnecessary data processing, data sufficiency aims to minimize the ecological impacts of collecting, processing, and storing extensive, unnecessary data. To clarify the distinction from the legal term, different terminology is used.While data minimization prevents risks to fundamental rights caused by extensive, unnecessary data processing, data sufficiency aims to minimize the ecological impacts of collecting, processing, and storing extensive, unnecessary data. To clarify the distinction from the legal term, different terminology is used.

Research projects are inherently designed to initially generate data, the purpose of which is not always foreseeable in advance, nor is it certain that they will meet the expectations of the research. This principle of scientific work is not to be questioned by this approach; however, data sufficiency should be considered during development with a view to the later usage phase. When digital solutions developed in research projects are brought to market, the dissemination of data-efficient technologies can create ecological benefits through economies of scale.usage phase. When digital solutions developed in research projects are brought to market, the dissemination of data-efficient technologies can create ecological benefits through economies of scale.

The following presents strategies for sufficient data handling and highlights the ecological impacts of data management.The following presents strategies for sufficient data handling and highlights the ecological impacts of data management.

Literature

Santarius, Tilman, Bieser, Jan C.T., Frick, Vivian, Höjer, Mattias, Gossen, Maike, Hilty, Lorenz M., Kern, Eva, Pohl, Johanna, Rohde, Friederike, Lange, Steffen (2023): Digitale Suffizienz: konzeptionelle Überlegungen für IKT auf einem endlichen Planeten. Annals of Telekommunikation. 78, 277–295 (2023). https://doi.org/10.1007/s12243-022-00914-x