A data cooperative is a legally organized cooperative whose purpose is the collective collection, management, and use of data collected by its members. In contrast to other data pools, it can act as a democratic self-governing organization in which each member has a say in how data is collected, shared, and analyzed. The members retain control over the data they contribute. They collectively benefit from the resulting data services, for example, through new insights, innovative services, or economic advantages. As a forward-looking model, the data cooperative enables smaller players to access reliable data infrastructure and data resources that would otherwise remain inaccessible. It promotes data sovereignty and strengthens trust in data-driven cooperation. Here, the definition of data derives from the field of business informatics, which gives a scientifically sound typology of data, systematized on several levels. The following elaboration offers conceptual clarification and presentation of central data types. Depending on the context and processing purpose, their classification is essential for business informatics, as they form the raw material for information systems and business processes. Data cooperatives are gaining in importance as a cooperative form of organization enabling democratic co-determination, community benefit, and equal access to data for regional actors.
- data cooperatives
- GovTech
- regional development
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Structured data: Data that follows a predetermined format, usually in tables, databases, or standardized forms (e.g., customer database; “Structured data offers high accessibility for algorithmic processing” [3]).
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Unstructured data: Data that is not subject to a fixed scheme, such as text, images, audio, or video files. They are more difficult to evaluate automatically, but they make up a large part of the digital data volume (“Unstructured data such as emails and social media posts are less directly usable for classic analysis methods” [2]).
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Semi-structured data: Hybrid forms such as Extensible Markup Language (XML) and JavaScript Object Notation (JSON) files, in which data is in a certain format but is not fully tabulated (“Semi-structured data combines flexibility with machine readability” [4]).
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Primary data: Data collected directly in the investigation process, e.g., through surveying, observation, or measurement (“Primary data are original data sets whose quality can be directly controlled” [4]).
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Secondary data: Pre-existing data that are reused for new analyses (e.g., statistics, research databases; “Secondary data are crucial for efficiency and availability but can pose quality risks” [2].
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Master data: Relatively stable data for the identification and description of business objects, e.g., customer, product, or supplier data (“Master data is indispensable for the integrity of operational processes” [3]).
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Transaction data: Data that depicts business processes and transactions, such as orders, invoices, or bookings (“Transaction data documents the operational dynamics in companies” [4]).
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Metadata: Data about data, e.g., information about the time of origin, source, format, or permissions of the actual data (“Metadata is essential for discoverability and governance of information resources” [2]).
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Streaming data: Data generated or transmitted continuously and in real time, such as sensor data in the Internet of Things (IoT) (“Streaming data enables real-time analysis of operational processes” [2]).
References
- Baden-Württemberg Ministry of Economic Affairs. Ministry of Economic Affairs Is Funding Pilot Projects on Data Cooperatives of Companies with 1.4 Million Euros. 2020. Available online: https://wm.baden-wuerttemberg.de/de/service/presse-und-oeffentlichkeitsarbeit/pressemitteilung/pid/wirtschaftsministerium-foerdert-pilotprojekt-zu-datengenossenschaften-von-unternehmen-mit-14-million (accessed on 12 December 2025).
- Weber, P.; Groß, N.; Grieser, F. Cooperatives as a Legal Framework for IoT Ecosystems. Data Cooperative. In Hohenheimer Genossenschaftsforschung; Hess, S., Ed.; Universität Hohenheim: Stuttgart, Germany, 2021.
- Krcmar, H. Information MANAGEMENT. In Information Management; Springer: Berlin/Heidelberg, Germany, 2015; pp. 85–111.
- Otto, B.; Österle, H. Corporate Data Quality: Prerequisite for Successful Business Models; Springer Nature: Berlin/Heidelberg, Germany, 2016; p. 205.