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Schachtner, C.; Baumann, N. Data Cooperatives and Their Impact on GovTech as a Regional Development Approach for Digital Transformation at a Local Level. Encyclopedia. Available online: https://encyclopedia.pub/entry/59388 (accessed on 14 January 2026).
Schachtner C, Baumann N. Data Cooperatives and Their Impact on GovTech as a Regional Development Approach for Digital Transformation at a Local Level. Encyclopedia. Available at: https://encyclopedia.pub/entry/59388. Accessed January 14, 2026.
Schachtner, Christian, Nadine Baumann. "Data Cooperatives and Their Impact on GovTech as a Regional Development Approach for Digital Transformation at a Local Level" Encyclopedia, https://encyclopedia.pub/entry/59388 (accessed January 14, 2026).
Schachtner, C., & Baumann, N. (2025, December 30). Data Cooperatives and Their Impact on GovTech as a Regional Development Approach for Digital Transformation at a Local Level. In Encyclopedia. https://encyclopedia.pub/entry/59388
Schachtner, Christian and Nadine Baumann. "Data Cooperatives and Their Impact on GovTech as a Regional Development Approach for Digital Transformation at a Local Level." Encyclopedia. Web. 30 December, 2025.
Peer Reviewed
Data Cooperatives and Their Impact on GovTech as a Regional Development Approach for Digital Transformation at a Local Level
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia6010003

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
This entry examines the challenge digital transformation means for municipalities in terms of using data as a central resource for innovation and efficiency. The state of this research is still young; in particular, there is a lack of empirical studies on the impact and scalability of data cooperatives in the GovTech context. Basically, data cooperatives can act as enablers for GovTech and regional development, provided that legal, technical, and cultural conditions are created. They transfer the classic cooperative principle to the digital world [1]. In combination with GovTech—digital technologies and services for public administration—data cooperatives offer the potential to accelerate the modernization of administration, promote data-driven innovation, and strengthen regional value creation. Data is the basis of all information processing and is typified according to different criteria to enable its use for operational, analytical, and strategic purposes [2]. Data are formalized representations of facts or observations that are available digitally for processing, storage, analysis, or communication [3]. Practice-related examples from Baden-Württemberg and Hesse show how new business models and smart solutions for municipalities can be created through the shared use of data. However, challenges include legal uncertainties, a lack of standards, and limited experience with cooperative data models in administration.
Typology of data by degree of structuring
  • 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]).
  • 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]).
  • 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]).
Typology of data by source and origin
  • 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]).
  • 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].
Typology of data by context of use
  • 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]).
  • Transaction data: Data that depicts business processes and transactions, such as orders, invoices, or bookings (“Transaction data documents the operational dynamics in companies” [4]).
  • 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]).
Typology of data by processing and updating frequency
  • Batch data: Data processed at periodic intervals, e.g., daily settlements [4].
  • 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]).
The differentiated typification of data is fundamental for the development and operation of digital business models and information systems. It determines architecture, governance, analysis procedures, and legal frameworks in the management of digital processes [3]. Business informatics systems must take these typologies into account both conceptually and technically to ensure data quality, data protection, and value creation.

References

  1. 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).
  2. 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.
  3. Krcmar, H. Information MANAGEMENT. In Information Management; Springer: Berlin/Heidelberg, Germany, 2015; pp. 85–111.
  4. Otto, B.; Österle, H. Corporate Data Quality: Prerequisite for Successful Business Models; Springer Nature: Berlin/Heidelberg, Germany, 2016; p. 205.
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Subjects: Management
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Christian Schachtner ,
Nadine Baumann
View Times: 10
Book: Encyclopedia of Digital Society, Industry 5.0 and Smart City
Online Date: 30 Dec 2025
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