Data Management in Agricultural Enterprises: A Framework for Data-Driven Decision Support

Michal StočesJan JarolímekIrena BenešováPavel ŠimekZdeňka Žáková KroupováLudmila PánkováJiří VaněkEva KánskáMartin HavránekMichael Anderle

DOI 10.7160/aol.2026.180305
No 3/2026, September
pp. 51-60

Stočes, M., Jarolímek, J., Benešová, I., Šimek, P., Žáková Kroupová, Z., Pánková, L., Vaněk, J., Kánská, E., Havránek, M. and Anderle, M. (2026) "Data Management in Agricultural Enterprises: A Framework for Data-Driven Decision Support", AGRIS on-line Papers in Economics and Informatics, Vol. 18, No. 3, pp.51-60. ISSN 1804-1930 DOI 10.7160/aol.2026.180305.

Abstract

The growing digitalisation of agriculture is leading to a rapid increase in the volume of data produced by agricultural enterprises; however, its practical use is often limited by fragmented data sources, varying data quality, insufficient metadata, and inconsistent storage and management practices. The aim of this article is to propose a practical framework for the systematic management of data in agricultural enterprises to support its long-term availability, reuse, and integration into decision-making processes. The proposed approach is based on four interlinked steps: identifying data needs and issues, creating and recording a data inventory, drawing up a Data Management Plan (DMP), and implementing it in practice. The framework links technical aspects of data management – such as data formats, metadata, quality, integrity, security, backup and archiving – with the definition of responsibilities for individual staff members and farm (agricultural enterprise) management. For practical implementation, two basic approaches to data storage are distinguished: file systems and data platforms or repositories. The more advanced platform-based approach enables the centralisation of heterogeneous data sources, the automation of their transfer and processing, and subsequently the integration of the acquired data with analytical tools and decision-support systems. The proposed framework thus represents a path from the isolated collection of operational data to its systematic management and utilisation as a long-term information source for both operational and strategic decision-making within an agricultural enterprise.

Keywords

Agricultural data, Data Management Plan, data quality, metadata, precision agriculture, Institutional Data Repositories, Decision Support sSystems, FAIR data principles

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