| Abstrakt: |
The aim of this study is to define data sources and propose methods for effective and secure data management in an agricultural enterprise in the context of using data for decision support. Current developments in information and communication technology (ICT) have contributed towards the increase in the amount of generated data in various fields. The main data sources for agricultural enterprises are the farm itself, suppliers, government, market, and research. The use of smart solutions, artificial intelligence, and other innovative practices in agriculture is discussed at many conferences, in various journals, strategies and project plans. Data is the essential raw material for all these solutions. Large amounts of data cannot be analysed efficiently with spreadsheet programs. Currently, there are trends in the use of data, for example, in business intelligence (decision-making systems), e.g. tools using online transaction processing (OLAP) or process automation or the possibility of e.g. tracing the origin of food. The availability and possibility of creating large data sets bring many challenges related to managing that data. To effectively manage farm data, it is essential to have a well-developed data management plan (DMP) used to formalise the processes related to handling. A DMP mainly addresses archiving, backup, licensing and other important aspects of data management. The challenges and developments in farm data management include incorporating artificial intelligence into data analysis and security. Food is classified as an "Entity of Critical Importance" in the NIS2 EU Directive, which also deals with cybersecurity issues. [ABSTRACT FROM AUTHOR] |
