Using Machine Learning to Automate the Analysis of Unharmonized Company Data

Teodor Todorov
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Teodor Todorov: University of National and World Economy, Sofia, Bulgaria

Innovative Information Technologies for Economy Digitalization (IITED), 2025, issue 1, 266-273

Abstract: A significant challenge for small and medium-sized enterprises (SMEs) in the process of knowledge extraction lies in ensuring the availability of high-quality, harmonized data. In SMEs, information is often stored in fragmented and poorly structured files, which makes the development of reliable analytical and predictive systems both complex and time-consuming. This paper explores the potential applications of machine learning methods for working with non-harmonized corporate data.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:nwe:iitfed:y:2024:i:1:p:266-273

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