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Using Machine Learning to Automate the Analysis of Unharmonized Company Data

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  • Teodor Todorov

    (University of National and World Economy, Sofia, Bulgaria)

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.

Suggested Citation

  • Teodor Todorov, 2025. "Using Machine Learning to Automate the Analysis of Unharmonized Company Data," Innovative Information Technologies for Economy Digitalization (IITED), University of National and World Economy, Sofia, Bulgaria, issue 1, pages 266-273, October.
    • Handle: RePEc:nwe:iitfed:y:2024:i:1:p:266-273
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    File URL: https://www.unwe.bg/doi/iited/2025/IITED.2025.34.pdf
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