Fundamentals of Intellectual Data Analysis Using and Its Capabilities in the Knowledge Economy
In the modern economy, knowledge has become a key factor in creating value, which increases the demand for and significance of methods for its production. Given the multiple increase in the volume of data containing useful but hidden information and the emergence of digital technologies that allow for processing this data to extract the information, one of the most important methods for acquiring knowledge has become intellectual data analysis (IDA). The aim of the research is to explain the fundamentals, capabilities, and characteristics of applying IDA in the knowledge economy, as well as to provide an overall assessment of its results. The necessity of IDA for acquiring knowledge has been substantiated, its essence has been clarified, and the fundamentals of its implementation have been summarized, including typical tasks that it addresses. Considering that modern approaches to IDA are based on the use of digital technologies, the main elements of the relevant infrastructure and IDA tools have been systematized, and their impact on effectiveness has been substantiated. Considering the purpose of IDA, a comprehensive evaluation of its results in terms of knowledge acquisition has been conducted, emphasizing its inherent limitations and role in this process. Practical applications of IDA have been identified, demonstrating its expanding role in the knowledge economy. The rise of the data economy, which is evolving and developing its own distinct characteristics, has been observed. The process through which data creates value and the fundamentals of understanding their usefulness have been explained. The gradual «enrichment» of data to unlock its usefulness through IDA has been demonstrated. In conclusion, general recommendations for the development of IDA within the knowledge economy, where it has become integral, have been formulated.
Authors
- Gennadiy Shevchenko (ORCID: https://orcid.org/0000-0003-3984-9266)
- Igor Khanin (ORCID: https://orcid.org/0000-0002-4221-2314)
- Maxim Polyakov (ORCID: https://orcid.org/0000-0001-7896-2486)
- Vladimir Bilozubenko (ORCID: https://orcid.org/0000-0003-1269-7207)
- Mykola A. Nahorianskyi
Publication Details
- Journal
- Business Inform
- Published
- 2025-01-01
- DOI
- https://doi.org/10.32983/2222-4459-2025-2-181-195
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00