How experimental and strategic are Business Intelligence (BI) and Data Mining applications?

Rodrigo Fontes Cruz, Methanias Colaço Júnior, Victor Menezes Gois

Abstract


Objective: Identify and characterize the methodologies used for the experimental development of intelligent applications aligned with strategic planning.

Methodology: A systematic mapping was carried out to characterize the research in the area, considering the last ten years.

Originality: No scientific studies were found with the same research object of this article, to identify and characterize the methodologies for the experimental development of intelligent applications aligned with strategic planning, which increases the importance of the results presented here.

Main results: As a result, no studies were found that presented any complete approach to discipline strategic alignment and experimentation, providing clear compliance with strategic objectives and an experimental phase in the validation of results. However, some trials of parts of these characteristics could be mapped, such as experimentation found in 28,57% of the studies. Among the countries, China, the United States and Brazil led the ranking of publications on the subject. As for the medium of publication, Journal was the most used option for publication. In addition, the "IEEE International Conference on Advanced Communications, Control and Computing Technologies" and the journal "Expert Systems with Applications" stood out as major publishers.

Theoretical Contributions: This research presents results relevant to academia and entrepreneurs, providing evidence that there is a gap in research on a formal method of BI and Data Mining applications experimental and strategy-driven development. In addition, this work is presented as a source of consultation to the existing method standards for the development of intelligent applications, as well as being replicable and extended by the applied systematization. Finally, there is a focus on research that proposes methods of creating experimental applications validated experimentally and aligned with strategy.


Keywords


Strategic alignment; Business Intelligence; Data Mining; Data science.

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DOI: https://doi.org/10.5585/riae.v21i1.17689

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