The Big Data phenomenon has imposed maturity on companies regarding the exploration of their data, as a prerogative to obtain valuable insights into their clients and the power of analysis to guide decision-making processes. Therefore, a general approach that describes how to extract knowledge for the execution of the business strategy needs to be established. The purpose of this research paper is to introduce and evaluate the implementation of a process for the experimental development of Data Mining (DM), AI and Data Science applications aligned with the strategic planning. A case study with the proposed process was conducted in a federal educational institution. The results generated evidence showing that it is possible to integrate a strategic alignment approach, an experimental method, and a methodology for the development of DM applications. Data Mining (DM) and Data Science (DS) applications also present the risks of other Information Systems, and the adoption of strategy-driven and scientific method processes are critical success factors. Moreover, it was possible to conclude that the application of the scientific method was facilitated, besides being an important tool to ensure the quality, reproducibility and transparency of intelligent applications. In conclusion, the process needs to be mapped to foment and guide the strategic alignment.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.