Evaluation of a process for the Experimental Development of Data Mining, AI and Data Science applications aligned with the Strategic Planning

Abstract

Context: 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. Purpose: 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. Method: A case study with the proposed process was conducted in a federal educational institution. Results: 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. Conclusion: 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.