Optimizing data mining processes in government databases: a comprehensive approach to strategic information analysis

Abstract

In the current technological era, marked by the massive and continuous generation of data, information must go beyond mere raw data analysis. In the governmental context, detailed data analysis is crucial to aid in the understanding and deep comprehension of complex phenomena within public administration. This type of analysis is essential for driving public policy formulation, allocating resources efficiently, and generating positive societal impacts. This article thus explores the application of data mining techniques in governmental contexts and related databases. Through a critical review of practical cases drawn from the literature, the study seeks to discuss the complexities and challenges inherent in mining data from governmental databases. The analyzed cases provide a comprehensive view of the methodologies used, highlighting best practices and common obstacles faced in the process of extracting valuable information from large volumes of governmental data. The investigation covers various aspects of data mining, from data collection and cleaning to the selection and application of specific algorithms for interpreting results. By addressing these elements, the article offers insights into the techniques and strategies for applying data mining in governmental contexts. This study is designed to serve as a reference for both researchers and professionals operating at the intersection of data science and public administration. Additionally, it aims to inform decision-making processes in the public sector, promoting more effective and efficient governance. By offering detailed insights into best practices, potential pitfalls, and emerging trends in the field of governmental data mining, the article contributes to enriching academic and professional discourse. Furthermore, it seeks to enhance public sector governance by providing the tools and understanding necessary for improved policy-making and resource management.