Mapping Organizational Data Management Patterns: A Correspondence Analysis of Firm Size, Data Automation, and Decision-Making Mode

With the advent of the digital era, an increasing number if organizations have begun to attach importance of the application to enhance their decision-making capabilities and competitiveness. Although there have many extensive research on data analysis and business intelligence at present. However, the the interrelationship between data management practices and organizational characteristics is currently not adequately explored in the research. Therefore, the objective of this study is to examine the impact of organizational attributes on data management. This study combines bibliometric analysis and literature research methods to systematically review the literature. Using VOSviewer, a knowledge map was drawn and analyzed and summarized based on the map.It has been observed that the adoption and impact of information systems and digital tools vary significantly across different enterprises. Moreover, they are also greatly influenced by the organizational environment. A structured survey questionnaire was designed to collect 140 responses from the fourth quarter of 2023 to the first quarter of 2024. Correspondence analysis was adopted as the main analytical method, and the analysis was implemented using SPSS 25 version software. Three independent correspondence analyses were execuated, and the stability of the primary dimensions across all analyses, explaining between 73% and 77% of total inertia, demonstrates the robustness of the identified structural relationships. The results indicate that firm size, geographical area, and decision-making mode consistently emerge as key drivers of organizational data management practices. Rather than estimating marginal effects, the study contributes by identifying ideal-type organizational configurations associated with different stages of data management maturity. In particular, the findings reveal a clear progression from spreadsheet-based tools to database-driven systems as firms grow. Moreover, the study highlights that the adoption of data analytics and automation should be understood as an organizational and cultural transformation rather than a purely technological upgrade. This also provides important implications for leadership development and workforce training. Overall, this research advances the understanding of organizational data capability configurations and provides actionable insights for organizational diagnosis.