Effect of Big Data and Analytics on Financial Management in Nigeria's Public Sector

Abstract

Big Data and Analytics (BDA) in financial management are increasingly pivotal globally, offering substantial benefits for transparency and decision-making, particularly in Nigeria's public sector. The main objective is to examine the effect of big data and analytics on financial management in Nigeria's public sector. This study adopts a field survey design, the population of this study was the entire staff of the audit and finance department of the office of the auditor general of the federation. Through a pilot survey, there were 185, and all were used as sample size census sampling techniques. Primary data sourced from a questionnaire was mainly used in this study the instrument of data collection for this study is mainly a questionnaire that was personally delivered to respondents at their workplace. Using the four Likert Scale method, the responses are scored as Strongly Agree (SD) =4, Agree (A) =3, Disagree (D) =2, Strongly Disagree (SD) =1. The study employs descriptive statistics, such as mean and standard deviation, to summarize and describe the characteristics of the data collected with the aid of SPSS 23 Version software. The findings show that, the p-value for DD is 0.002, which is less than the common significance level of 0.05. The t-value is 8.635, which is significantly higher than the critical t-value for typical significance levels. Since the p-value is less than 0.05, the study rejects the null hypothesis H01. This means Data description significantly affects financial reporting quality in Nigeria's public sector while the p-value for DDi is 0.003, which is less than the common significance level of 0.05. The t-value is 5.363. Data diagnostics significantly affect financial reporting quality in Nigeria's public sector. The study recommended based on the findings that Enhance Data Description Practices. Improve the clarity, transparency, and comprehensiveness of data descriptions in financial reports. Develop standardized templates and guidelines for data descriptions to ensure consistency across different financial reports and Strengthen Data Diagnostic Processes. Enhance the effectiveness and efficiency of data diagnostic practices to improve the accuracy and reliability of financial reporting.  Conduct regular and thorough internal audits to review financial reporting practices and identify areas for improvement or potential discrepancies.