The Relevance of Data Analysis in Risk Management in Uganda
Date
2024-10-20
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Uganda Christian University
Abstract
In this regard, the study analyses the importance of data analysis in pinpointing and reducing economic risks centering on features that are public debt reduction determination, inflation abatement policy, application flattering price oscillating, political instability attention, basically includes macroeconomics climate change. This would be possible through secondary data sources, which could help inform more effectively how strong technological infrastructure, expert analytical capacities and a robust governance framework can improve risk management processes even in developing countries like Uganda. The study underscores the need for using data in decision-making on economic diversification, and maintaining macro-economic stability while also stressing that more needs to be done regarding strategies of perpetuated improvement — as well as encouraging multidisciplinary approaches to deal with new challenges (IMF 2020; World Bank 2020). The research, therefore, provides more support to the promotion of good governance and anti-corruption means using data analytics is required for better transparency in public institutions (Transparency International 2020; Ugandan Ministry of Finance 2021). This is one of the findings that demonstrates how important it is to incorporate data analysis into risk management for improved organizational resilience, effective decision-making and prevention of economic stability within an increasingly complex global environment (McKinsey & Company, 2021). Lastly, the study concludes with recommendations to government policymakers, financial institutions and academic think tanks those are involved in data-driven minimizing risk management so as enables sustainable economic development not only in Uganda but similar developing economies (Davenport & Harris 2017; Provost & Fawcett 2018). This study further develops the emerging literature relevant to data-driven risk management and sets a basis for more comprehensive research on integrating rich analytics into economic risk-management practice.
Description
Undergraduate Dissertation