Harnessing Data-Driven Resilience to Empower Underserved and Rural Small Businesses Amid Economic Uncertainty

• In Event: Entrepreneurial Support Organizations as Innovation Infrastructure: Ecosystem Roles, Reach, and Policy Implications

Abstract

Background

Small businesses in underserved and rural communities often struggle with economic volatility due to limited resources, infrastructure, and support systems. This problem is now aggravated by external pressures such as tariff wars and restrictive public policies, which further jeopardize their survival, exacerbating disparities between small enterprises and larger corporations. The urgency to explore solutions is underscored by the cascading effects of reciprocal tariffs, which not only threaten the survival of individual businesses but also destabilize local economies reliant on their success. This paper explores data-driven resilience-an approach leveraging data science to optimize decision-making, resource allocation, and adaptive strategies-to mitigate these challenges, foster sustainable business operations, and improve the livelihoods of those who need it most. 

Conceptual Framework

This study is grounded in the theory of economic resilience, emphasizing how data methodologies can facilitate strategic adaptability in response to external disruptions. It integrates concepts from supply chain analytics, predictive modelling, and policy analysis, evaluating their role in enhancing local small business sustainability. The study also explores how community-based initiative plays a critical role in bridging the gap between data-driven solutions and practical implementation. This includes collaborative efforts involving local governments, nonprofits organizations, and private enterprises to facilitate data resilience amid economic uncertainty.

Data & Methods

The research will employ a mixed-methods approach. Quantitative analysis includes economic indicators and business performance metrics from small enterprises affected by tariff policies. Qualitative data will include case studies and interviews with small business owners, policy makers, and industry experts. Machine learning techniques will be used to forecast trends and assess the efficacy of data-driven strategies in mitigating economic challenges.

Forthcoming Findings

Preliminary findings suggest that data-driven solutions-such as predictive analytics, supply chain optimization, and digital infrastructure investments- substantially improve small businesses resilience amid economic uncertainty. Community-based initiatives that integrate data-sharing platforms and analytics training programs further strengthen local economies by equipping business owners with actionable insights. The analysis also highlights ethical considerations, including data privacy and equitable representation in digital transformation efforts.

Conclusion

By advocating for data-driven methodologies and community collaboration, this study presents a transformative framework for empowering underserved and rural small businesses. It argues that investment in data-driven resilience not only strengthens individual enterprises but also advances economic justice, ensuring that vulnerable communities are not left behind in the pursuit of progress.

Author

• Susan Obasi-Ikeagwu, Walden University
  Presenting Author