Understanding Data Analysis in Non-Profit Organizations: Processes, Practices, and Applications
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global | NEYA Global Publishing
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-uda-2025
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About the Article
This article explores how non-profits can embed structured data analysis into their operations to improve accountability, decision-making, and long-term sustainability. It outlines the core steps of defining objectives, collecting data, cleaning and preparing datasets, analyzing and modeling, and finally interpreting results for action. Through illustrative case studies from health, education, fundraising, social services, and poverty alleviation, the article shows how systematic data use strengthens transparency, improves outcomes, and builds stakeholder trust.
Key Topics
- Defining objectives for data-driven practice
- Data collection, cleaning, and preparation methods
- Analytical and modeling techniques in non-profits
- Interpretation and communication for diverse stakeholders
- Case-based applications in health, education, and fundraising
- Organizational readiness and staff capacity building
- Ethical safeguards and responsible data management
- Future opportunities with AI, big data, and predictive analytics
Academic Value
By combining theoretical insights with applied strategies, this article contributes to the academic–practice interface on evidence-based management in the non-profit sector. It provides practitioners with a structured framework for institutionalizing data analysis, while also opening pathways for research on capacity-building and ethics in resource-constrained contexts.
Suggested Citation
Kazanskaia, A. N. (2025). Understanding Data Analysis in Non-Profit Organizations: Processes, Practices, and Applications. NEYA Global Publishing. https://doi.org/10.64357/neya-gjnps-uda-2025
References
Ebrahim, A. (2003). Accountability in practice: Mechanisms for NGOs. World Development, 31(5), 813-829.
https://doi.org/10.1016/S0305-750X(03)00014-7
Kazanskaia, A. N. (2025). Advanced Data Analytics Techniques for Social Impact. NEYA Global Publishing.
https://doi.org/10.64357/advanced-data-analytics-social-impact-2025
Kazanskaia, A. N. (2025). Basics of data analysis in non-profit organizations: Data types, collection methods, and foundational statistics. Neya Global Journal of Non-Profit Studies.
https://doi.org/10.64357/neya-gjnps-bda-2025
Kazanskaia, A. N. (2025). Data analysis templates for nonprofit decision-making - Practical tools for survey, financial, program, and donor data. Neya Global Journal of Non-Profit Studies.
https://doi.org/10.64357/neya-gjnps-da-temp-tp
Kazanskaia, A. N. (2025). Introduction to Data Analysis for Non-Profits. NEYA Global Publishing.
https://doi.org/10.64357/introduction-data-analysis-nonprofits-2025
Kazanskaia, A. N. (2025). Understanding data analysis in non-profit organizations: Processes, practices, and applications. Neya Global Journal of Non-Profit Studies.
https://doi.org/10.64357/neya-gjnps-uda-2025
Kazanskaia, A. N. (2025). Quantitative Research Methods. NEYA Global Publishing.
https://doi.org/10.64357/quantitative-research-methods-2025
Nguyen, L., & Williams, J. (2021). Data-driven program improvements in education. Education Research Review, 33, 100-115.
Patel, R., & Clark, S. (2020). Understanding donor behavior through data analysis. Non-Profit Fundraising Journal, 12(3), 45-62.
Patton, M. Q. (2015). Developmental evaluation: Applying complexity concepts to enhance innovation and use. Guilford Press.
Rivera, M., & Chen, Y. (2018). Data-driven poverty alleviation strategies. Development Studies Journal, 14(2), 67-84.
Smith, A., & Brown, T. (2022). Resource allocation in health non-profits: A data-driven approach. Journal of Health Management, 25(1), 15-30.
Zhao, H., & Thomas, K. (2019). The role of data analysis in non-profit transparency. Social Services Quarterly, 55(4), 221-238.
