Teaching Paper: Core Analytics Concepts for Non-Profit Data Strategy — From Big Data to Predictive Insights
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Teaching Paper | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-adv-dt-an-tp-04
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About the Article
Non-profits are increasingly expected to operate with efficiency, accountability, and transparency. Core analytics concepts—spanning predictive analytics, machine learning, big data, real-time analytics, and governance—provide the foundation for evidence-based strategy. This Teaching Paper introduces key terms such as regression, cluster analysis, anomaly detection, decision trees, neural networks, APIs, ETL, and master data management (MDM). Each concept is contextualized with non-profit applications, ensuring accessibility for practitioners. A case study demonstrates how a humanitarian NGO integrated predictive forecasting, anomaly detection, and real-time dashboards to improve disaster response. By bridging theory and practice, the paper equips organizations with a reference for building data literacy, adopting advanced tools, and embedding analytics into strategic workflows.
Key Topics
- Core concepts in advanced analytics for non-profits
- Predictive modeling, regression, and time series forecasting
- Machine learning methods: clustering, neural networks, reinforcement learning
- Real-time analytics and visualization tools
- Data governance, ETL, APIs, and master data management
- Practical strategies for data literacy and responsible adoption
Suggested Citation
Kazanskaia, A. N. (2025). Teaching Paper: Core Analytics Concepts for Non-Profit Data Strategy — From Big Data to Predictive Insights. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-adv-dt-an-tp-04
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