Integrating Big Data Analytics into Monitoring, Evaluation and Learning (MEL) Systems in Tanzania: A Decision-Making Framework - Nyamwesa, Aloyce M (Universitas Djuanda, 2026)
Monitoring,​‍​‌‍​‍‌ Evaluation, and Learning (MEL) systems are essential tools in assessing programme performance and aiding in evidence-based decision-making within development initiatives. Unfortunately, MEL practices in Tanzania are usually hindered by dispersed data sources, low analytical capacity, and slow uptake of evaluation results for decision-making. This paper aims at developing a decision-making framework that will integrate Big Data Analytics (BDA) in MEL (Monitoring, Evaluation, and Learning) systems. The authors adopted a mixed-methods research design that captured qualitative data through semi-structured interviews and focus group discussions, and quantitative data through structured questionnaires administered to MEL practitioners in the health, education, agriculture, and economic development sectors. The coding of qualitative data was done through thematic analysis, and the processing of quantitative data was done through descriptive and inferential statistics. One of the major findings of the study is that the inclusion of BDA into MEL has helped to increase the efficiency of data analysis and decision support. The research further revealed the main obstacles to the adoption of BDA in MEL that are data quality issues, lack of infrastructure, lack of technical skills, and concerns related to data governance. The authors present a holistic BDA-enabled MEL decision-making framework bridging diverse data sources and sophisticated analytics to support policy evaluation, formative, developmental, and summative evaluation functions. The suggested framework incorporates practical steps for organizations willing to enhance their MEL systems and serves as a generalizable model that can be customized and empirically tested in other developing-country settings and practice in the ​‍​‌‍​‍‌future.