Assessments of the data management practices in zakat institutions in Malaysia: a concept paper - Awang Abu Bakar, Normi Sham; Yahya, Norzariyah; Abdullah, Lili Marziana; S. Abd. Aziz, Madihah; Baharuden, Ahmad Fazreen; Rosli, Rozaini; Jumaan, Ibrahim Ali (IEEE XPLORE, 2025)
This paper assesses the data analytics practices within zakat institutions in Malaysia, focusing on the challenges and opportunities presented by the increasing complexity of data management. As zakat institutions are responsible for collecting and distributing funds to eligible recipients, efficient data handling and analysis are critical for decision-making and ensuring transparency in zakat distribution. This study examines the current state of data management in institutions like Lembaga Zakat Selangor (LZS) and explores how advanced data analytics, including predictive models and machine learning algorithms, can enhance the efficiency of zakat management. By employing a mixed-methods approach, the research investigates both quantitative data from zakat payers and qualitative insights from institutional staff. The study highlights the potential of predictive analytics to forecast future zakat payers and improve collection strategies. Key findings emphasize the need for better data integrity and the integration of technologies such as Random Forests and XGBoost to optimize zakat collection and distribution. The paper concludes that adopting comprehensive data analytics practices is essential for modernizing zakat institutions, ensuring greater accountability, and achieving more effective poverty alleviation through zakat funds.