Data Governance and Analytics Infrastructure for Scalable Decision-Making in Development and Agritech Programs
Mohammad Abdus Sami, Md Abu Kawsar Prodhan Hemal, Md. Ishtiaque Alam, Md. Lutfor Rahman
Abstract
The global development programs are facing the transition into Agriculture 4.0 and are all at once faced with what would be called the Information Gap that 73% of the agricultural data is unstructured and 40% of the IoT-created soil information is blanketed due to bad ingestion procedures. This study will examine the application of decentralized Data Governance and Analytics Infrastructure to the scalability of decision-making. Evaluating a multi-level structure on the Master Data Management (MDM), automated data quality protocols, and cloud-native analytics in this study identifies whether standardized governance is effective in the program or not. The study is also methodologically based on a quantitative simulation of 50,000 points of smallholder farmers in 3 geographical clusters based on which it compares the traditional so-called siloed systems to the proposed infrastructure. The framework is based on the RESTful API integration and the JSON-LD metadata schema to ensure semantic interoperability of diverse streams of data, e.g., the satellite-derived NDVI indices or the localized sensor findings. An analysis of statistically validating the models and results was done using a Root Mean Square error (RMSE) reduction; the analysis found that there was a change in yield forecasting models and the value changed by reducing to 0.18 when it was 0.42. Besides that, Zero-Trust Data Security has been put in place to ensure that when traffic was highest, data uptime was ensured at 99.9% levels. The outcomes indicate that a good governance system would reduce the cost of data cleaning by 38% and the predictive value of model (R2) of the models on crop-yield increase to 0.89 instead of 0.64. Also, it is found that by reducing Data Latency by 450ms it leads to efficiency in resource allocation an increase in 15% efficiency that can be quantified as regards regional subsidies. The infrastructure also experienced a 92% reduction in metadata variance and hence ascertaining that the localized Dark Data is brilliantly converted to beneficial actional data. The results can be used as technical recommendations to NGOs and Agtech businesses to go beyond fragmented pilots to linked and information-driven ecosystems that would be replicated to provide food security interventions over large-scale humanitarian endeavors