Review & Analysis of Master Data Management in Agtech & Manufacturing industry

Abstract

The role of data science in Agricultural Technology (Agtech) and manufacturing industries remains a top emerging intervention that has revolutionized sustainability and productivity. The measure of this interaction is based on the need for accuracy, precision, consistency, accountability, and uniformity. Such practices would be cited in activities such as crop planning and management, precision farming, market intelligence, value chain integration, climate resilience, and risk management in Agtech. The metrics around these practices are based on the active engagement of the farmers to make observations, collect data, analyze, and create historical data historical data that improves predictive interventions. This would include using data repositories, managing inconsistent data repository standards, sustaining data repositories, and dataset appraisal. Assigning values to the dataset gathered would help the farmers make objective conclusions supported by evidence and research interventions. Transparency in data management is another major component that helps farmers improve the relevance and significance of the information obtained. The same interventions have been realized in the exercise of Master Data Management in the Manufacturing industry, with the activities being enforced to realize a balanced local and global economy, integrating direct and indirect customers and mixing different implementation styles. The concept of master data management has received increased appraisal within the manufacturing industries through the intervention of producing products that are objective to meet customer needs.