SUSTAINABLE MANAGEMENT OF FOLK ART THROUGH DATA ANALYTICS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6865Keywords:
Folk Art Management, Data Analytics, Cultural Sustainability, Machine Learning, Market Optimization, Heritage PreservationAbstract [English]
Sustainable management of folk art is becoming very essential in the conservation of cultural heritage and the economic sustainability of artisan communities. This paper suggests a data analytics-based system to increase the sustainability of folk art ecosystems through the combination of market insight, resources optimization, and community engagement. The challenge of folk art in modern economies that are usually marginalized is associated with the lack of demand variability, unfair pricing and online irreachability. Through descriptive, predictive and prescriptive analytics, this study will be able to determine some of the key parameters that affect the long-term viability of the folklore art practices. The consumer trends and seasonality are analyzed with demand forecasting models, whereas the pricing analytics can help to allocate the fair value across the artisans, intermediaries and markets. The analysis also evaluates the sustainability measures, including environmental (material use and waste minimization), economic (income stability and diversity in the market) and social (cultural involvement and the intergenerational transfer of knowledge). An index of sustainability based on data is suggested to assess policy interventions and in order to optimize the allocation of resources to the artisan clusters. The predictive accuracy of the framework is improved by the implementation of AI and ML technologies like clustering, sentiment analysis, and regression modeling. This project eventually serves as a bridge between the historical cultural heritage administration and the modern analytics to achieve the sustainable development agenda by empowering the folk art communities and enhancing the intangible cultural resources in the digital economy.
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Copyright (c) 2025 Sachin Pratap Singh, Akhilesh Kumar Khan, Ms. Rutu Bhatt, Sahil Khurana, Shailesh Solanki, Dr. Satish Upadhyay, Milind Patil
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