DATA-INFORMED DECISION MAKING IN ARTS AND CULTURAL MANAGEMENT
DOI:
https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7336Keywords:
Data-Informed Decision Making, Cultural Analytics, Arts Management, Audience Engagement, Big Data In Culture, Cultural Data Science, Data-Driven Cultural Management, Digital Transformation In Cultural InstitutionsAbstract [English]
The process of curriculum diversification due to the growing digitalization of cultural activities and the rapid development of data technologies has given new prospects of enhancing the decision-making process in arts and cultural management. Cultural institutions including museums, galleries, theaters, cultural organizations produce huge amounts of data via ticketing systems, digital platforms, social media exchanges and audience feedback systems. These sources of data offer useful information about audience behavior, participation in a given culture, and performance. Nevertheless, numerous cultural institutions continue to use their old forms of management that are mainly grounded in intuition and scanty qualitative data. This paper discusses data-informed decision making as a way of improving strategic cultural management and effectiveness of an organization. The paper conducts a review of literature on data analytics in arts and cultural management and considers some of the crucial technologies to aid cultural data analysis, such as big data analytics, artificial intelligence, machine learning, and the data visualization systems. An in-depth comparison is made of current data-driven cultural management strategies and assessed with regard to their advantages and shortcomings in aiding strategic decision making. On the basis of this analysis, the paper suggests a data-driven cultural decision-making model that incorporates numerous data sources, analytics tools, and strategic management procedures. The data collection systems, cloud-based storage infrastructure, analytics engines, visualization dashboards, and decision-support mechanisms are among the elements of the proposed framework. The framework is designed to assist the cultural institutions in enhancing raw data into practical insights, which enhance audience engagement, cultural programming, financial planning, and policy developments. Potential applications and the expected outcomes of the implementation of data-informed strategies discussed in the study are also improved audience participation, transparency in organizations, and the development of cultural policies based on evidence. Lastly, the paper also lists several challenges pertaining to data privacy, technology infrastructure, and skill development and points out the opportunities that the future research in cultural data science holds.
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