FOR IMMEDIATE RELEASE

Talent Search: A Data-Driven Platform Empowering Objective and Scalable Artist Discovery for A&R Professionals

Talent Search, developed by Qihang Zhang and Ruijingya Tang, addresses longstanding challenges in music talent scouting by introducing a scalable, objective, and data-driven solution. Officially launched in November 2024 after 18 months of development in California, the tool operates within Chartmetric's analytics platform and is designed to help A&R professionals discover promising new artists based on real-time streaming and social media trends. By moving beyond traditional, network-dependent scouting methods, Talent Search empowers users with actionable insights and transparent metrics, making talent evaluation more accessible and equitable.

The platform stands out for its intuitive interface, which translates complex datasets into clear, interactive visualizations. Users can explore artist growth potential through customizable charts, dynamic filters, and an evaluative color system that highlights key performance indicators. This approach not only reduces bias but also enables industry professionals to make informed decisions quickly, streamlining the process of identifying and supporting emerging talent on a global scale. Talent Search's backend processes vast amounts of data efficiently, ensuring accuracy and seamless navigation through intricate information.

Talent Search's development was rooted in extensive industry research, including stakeholder interviews and competitive analysis, to ensure it met the real needs of A&R teams. The designers overcame significant challenges in making diverse music industry data interpretable and actionable, refining their visualization techniques to balance usability with analytical depth. The result is a tool that democratizes access to music industry opportunities, allowing artists from all backgrounds to gain recognition based on measurable success. The Iron A' Design Award recognizes Talent Search's impact in fostering a more inclusive, efficient, and data-driven future for music scouting.

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