Talent Search Ohun-elo Awari Oṣere láti ọwọ́ Qihang Zhang and Ruijingya Tang

Talent Search Ohun-elo Awari Oṣere láti ọwọ́ Qihang Zhang and Ruijingya Tang jẹ́ aṣẹ́gun olókìkí púpọ̀ ti 2025 Àmì-ẹ̀yẹ Irin A' Design Award nínú Ẹ̀ka Ìdíje Àpẹẹrẹ Ojú-ìbáṣepọ̀, Ìbáṣepọ̀ àti Ìrírí Olùlò.

IrinOjú-ìbáṣepọ̀· 2025

Talent Search Ohun-elo Awari Oṣere

látọ̀wọ́ Qihang Zhang and Ruijingya Tang

Oníbàárà: Chartmetric

Aṣẹ́gun Àmì-ẹ̀yẹ Irin A' Design Award nínú Ẹ̀ka Ojú-ìbáṣepọ̀

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Aṣẹ́gun Àmì Ẹ̀yẹ Tí a Jẹ́rìísí

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Ṣàbẹ̀wò Ojú Òpó Wo ní A' Design Award Ìwé Ìròyìn Àmì Ẹ̀yẹ

Àwọ̀

Àpẹrẹ Náà

Nípa Talent Search Ohun-elo Awari Oṣere

Talent Search jẹ́ ohun-èlò tí a gbé ka ẹ̀rọ ayélujára fún wíwá àwọn tálẹ́ńtì orin. Ohun-èlò yìí ń ràn àwọn akọ́ṣẹ́mọ́ṣe ẹ̀ka orin àti àkójọpọ̀ orin lọ́wọ́ láti rí àwọn oṣèré tuntun tí wọ́n ní agbára ìdàgbàsókè tí ó lágbára. A ń yí àwọn dátà láti orí ìgbọ́rọ̀ (streaming) àti media àwùjọ padà sí àwòrán tí ó ṣe kedere àti tí ó ní ìfọwọ́sowọ́pọ̀, pẹ̀lú àwọn chart, àwọn asẹ́wádí (filters) àti àwọn àtòjọ ipò gíga. Ètò àwọ̀ ìṣirò kan ń ràn lọ́wọ́ láti fi ìfiwéra kánkán ṣe láàrin àwọn ìwọ̀n iṣẹ́ṣe. Talent Search ń dín gbígbáralé àwọn àjọṣepọ̀ ẹnìkọ̀ọ̀kan àti ìdájọ́ tara-ẹni kù. Ìlànà àpẹrẹ yìí ní ète láti mú kí ìṣirò tálẹ́ńtì jẹ́ ohun tí ó dá lórí òtítọ́ jùlọ, tí ó munadoko jùlọ, àti tí ó ṣí sílẹ̀ sí àwọn oṣèré tuntun.

Ìdánimọ̀ Náà

Nípa Àmì-ẹ̀yẹ Irin A' Design Award

Talent Search Ohun-elo Awari Oṣere jẹ́ olùborí Àmì-ẹ̀yẹ Irin A' Design Award, ìdánimọ̀ olókìkí tí a fún àwọn àwòṣe tó dára tí wọ́n pàdé àwọn ohun tí iṣẹ́ àti ilé-iṣẹ́ nílò tí a retí láti ọ̀dọ̀ àwọn ẹ̀dá tí a ṣàwòṣe dáradára. Àwọn olùborí Irin ń fi ìjáfáfá ńlá, ìmọ̀ pàtó gíga, òye ògbóǹtarìgì àti agbára ìṣẹ̀dá àwọn olùṣẹ̀dá wọn hàn. A retí pé Talent Search Ohun-elo Awari Oṣere yóò jẹ́ ẹ̀dá tí a kà sí gidigidi, tí a gbóríyìn fún tí a sì nífẹ̀ẹ́, tó wúlò tó sì ní ìmúṣẹ̀dá tuntun. Àwòṣe tó borí Àmì-ẹ̀yẹ Irin A' Design Award yìí ń kó àwọn ọ̀nà tó dára jùlọ ilé-iṣẹ́ jọ, ó sì ní àwọn ànímọ́ ìmọ̀-ẹ̀rọ tó péye tí a so mọ́ ìmọ̀ ṣíṣe àti ẹ̀bùn. A bọ̀wọ̀ fún Talent Search Ohun-elo Awari Oṣere nítorí ìfọ̀kànbalẹ̀ rẹ̀, a sì ṣàwòṣe rẹ̀ láti pèsè ìmúdára ìgbésí ayé tó ń ran ayé lọ́wọ́ láti di ibi tó dára jù.

Lẹ́yìn Àpẹrẹ Náà

Àkójọ Ìròyìn

Àwọn ẹ̀tọ́, àwọn ọmọ ẹgbẹ́ àti àlàyé ìmọ̀ ẹ̀rọ tí olùṣàpẹrẹ pèsè.

Àwọn Ohun Àkànṣe: Talent Search streamlines music scouting by leveraging data-driven insights to identify emerging artists with strong growth potential. Unlike traditional methods reliant on network referrals, it provides quantifiable performance metrics, reducing bias and scaling global talent discovery. Its intuitive design ensures data interpretability, employing accessible visualizations and an evaluative color system to enhance decision-making for industry professionals.
Ìwọ̀n àti Àpò: Talent Search runs on Chartmetric’s web-based analytics platform, utilizing a scalable backend to process large volumes of streaming and social data in real time. It employs advanced data analytics for trend detection, statistical normalization techniques for data accuracy, and an interactive front-end with dynamic filtering and visualization capabilities. Optimized for efficiency, it ensures seamless navigation through complex datasets, enabling precise and data-driven talent scouting decisions.
Àwọn Àkọlé: Talent scouting, emerging artists, music analytics, data-driven A&R, streaming insights, social media analysis, artist discovery, music industry tool, performance metrics, data visualization
Ìmọ̀-ẹ̀rọ Ìṣelọ́pọ̀: Talent Search was developed using a user-centered design approach, integrating industry research, stakeholder interviews, and iterative prototyping. Data visualization techniques enhance interpretability, employing a diverging color scale for evaluative metrics and a sequential scale for neutral data. Built within Chartmetric’s analytics platform, it leverages advanced data analytics to process streaming and social data, delivering actionable insights that empower A&R professionals in talent scouting.
Ìpèníjà Àpèsè: The biggest challenge was translating complex music industry data into intuitive, actionable insights. A&R teams rely on multiple fragmented sources, and making diverse data—from streaming numbers to social engagement—easily interpretable required extensive iteration. Balancing usability with analytical depth was key. We overcame this by refining data visualization techniques, ensuring clarity without oversimplification, and addressing industry bias by prioritizing transparent, equitable metrics.
Ìwádìí: We conducted mixed-methods research to address inefficiencies in music talent scouting. The goal was to create a data-driven tool that enhances A&R decision-making. We used stakeholder interviews, competitive analysis, and data analytics to understand user needs. Data was collected through industry consultations and Chartmetric’s database. Participants included A&R teams from record labels, streaming platforms, and management agencies.

Findings revealed that traditional scouting is subjective and network-dependent, making it hard to identify emerging artists early. Our research highlighted the need for transparent, interpretable data to support talent evaluation. Based on these insights, we designed Talent Search to present key growth metrics through interactive visualizations, enabling more informed and efficient scouting.

This innovation has reshaped talent discovery by providing objective, scalable insights that reduce bias and expand access to industry opportunities. By democratizing data, Talent Search allows promising artists—regardless of background—to gain recognition based on measurable success. It streamlines business operations for A&R professionals while fostering a more inclusive and data-driven approach to music scouting.
Ìmísí: Talent Search was inspired by the need for a scalable, data-driven approach to music scouting. Traditional methods rely on industry connections and live performances, limiting accessibility and objectivity. Our design leverages advanced analytics to surface promising artists early, empowering professionals with actionable insights. Rooted in research on music industry trends and A&R workflows, it ensures clarity, efficiency, and fairness in talent evaluation.
Àkókò Iṣẹ́ Àkànṣe: The project commenced in May 2023 in California and concluded in November 2024. After an 18-month development period, the Talent Search tool was officially launched on November 7, 2024.
Ìlànà Ìṣiṣẹ́: Talent Search allows A&R professionals to discover emerging artists by analyzing streaming and social media trends. Users can explore artists through interactive charts, filters, and rankings that highlight growth potential. The tool simplifies complex data into clear insights, helping users make informed scouting decisions. By presenting key performance metrics in an accessible way, it reduces guesswork and streamlines the process of identifying promising talent efficiently.
Àwọn Ọmọ Ẹgbẹ́: Qihang Zhang, Ruijingya Tang
Ìfọwọ́sí Àwòrán: Image #1: Illustrator Ruijingya Tang, Talent Search Interface, 2024
Image #2: Illustrator Qihang Zhang, Talent Search Interface, 2024
Image #3: Illustrator Ruijingya Tang, Talent Search Interface, 2024
Image #4: Illustrator Qihang Zhang, Talent Search Interface, 2024
Image #5: Illustrator Ruijingya Tang, Talent Search Interface, 2024