Challenges of Data Science Implementation in Indonesian Digital–Native Newsrooms: Detik.com and Kumparan

This study explores the implementation challenges of data science in Indonesian digital–native newsrooms by focusing on two leading platforms: Detik.com and Kumparan. Rather than generalizing to the entire media ecosystem, this research provides a focused comparative case study to capture institutional variations in how data science is adopted and managed across different newsroom models. The comparison between Detik.com and Kumparan is significant because both are among Indonesia′s most data‐driven digital–native media, yet they differ in organizational structure, technological resources, and innovation strategy—making them ideal cases for examining the broader dynamics of data science adoption in emerging media systems. Using a mixed‐method approach that includes in‐depth interviews, field observation, and directed content analysis, the study examines how data science is integrated into newsroom practices for content planning, audience engagement, and predictive analytics. Interviews with key personnel provide insight into internal decision‐making and organizational constraints, while content analysis of 120 news items reveals the extent and patterns of data‐driven editorial output. The findings indicate that both platforms face similar difficulties in data quality, fragmented system integration, and limited human resource capacity in analytics. Although predictive tools are being tested, their implementation remains experimental due to infrastructure and talent gaps. The study contributes to the literature on data‐driven journalism in emerging media environments by highlighting the tension between technological potential and organizational readiness.