Methodological Approaches to Working with Open Data in Data Journalism

Abstract

Journalism, both as an academic discipline and professional practice, is fundamentally grounded in data. The increasing prominence of data journalism underscores the field’s adaptation to digital transformation, the growing availability of data and shifting audience expectations. This article explores methodological approaches to working with open data in data journalism, with particular focus on the practical experience of Kazakhstan. The study tackles a central conceptual question: if journalism itself is inherently data-driven, what rationale exists for distinguishing data journalism as a separate methodological domain?

The article analyzes the development of data journalism in Kazakhstan within the context of the country’s open data policy and digital governance initiatives. Methodologically, the research focuses on main stages of working with open data in journalism: data discovery, evaluation of data quality, cleaning and structuring datasets, analytical interpretation, and visual presentation.

The findings highlight that effective data journalism in Kazakhstan depends on interdisciplinary collaboration between journalists, data analysts, and IT specialists, as well as on the development of data literacy within newsrooms. The article argues that open data serves as a methodological foundation for data journalism, enabling evidence-based reporting and strengthening the democratic function of the media. By contextualizing global theoretical perspectives within Kazakhstan’s media environment, this study contributes to a deeper understanding of data journalism as both a methodological practice and a socially significant form of journalism.