A Systematic Review of Recent Literature on Data Governance (2017–2023)

Abstract

In today’s rapidly changing environment, organizations are fighting a decisive battle for the most effective use of data. Owing to technological innovation, the volume, velocity, variety, variability, and veracity of data gathered, stored, and processed by organizations in electronic systems are rapidly growing. Analytics, process mining, and artificial intelligence are among the modern application domains of data, enabling data-driven decision making and process innovation for an operating advantage. Data governance, encompassing standards, policies, responsibilities, and relations for managing data, is essential for organizations to maximize the value of the use of data in an effective, cost-efficient, safe, and compliant way. Although data governance has matured as a scientific and business discipline in recent years, the formal definition of data governance and its implementation practices in organizations are still facing ambiguity. New regulations in data protection (e.g., the European Union’s General Data Protection Regulation) and safe and ethical data processing (e.g., the European Union’s Artificial Intelligence Act) further increase the pressure for compliance and conformity in organizations’ management of their data assets. Applying the systematic literature review approach, our objective was to capture state-of-the-art data governance research. The literature review provides an incremental analysis of the most relevant published work on data governance in the period from 2017 to 2023, complementing and enhancing previous systematic literature reviews. The study examines in detail 38 publications, refreshing scientific knowledge and providing further orientation for a growing community of scholars and practitioners in the dynamically evolving data governance discipline.

Keywords

• Analytics
• artificial intelligence
• big data
• data governance
• systematic literature review