Abstract
The amount of data and the speed at which it increases grows rapidly. Companies and public institutions try to manage this increasing flood of data effectively and in a manner that adds value. Besides, the companies and public institutions also join corporate networks or platforms to increase their value by sharing their data. The evolution of traditional business intelligence into business analytics, including real-time analysis, increases the high demand for qualitative data. Data governance tries to create a framework to manage these issues. This interdisciplinary research field has now been in existence for nearly two decades. With this contribution, we attempt to provide the research field with a blueprint. This paper aims to explore the past to understand the present and shape the future of data governance. We give an overview of how the research field changed from 2005 to 2020, commenting on its development and pointing out future research paths based on our findings. We, therefore, conducted a bibliometric analysis to describe the research field’s bibliometric and intellectual structure. The findings show that for years the research field concentrated on a few topics, which currently undergoes change and has led to an opening up of the research field. Finally, the results are discussed and future research strands are highlighted
Highlights
Governance literature has traditionally used an agency theory approach, concentrating solely on identifying conflicts of interest between management and shareholders, where one group delegated work to another
Two thousand one hundred and twenty-five different authors have contributed to the research in data governance, with 2,485 author appearances in the documents
All subdomains of data governance research (DGR) that we found in the word clouds, such as big data, corporate governance, or business analytics, are represented
Summary
Governance literature has traditionally used an agency theory approach, concentrating solely on identifying conflicts of interest (agency problems) between management and shareholders, where one group delegated work to another. In a space of ten years, numerous authors realized that a governance framework is urgently needed to manage these upcoming challenges of treating data as an asset. These first multiple occurrences of contributions that dealt with topics concerning the new notion of data governance were timely related to the simultaneously increasing amount of data (Cheong & Chang, 2007; Griffin, 2005; Wende, 2007)
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