Data governance activities: a comparison between scientific and practice-oriented literature

Abstract

Purpose The purpose of this paper is to explore the current literature on data governance in scientific and practice-oriented publications, and to provide a comparative analysis of the activities reported for data governance. Data have become a key organisational asset and data governance both a necessary and critical activity. Design/methodology/approach A comprehensive literature review is conducted in order to identify the published material that reflects the current state of knowledge. A systematic procedure was followed that identified 61 publications that explicitly mention data governance activities. Open coding techniques were applied to conduct content analysis, resulting in the identification of 591 concepts. A critical analysis also identified gaps in the literature. Findings The analysis identified 120 data governance activities which are understood as: “action” plus “area of governance” plus “decision domain” (e.g. define data policies for data quality). The authors define and present a data governance activities model based on the analysis. The analysis also shows a higher volume of data governance activities reported by practice-oriented publications that are associated with the “implement” and “monitor” actions of the areas of governance across the decision domains compared with scientific publications, whereas The authors found that the scientific publications focus more on defining activities. The results contribute to identifying research gaps and concerns on which ongoing and future research efforts can be focused. Research limitations/implications This paper is of interest to both academics and practitioners, as it helps them understand the activities associated with a data governance programme. Current literature fails to provide a comprehensive understanding of the data governance activities that are required when considering a data governance programme. Therefore, the proposed model for data governance activities can be used to give insights into these activities. Originality/value To the knowledge of the authors, this study is the first to explicitly consider data governance activities from both an academic and practice-oriented perspective.

Data Access in Connected Health Innovation: Managerial Orchestration Challenges and Solutions

“…By identifying challenges and potential solutions at the intersection of innovation network orchestration and data management in the context of connected health, it adds to the existing knowledge that assumes data availability as a central part of network orchestration, and/or expects that securing information mobility is a matter of motivating the parties to share their data and knowledge (see, for example, Dhanaraj & Parkhe, 2006;Möller & Halinen, 2017). Likewise, it adds to the discussion on managing data from the point of view of privacy concerns (see Alhassan et al, 2018;Corso & Paolucci, 2001). This study therefore contributes to the innovation management and network orchestration literatures in the context of connected health, where data-driven innovations such as AI-based decision support solutions need to be continuously developed in order to improve the quality of care and costeffectiveness (Pikkarainen et al, 2018).…”

“…This paper is based on the integration of theoretical frameworks on data management activities (Alhassan et al, 2018), and on information (knowledge) mobility as a central innovation network orchestration activity (Hurmelinna-Laukkanen & Nätti, 2018;Nambisan et al, 2017;Nambisan & Sawhney, 2011;Sabatier et al, 2010). Such integration enables a better understanding of innovation network orchestration challenges from the perspective of data access in the healthcare sector.…”

“…These may be grouped into three general categories: individual level (referring to representatives of different organizations), organizational level (referring to stakeholder organizations such as hospitals, companies, government agencies), and institutional level (that is, regional, national, and international frames for working beyond organizational boundaries, including legislation Connected Health Innovation: Data Access Challenges in the Interface of AI Companies and Hospitals Laura Kemppainen, Minna Pikkarainen, Pia Hurmelinna-Laukkanen & Jarmo Reponen and policies) factors. These categories can be approached based on data management activities (see Alhassan et al, 2018). Summarizing the above discussion, and going into more fine-grained detail, we identify managerial orchestration challenges and solutions as mapped in Table 2 below.…”

“…Previous studies on collaborative innovation emphasize the importance of organizing data access and knowledge transfer in the collaboration process (Alhassan et al, 2018;Hurmelinna-Laukkanen & Nätti, 2018). Such need for data, information, and knowledge exchange is highly relevant in the context of AI and connected health.…”

Data Governance as a Collective Action Problem

“…The lack of a widely accepted definition of data governance (Pierce et al 2008) continues to permeate the field (Al-Ruithe et al 2018;Benfeldt Nielsen 2017;Brous et al 2016). One stream of research argues that data governance should function as the means to resolve poor data quality, emerging from the field of data quality management (Alhassan et al 2016(Alhassan et al , 2018Al-Ruithe et al 2018;Cheong and Chang 2007;Otto 2011a;Soares 2010;Weber et al 2009). While little attention is paid to what is actually meant by resolving poor quality, Otto mentions ensuring availability of specific data sets to remain compliant with regulatory or legal provisions, effective reporting, and integrated customer management (2011b).…”