A systematic literature review of data governance and cloud data governance

Al-Ruithe, Majid; Benkhelifa, Elhadj; Hameed, Khawar

doi:10.1007/s00779-017-1104-3

Cited by 124 publications

(81 citation statements)

References 26 publications

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“…As we pointed out, there is a close relationship between research data and business data; it is worth noting that until the early 1990s, data were not treated as valuable business assets, but regarded to be by-products that lose most of their value beyond a given transaction [18].…”

Section: Changing Views On the Nature Of Datamentioning

confidence: 99%

Quality of Open Research Data: Values, Convergences and Governance

Koltay

2020

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This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.

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Section: Changing Views On the Nature Of Datamentioning

confidence: 99%

Quality of Open Research Data: Values, Convergences and Governance

Koltay

2020

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“…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).…”

Section: Data Governancementioning

confidence: 99%

“…Without a problem, there is blind groping in the dark" (Dewey 1938, p. 3). The prevailing lack of a common direction of inquiry (Al-Ruithe et al 2018;Benfeldt Nielsen 2017;Brous et al 2016) could indicate there is still some groping in the dark, when it comes to data governance. Yet, if formulation of research problems is taken for granted or not made explicit, it is likely that essential characteristics remain overlooked, resulting in inadequate or misdirected solutions (Volkema 1995).…”

Section: Implications and Suggestions For Future Researchmentioning

confidence: 99%

Data Governance as a Collective Action Problem

2019

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While governing data as an organizational asset has clear benefits, mobilizing an organization to implement data governance remains elusive for practitioners. On that account, this paper examines why governing data is difficult in local government organizations. Based on a literature review and an empirical case study, we establish the inherent challenges and build on the notion of collective action to theorize the problem of data governance. Following an engaged scholarship approach, we collect empirical material through six group interviews with 34 representatives from 13 different Danish municipalities. We extend existing data governance research with our problem triangle that identifies and explicates the complex relations between six distinct challenges: value, collaboration, capabilities, overview, practices, and politics. We demonstrate the value in theorizing data governance as a collective action problem and argue for the necessity of ensuring researchers and practitioners achieve a common understanding of the inherent challenges, as a first step towards developing data governance solutions that are viable in practice.

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“…As stated before, SLRs are a systematic approach for the search and evaluation of the available literature in a given topic, providing a strong foundation of the state of the art and giving an overview of the current strengths and weaknesses of the field of interest. For that reason, and even though conducting a systematic P O S T review requires a great amount of work and dedication (Petticrew and Roberts, 2008), SLRs have reached a considerable level of adoption in many research fields (Eachempati and Srivastava, 2017), (Hordri et al, 2017), (Franco-Bedoya et al, 2017), (Al-Ruithe et al, 2018), (Nelson and Olovsson, 2016). Table 1 shows the different stages needed to perform a SLR (Tsafnat et al, 2014).…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Information retrieval methodology for aiding scientific database search

Marcos-Pablos

García-Peñalvo

2018

Soft Comput

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During literature reviews, and specially when conducting systematic literature reviews (SLRs), finding and screening relevant papers during scientific document search may involve managing and processing large amounts of unstructured text data. In those cases where the search topic is difficult to establish or has fuzzy limits, researchers require to broaden the scope of the search and, in consequence, data from retrieved scientific publications may become huge and uncorrelated. However, through a convenient analysis of these data the researcher may be able to discover new knowledge which may be hidden within the search output, thus exploring the limits of the search and enhancing the review scope. With that aim, this paper presents an iterative methodology that applies text mining and machine learning techniques to a downloaded corpus of abstracts from scientific databases, combining automatic processing algorithms with tools for supervised decision making in an iterative process sustained on the researchers' judgement, so as to adapt, screen and tune the search output. The paper ends showing a working example that employs a set of developed scripts that implement the different stages of the proposed methodology

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A systematic literature review of data governance and cloud data governance

Cited by 124 publications

References 26 publications

Quality of Open Research Data: Values, Convergences and Governance

Quality of Open Research Data: Values, Convergences and Governance

Data Governance as a Collective Action Problem

Information retrieval methodology for aiding scientific database search

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