Data governance activities: an analysis of the literature

Alhassan, Ibrahim; Sammon, David; Daly, Mary

doi:10.1080/12460125.2016.1187397

Cited by 86 publications

(64 citation statements)

References 32 publications

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“…Our data collection, coding and analysis process draws from the eight steps of data collection and coding adopted from [28,30], with an edition of one stepidentification of relevant literature [70]. Below we illustrate how these steps were used:…”

Section: Methodsmentioning

confidence: 99%

“…In this step researchers decide what the level of analysis should be [28,30]. This could be a signal word, a set of words, phrases or the entire document.…”

Section: Step 2: Decide the Level Of Analysismentioning

confidence: 99%

“…In this step, researchers decide whether to code text using a predefined set of concepts or develop a list of concepts incrementally during the coding process [28,30]. In this study concepts were developed incrementally during the coding process.…”

Section: Step 3: Decide How Many Concepts To Code Formentioning

confidence: 99%

“…In this step, researchers decide whether to code for the existence or frequency of concepts [28,30]. In this study we coded for existence of concepts.…”

Section: Step 4: Decide Whether To Code For the Existence Or Frequencmentioning

confidence: 99%

“…In this study we coded for existence of concepts. However, as frequency of the concepts recurred with the coding of the cases, frequency of codes was then considered in order to gain a deeper insight of the concepts [30].…”

Section: Step 4: Decide Whether To Code For the Existence Or Frequencmentioning

confidence: 99%

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Determining Digital Transformation Success Factors

Morakanyane¹,

O’Reilly

McAvoy

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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To understand what organizations do which brings about successful digital transformation (DX), a review of representative literature was performed. From a total of 89 articles, 16 single-case articles which sufficiently documented end-to-end DX success stories of 10 exemplar organizations were selected. Within case content analysis and asking the questions analytical procedure were employed to code each exemplar case, where a total of 174 attributes were found. Making the comparisons procedure was employed to build thematic higher-level categories by grouping similar attributes. Cross case content analysis was employed to determine presence strength of attributes in the category groupings. A list of 7 success factors and 23 subfactors emerged from the thematic groupings. These success factors constitute the initial steps towards building a DX framework which organizations seeking to embark on successful digital transformation journeys can adopt.

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Section: Methodsmentioning

confidence: 99%

“…In this step researchers decide what the level of analysis should be [28,30]. This could be a signal word, a set of words, phrases or the entire document.…”

Section: Step 2: Decide the Level Of Analysismentioning

confidence: 99%

Section: Step 3: Decide How Many Concepts To Code Formentioning

confidence: 99%

“…In this step, researchers decide whether to code for the existence or frequency of concepts [28,30]. In this study we coded for existence of concepts.…”

Section: Step 4: Decide Whether To Code For the Existence Or Frequencmentioning

confidence: 99%

Section: Step 4: Decide Whether To Code For the Existence Or Frequencmentioning

confidence: 99%

See 3 more Smart Citations

Determining Digital Transformation Success Factors

Morakanyane¹,

O’Reilly

McAvoy

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

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Understanding data culture/s: Influences, activities, and initiatives: An Annual Review of Information Science and Technology (ARIST) paper.

Oliver

Cranefield

Lewellen

2023

Asso for Info Science & Tech

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Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community.

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Data governance for wicked problems: A case from the Thai health system

Gunawong

2023

E J Info Sys Dev Countries

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This study examined how data governance practices in multi‐agency organizations manifest while trying to solve a wicked problem. This study adopted the data governance framework to analyze the Working Group that is responsible for opium addiction and treatment projects in Thailand. This framework comprises five decision domains of data: data principles, data quality, metadata, data access, and data lifecycle. The results showed that the Working Group did not have strong data principles among the relevant agencies and faced local obstacles. This led to uncertainty in the other four areas. It demonstrated, however, some effective outcomes by improvising its specific approach, which considers the relationships between various stakeholders in their dynamic adaptations within the overall data governance process. The Working Group practiced localized framework strategies, which consisted of eight approaches: data consensus in the knowledge network, strong structure, information integrity and cultural awareness, flexibility of organizational integration, dynamic adjustment process, effective information sharing, unlocking stagnations, and data governance cycles. All these strategic approaches compensated for the low performance of the current universal data governance framework.

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Data governance activities: an analysis of the literature

Cited by 86 publications

References 32 publications

Determining Digital Transformation Success Factors

Determining Digital Transformation Success Factors

Understanding data culture/s: Influences, activities, and initiatives: An Annual Review of Information Science and Technology (ARIST) paper.

Data governance for wicked problems: A case from the Thai health system

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