Data governance: A conceptual framework, structured review, and research agenda

Abraham, Rene; Schneider, Johannes; Brocke, Jan vom

doi:10.1016/j.ijinfomgt.2019.07.008

Cited by 349 publications

(322 citation statements)

References 68 publications

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“…Data quality is a multidimensional concept with context-independent properties of individual data values and others that are dependent on the context of use [36]. Besides this, legal and regulatory requirements [37], the differing nature of big data [38], and cloud computing [39] have an impact on DQ, as well.…”

Section: The Nature Of Data Qualitymentioning

confidence: 99%

“…In general, the incomplete and often uncertain nature of big data negatively influences their quality, making them a source of risk. All in all, future research should determine what metrics of quality are adequate to it and how accurate big data need to be [37].…”

Section: Big Data Quality and Smart Datamentioning

confidence: 99%

“…Nonetheless, we have to emphasize that data governance has the potential to improve DQ, because it can increase the level of numerous quality factors. For instance-by creating risk-mitigating policies, proper data governance enables caring for the management of risks that may arise due to non-conformance with information policies or the lack of control [37].…”

Section: Data Governancementioning

confidence: 99%

“…It is based on agreed-upon models, describing who can take what actions, when and under what circumstances, using what methods [72]. It specifies a cross-functional framework that formalizes data policies by describing standards and procedures and monitoring compliance [37]. It is important to underline that data governance should not be optional, because it runs horizontally among others by defining the nature of data, and is use in the data management process.…”

Section: Data Governancementioning

confidence: 99%

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Quality of Open Research Data: Values, Convergences and Governance

Koltay

2020

Information

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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: The Nature Of Data Qualitymentioning

confidence: 99%

Section: Big Data Quality and Smart Datamentioning

confidence: 99%

Section: Data Governancementioning

confidence: 99%

Section: Data Governancementioning

confidence: 99%

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Quality of Open Research Data: Values, Convergences and Governance

Koltay

2020

Information

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“…Little evidence has been produced so far indicating what actually has to be organized by data governance and what data governance processes may entail [20,21], and many organizations find data governance difficult to implement [22,23]. There appears to be no "one-size-fits-all" approach to data governance [24] and the nuances attached to various domains and organizational types have not yet been extensively described [25,26]. Furthermore, evidence is scant as to the role data governance plays in ensuring the successful outcomes of data science initiatives [18,19].…”

Section: Literature Backgroundmentioning

confidence: 99%

Data Governance as Success Factor for Data Science

Brous

Janssen

Krans

2020

Lecture Notes in Computer Science

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More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.

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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: A conceptual framework, structured review, and research agenda

Cited by 349 publications

References 68 publications

Quality of Open Research Data: Values, Convergences and Governance

Quality of Open Research Data: Values, Convergences and Governance

Data Governance as Success Factor for Data Science

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

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