A Conceptual Framework for Designing Data Governance for Cloud Computing

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

doi:10.1016/j.procs.2016.08.025

Cited by 36 publications

(20 citation statements)

References 7 publications

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“…This is evidenced by the results of the systematic literature review discussed above, where only 11 records discussing data governance for cloud computing were reported. Governance in the cloud needs to understand, moderate and regulate the relationships between different cloud actors or stakeholders in terms of roles and responsibilities [24]. Data governance is meant to classify and assign responsibilities, communication, labelling and policies [57].…”

Section: Cloud Data Governancementioning

confidence: 99%

Data Governance Taxonomy: Cloud versus Non-Cloud

2018

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Forward-thinking organisations believe that the only way to solve the data problem is the implementation of effective data governance. Attempts to govern data have failed before, as they were driven by information technology, and affected by rigid processes and fragmented activities carried out on a system-by-system basis. Until very recently, governance has been mostly informal, with very ambiguous and generic regulations, in siloes around specific enterprise repositories, lacking structure and the wider support of the organisation. Despite its highly recognised importance, the area of data governance is still underdeveloped and under-researched. Consequently, there is a need to advance research in data governance in order to deepen practice. Currently, in the area of data governance, research consists mostly of descriptive literature reviews. The analysis of literature further emphasises the need to build a standardised strategy for data governance. This task can be a very complex one and needs to be accomplished in stages. Therefore, as a first and necessary stage, a taxonomy approach to define the different attributes of data governance is expected to make a valuable contribution to knowledge, helping researchers and decision makers to understand the most important factors that need to be considered when implementing a data governance strategy for cloud computing services. In addition to the proposed taxonomy, the paper clarifies the concepts of data governance in contracts with other governance domains.

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Section: Cloud Data Governancementioning

confidence: 99%

Data Governance Taxonomy: Cloud versus Non-Cloud

2018

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“…The different types of cloud environment are based on the ownership, size, tenants and level of access to the cloud. The four CDMs currently identified in CCS [13], [20]- [22] are public cloud, private cloud, hybrid cloud and community cloud. Some advantages of the CDM are scalability, cost effectiveness, reliability and accessibility provided an internet connection is in place.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As discussed by [31], since there is no specific agreement when it comes to providers' side, therefore its widen the gap in security issues. Furthermore, since the data is stored in CSP's server, users may lose control of their data [13].…”

Section: B Data Governancementioning

confidence: 99%

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Data Governance Cloud Security Checklist at Infrastructure as a Service (IaaS)

Saed¹,

Aziz²,

Abdulkadir³

et al. 2018

ijacsa

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Security checklist is an important element in measuring the level of computing security, especially in cloud computing. Vulnerability in cloud computing become major concern because it will lead to security issue. While security awareness and training can educate users on the severe impact of malware, implementation on data governance and security checklist also can help to reduce the risk of being attacked. Since security checklist is important element to measure security level in cloud computing, data governance can help to manage data right with correct procedure. Due to increasing threats and attacks, service providers and service consumers need to adhere to guidelines and/or checklists when measuring the security level of services and to be prepared for unforeseen circumstances, especially in the IaaS platform. As the IaaS platform lies at the lower level in cloud computing where data are stored, it is vital that IaaS security be given serious consideration to prevent not only data breaches but also data losses. The objective of this paper is to discuss the implementation of security checklist in IaaS layer. In this paper also, several studies related with security assessment and checklist that had been discussed and developed by previous researchers and professional bodies will be discussed. This paper will also discuss the result from interview session that had been conducted by the author with several data centers (DCs) and experts regarding the implementation of security measures in small cloud DCs.

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“…Previous academic research is more about building models, proposing frameworks of data governance [13], [18], [19]. As to technique and solution research, we find there is still a gap.…”

Section: Hybrid Cloud For Enterprisementioning

confidence: 99%

MEE: A Hybrid Cloud Management Solution with Data Governance Method

Wei¹,

Rodríguez²

2018

IJCCE

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Cloud computing represents the modern way of IT efficiency. Though cloud adoption is growing rapidly, the overall adoption rate is still not ideal. This paper discusses the concerns about cloud computing from an enterprise perspective and proposes a solution of hybrid cloud with data governance methodologies. This solution uses holistic point of view, which combines data management methods and cloud computing technologies together, to answer and ease the concerns. Various topics between private cloud and public cloud are discussed and compared in the paper, include security, customization, data transfer, data/vendor lock-in, Software as a Service (SaaS) integration, cost, and resource provision. It is important to note these topics because cloud infrastructure management is not simply about cloud technologies but also data quality management. After a brief overview of data governance, a solution with modeling, enforcement, and evaluation (MEE) is proposed to manage a hybrid cloud infrastructure. This kind of research has the possibility to influence better adoption of cloud computing and improve data quality in the enterprise area.

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A Conceptual Framework for Designing Data Governance for Cloud Computing

Cited by 36 publications

References 7 publications

Data Governance Taxonomy: Cloud versus Non-Cloud

Data Governance Taxonomy: Cloud versus Non-Cloud

Data Governance Cloud Security Checklist at Infrastructure as a Service (IaaS)

MEE: A Hybrid Cloud Management Solution with Data Governance Method

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