Context

Big Data Analytics is a rapidly emerging IT practice whose applications offer benefits for a wide variety of business areas across an organisation. Given the wide scope of applications, the many types of processing involved, including those for purposes not yet foreseen, and the inherent privacy concerns resulting from collecting and storing personal data, the newly introduced General Data Protection Regulation (GDPR) poses specific challenges for safeguarding the security and protection of big data. These challenges are not limited to the IT function but extend across the entire organisation. This raises the question whether Enterprise Architecture Management (EAM), as an approach for ensuring the coherence, strategic alignment and focus on value creation of all organisational resources, offers guidance for addressing those challenges in a holistic manner, and thus provides a fruitful ground for developing an approach for complying to GDPR requirements in a Big Data context.

Objective

This study surveys the state-of-the-art in research on security, privacy, and protection of big data. The focus is on investigating which specific issues and challenges have been identified and whether these have been linked to GDPR requirements. Further, it examines whether previous research has investigated the potential of EAM in addressing those challenges and what the main findings of those studies are.

Method

We used Systematic Mapping Review (SMR), which is a methodology for literature review aimed at surveying the state-of-the-art in a research field as it is documented in the scientific literature. Further, we used Template Analysis, which is a thematic analysis technique, for coding the texts of the selected papers, classifying the research studies, and interpreting the different themes addressed in the literature.

Results

Our study indicates that only few researchers have explored the use of EAM practices in relation to data security and protection in a Big Data context. We further identified seven trends within the areas under consideration that could be subjects for further research.

Conclusions

Our study does not invalidate the potential of EAM to help addressing GDPR requirements in a Big Data context. However, how EAM practices may contribute to risk management and data governance in environments where big data are being processed, is still a huge research gap, which we intend to address in our future research.

中文翻译：

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企业体系结构管理作为解决大数据环境中通用数据保护法规要求的解决方案：系统映射研究

语境

大数据分析是一种迅速兴起的IT实践，其应用程序为整个组织的各种业务领域提供了好处。鉴于应用范围广泛，涉及到许多类型的处理，包括尚未预见的处理，以及由于收集和存储个人数据而引起的固有隐私问题，新引入的通用数据保护条例（GDPR）对维护提出了特定的挑战大数据的安全性和保护。这些挑战不仅限于IT功能，还涉及整个组织。这就提出了一个问题，即企业体系结构管理（EAM）是否可以作为一种确保所有组织资源的连贯性，战略一致性并专注于创造价值的方法，

目的

本研究调查了有关大数据安全性，隐私性和保护性的最新技术。重点是调查已发现哪些特定问题和挑战，以及这些问题和挑战是否已与GDPR要求联系在一起。此外，它检查了以前的研究是否已经调查了EAM在解决这些挑战方面的潜力，以及这些研究的主要发现是什么。

方法

我们使用了系统映射评论（SMR），这是一种文献综述方法，旨在调查科学文献中记录的研究领域的最新技术。此外，我们使用模板分析（一种主题分析技术）对所选论文的文本进行编码，对研究进行分类并解释文献中涉及的不同主题。

结果

我们的研究表明，只有极少数研究人员探索了在大数据环境中与数据安全性和保护相关的EAM实践的使用。我们进一步确定了所考虑领域内的七个趋势，这些趋势可能会成为进一步研究的主题。

结论

我们的研究并未使EAM在大数据环境下帮助满足GDPR要求的潜力无效。但是，EAM实践如何在正在处理大数据的环境中有助于风险管理和数据治理，仍然是一个巨大的研究空白，我们打算在未来的研究中解决这一空白。