Challenges in Value-Driven Data Governance

Attard, Judie; Brennan, Rob

doi:10.1007/978-3-030-02671-4_33

Cited by 10 publications

(18 citation statements)

References 12 publications

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“…[12]. The concepts of data governance are sometimes abstract [6], Omnipresent [31], challenged by the highly dynamic, digitally rich educational ecosystem [21], with implementation processes that follow a non-linear path or formula [32] and [40]. Nevertheless, the fundamental concepts, tools, and techniques of data governance are interdisciplinary [9], interdependent, interrelated, and interconnected with SICT [21], economy, business, leadership, regulatory and compliance issues, privacy and security, and data quality, accessibility, and reliability [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Revamping Nigeria’s economy through sustainable data governance

Aguboshim¹,

Obiokafor²,

Ezeife³

2022

World J. Adv. Res. Rev.

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Big data technologies have intensified the need for Sustainable Data Governance (SDG). Significant empirical evidence from literature revealed that of the 2.7 zettabytes of data now in the digital universe, only 67% of organizations deployed data governance or data intelligence solutions, 76% of company executives consider information "mission-critical" or most important asset, while 46% including Nigeria had no formal governance strategy in place. Globally, significant relationships exist between SDG and Sustainable Information and Communication Technology (SICT). Data governance (DG) that is driven by SICT is agile, holistic, security-embedded, accurate, high-quality, sustainable, and on a real-time enterprise data pipeline, required in revamping any nation’s economy. Despite these global impacts of SDG in revamping the national economy, numerous investigations have shown that DG in Nigeria has remained relatively non-existent or unattended to because its approach is driven by IT that adopted rigid and fragmented processes that were carried out on a system by system basis, lacked a single version of the truth or one single reference for critical master data across geographies, business structure, and wider support of the organization. Poor sustainability of ICT in Nigeria also posed barriers that impede progress related to DG due to corrupt policies and practices, ignorance, and illiteracy that plagued SICT innovations in Nigeria. The authors adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) as the conceptual framework for this study. UTAUT model claimed that users’ acceptance behavior towards technology is determined by users’ perceived benefits of using technology and the factors that drive users’ decision to use it. A narrative review methodology was adopted in this study to review significant information based on the study conceptual framework, and existing systems that enhance SDG in revamping Nigeria’s economy. Articles reviewed include peer-reviewed articles and other documentaries within the last 5 years, extracted from electronic databases, using keywords such as “ICT and SDG”, “SDG and national economic development”, “Trends for SDG”, etc. Results from this study revealed that better decision-making, analytics, and regulatory compliance to policies, laws, and guidelines on the adoption of ICT, coupled with good formulation and communication of same, are the major drivers of sustainable DG for revamping the national economy. The result of this study may increase understanding, minimize corrupt practices and encourage trust and regulatory compliance of ICT innovations, adoption, and sustainability that can positively impact SDG for revamping Nigeria’s economy.

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Section: Literature Reviewmentioning

confidence: 99%

Revamping Nigeria’s economy through sustainable data governance

Aguboshim¹,

Obiokafor²,

Ezeife³

2022

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…For example, one of the dimensions which are mentioned in the literature is "Volume" (Laney, 2017). The volume dimension reifies concepts such as "more data more value" and leads to metrics such as counting joins in queries and an indicator of value whereby if these two things are satisfied by a database table then its value is high compared to other tables (Attard and Brennan, 2018).…”

Section: Data Value Dimensionsmentioning

confidence: 99%

“…This means it is important to have methods to decide which data is valuable and which data is not (International, 2017). Data value can play a deciding role in data management decisions as it can be used to rank the data based on its importance to the organisation, thus facilitating the decision-making process for data deletion, curation and improvement (Attard and Brennan, 2018).…”

Section: Introductionmentioning

confidence: 99%

Towards an Automatic Data Value Analysis Method for Relational Databases

Bendechache

Limaye

Brennan

2020

Proceedings of the 22nd International Conference on Enterprise Information Systems

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Data is becoming one of the world's most valuable resources and it is suggested that those who own the data will own the future. However, despite data being an important asset, data owners struggle to assess its value. Some recent pioneer works have led to an increased awareness of the necessity for measuring data value. They have also put forward some simple but engaging survey-based methods to help with the first-level data assessment in an organisation. However, these methods are manual and they depend on the costly input of domain experts. In this paper, we propose to extend the manual survey-based approaches with additional metrics and dimensions derived from the evolving literature on data value dimensions and tailored specifically for our use case study. We also developed an automatic, metric-based data value assessment approach that (i) automatically quantifies the business value of data in Relational Databases (RDB), and (ii) provides a scoring method that facilitates the ranking and extraction of the most valuable RDB tables. We evaluate our proposed approach on a real-world RDB database from a small online retailer (MyVolts) and show in our experimental study that the data value assessments made by our automated system match those expressed by the domain expert approach.

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“…Beyond the scope of pure scientific interest, the data governance, that is to say the lifecycle management of the data, is particularly crucial for our modern societies (Olhede and Wolfe (2018); Tallon (2013); Attard and Brennan (2018)). What data are publicly available?…”

Section: Context and Related Workmentioning

confidence: 99%

European Court of Human Right Open Data project

Quemy

2018

Preprint

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This paper presents thirteen datasets for binary, multiclass and multilabel classification based on the European Court of Human Rights judgments since its creation. The interest of such datasets is explained through the prism of the researcher, the data scientist, the citizen and the legal practitioner. Contrarily to many datasets, the creation process, from the collection of raw data to the feature transformation, is provided under the form of a collection of fully automated and open-source scripts. It ensures reproducibility and a high level of confidence in the processed data, which is some of the most important issues in data governance nowadays. A first experimental campaign is performed to study some predictability properties and to establish baseline results on popular machine learning algorithms. The results are consistently good across the binary datasets with an accuracy comprised between 75.86% and 98.32% for an average accuracy of 96.45%.

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Challenges in Value-Driven Data Governance

Cited by 10 publications

References 12 publications

Revamping Nigeria’s economy through sustainable data governance

Revamping Nigeria’s economy through sustainable data governance

Towards an Automatic Data Value Analysis Method for Relational Databases

European Court of Human Right Open Data project

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