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

Data management solutions on their own are becoming very expensive and not able to cope with the reality of everlasting data complexity. Businesses have grown more sophisticated in their use of data, which drives new demands that require different ways to handle this data. Forward-thinking organizations believe that the only way to solve the data problem will be the implementation of an effective data governance. Attempts in governing data failed before, as they were driven by IT, and affected by rigid processes and fragmented activities carried out on system by system basis. Up to very recently governance is mostly informal with very ambiguous and generic regulations, in siloes around specific enterprise repositories, lacking structure and the wider support of the organization. Despite its highly recognized importance, the area of data governance is still under developed and under researched. Since data governance is still under researched, there is need to advance research in data governance in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of data governance. In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the state-of-the-art of research in data governance. The objective of the study is to provide a credible intellectual guide for upcoming researchers in data governance to help them identify areas in data governance research where they can make the most impact. The systematic literature review covered published contributions from both academia and industry. The presented SLR searches and examines most relevant published work since year 2000 to-date for data governance for non-cloud, and for cloud computing since 2007. Only 52 studies met the inclusion criteria, which are critically reviewed.

A systematic literature review of data governance and cloud data governance

While governing data as an organizational asset has clear benefits, mobilizing an organization to implement data governance remains elusive for practitioners. On that account, this paper examines why governing data is difficult in local government organizations. Based on a literature review and an empirical case study, we establish the inherent challenges and build on the notion of collective action to theorize the problem of data governance. Following an engaged scholarship approach, we collect empirical material through six group interviews with 34 representatives from 13 different Danish municipalities. We extend existing data governance research with our problem triangle that identifies and explicates the complex relations between six distinct challenges: value, collaboration, capabilities, overview, practices, and politics. We demonstrate the value in theorizing data governance as a collective action problem and argue for the necessity of ensuring researchers and practitioners achieve a common understanding of the inherent challenges, as a first step towards developing data governance solutions that are viable in practice.

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Data Governance as a Collective Action Problem

Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance, and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of ‘responsible data governance’, applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by governance of neuroscience big data in the Human Brain Project (HBP).

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Responsible Data Governance of Neuroscience Big Data

This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability.,Initially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis.,The most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy.,In addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities.,To transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology.,A vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.

Olivia Benfeldt Nielsen

Papers

A Comprehensive Review of Data Governance Literature