A Comprehensive Review of Data Governance Literature

Organizations have found that seemingly tedious data problems are fundamentally business problems, and cannot be solved by the IT group alone. Public organizations routinely store large volumes of data about its citizens and while analysis of this data can improve decision-making and better address individual needs, this fails due to a lack of data governance. Data governance has received growing attention from both practitioners and academics as a promising approach to solving organizational data issues. This paper presents a review of data governance literature, classifying authors, research disciplines, methods and related theoretical fields, providing researchers with an overview of this emerging field. The paper is concluded by suggesting four areas for future development of the data governance field in the context of the public sector

A Model for Data Governance – Organising Accountabilities for Data Quality Management

Enterprises need data quality management (DQM) that combines business-driven and technical perspectives to respond to strategic and operational challenges that demand high-quality corporate data. Hitherto, companies have assigned accountabilities for DQM mostly to IT departments. They have thereby ignored the organisational issues that are critical to the success of DQM. With data governance, however, companies implement corporate-wide accountabilities for DQM that encompass professionals from business and IT. This paper outlines a data governance model comprised of three components. Data quality roles, decision areas and responsibilities build a matrix, comparable to a RACI chart. The data governance model documents the data quality roles and their type of interaction with DQM activities. Companies can structure their company-specific data governance model based on these findings

A MORPHOLOGY OF THE ORGANISATION OF DATA GOVERNANCE

Both information systems (IS) researchers and practitioners consider data governance as a promising approach for companies to improve and maintain the quality of corporate data, which is seen as critical for being able to meet strategic business requirements, such as compliance or integrated customer management. Both sides agree that data governance primarily is a matter of organisation. However, hardly any scientific results have been produced so far indicating what actually has to be organised by data governance, and what data governance may look like. The paper aims at closing this gap by developing a morphology of data governance organisation on the basis of a comprehensive analysis of the state of the art both in science and in practice. Epistemologically, the morphology represents an analytic theory, as it serves for structuring the research topic of data governance, which is still quite unexplored. Six mini case studies are used to evaluate the morphology by means of empirical data. Providing a foundation for further research, the morphology contributes to the advancement of the scientific body of knowledge. At the same time, it is beneficial to practitioners, as companies may use it as a guideline when organising data governance

Corporate data quality management in context

Presently, we are well aware that poor quality data is costing large amounts of money to corporations all over the world. Nevertheless, little research has been done about the way Organizations are dealing with data quality management and the strategies they are using. This work aims to find some answers to the following questions: which business drivers motivate the organizations to engage in a data quality management initiative?, how do they implement data quality management? and which objectives have been achieved, so far? Due to the kind of research questions involved, a decision was made to adopt the use of multiple exploratory case studies as research strategy [32]. The case studies were developed in a telecommunications company (MyTelecom), a public bank (PublicBank) and in the central bank (CentralBank) of one European Union Country. The results show that the main drivers to data quality (DQ) initiatives were the reduction in non quality costs, risk management, mergers, and the improvement of the company's image among its customers, those aspects being in line with literature [7, 8, 20]. The commercial corporations (MyTelecom and PublicBank) began their DQ projects with customer data, this being in accordance with literature [18], while CentralBank, which mainly works with analytical systems, began with data source metadata characterization and reuse. None of the organizations uses a formal DQ methodology, but they are using tools for data profiling, standardization and cleaning. PublicBank and CentralBank are working towards a Corporate Data Policy, aligned with their Business Policy, which is not the case of MyTelecom. The findings enabled us to prepare a first draft of a "Data Governance strategic impact grid", adapted from Nolan& MacFarlan IT Governance strategic impact grid [17], this framework needing further empirical support

Big data analytics: a state-of-the-art review

International Conference on Data Science and Applications (ICONDATA'19) (2. : 2019 : Balıkesir, Turkey)Big data analytics has been a subject for debate, discussions and arguments. However, the applicability and challenges of big data in terms of three views (i.e., data diagnosticity, data diversity and data governance) has been widely ignored. This paper provides a brief overview for data diagnosticity, data diversity and data governance in line with information value. In essence, this paper raises interesting and importance issues facing big data usage and concludes with a number of research questions that needs urgent attention.Büyük veri analizi, müzakere, fikir çatışma ve tartışmalara konu olmuştur. Bununla birlikte, büyük verilerin üç farklı bakış açısından (yani veri tanılaması, veri çeşitliliği ve veri yönetişimi) uygulanabilirliği ve dezavantajlarına rağmen, aralarındaki ilişkiyi inceleyen çalışmalar ilginç bir şekilde sınırlı düzeydedir. Bu çalışmada, bilgi değeri doğrultusunda veri tanılaması, veri çeşitliliği ve veri yönetişimi hakkında kısa bir genel değerlendirme sunulmaktadır. Bu konu esasıyla, bu çalışmada büyük veri kullanımının karşı karşıya kaldığı enteresan ve önemli konuları gündeme getirmekte ve acil dikkat gerektiren bir dizi araştırma sorusu ile sonuçlanmaktadır.No sponso

Evaluation criteria for information quality research

Evaluation of research artefacts (such as models, frameworks and methodologies) is essential to determine their quality and demonstrate worth. However, in the information quality (IQ) research domain there is no existing standard set of criteria available for researchers to use to evaluate their IQ artefacts. This paper therefore describes our experience of selecting and synthesising a set of evaluation criteria used in three related research areas of information systems (IS), software products (SP) and conceptual models (CM), and analysing their relevance to different types of IQ research artefact. We selected and used a subset of these criteria in an actual evaluation of an IQ artefact to test whether they provide any benefit over a standard evaluation. The results show that at least a subset of the criteria from the other domains of IS, SP and CM are relevant for IQ artefact evaluations, and the resulting set of criteria, most importantly, enabled a more rigorous and systematic selection of what to evaluate.This is the author accepted manuscript. The final version is available from InderScience via https://doi.org/10.1504/IJIQ.2016.1000404

A sustainable approach for data governance in organizations

Bento, P., Neto, M., & Corte-Real, N. (2022). How data governance frameworks can leverage data-driven decision making: A sustainable approach for data governance in organizations. In A. Rocha, B. Bordel, F. G. Penalvo, & R. Goncalves (Eds.), 2022 17th Iberian Conference on Information Systems and Technologies (CISTI): Proceedings (pp. 1-5). (Iberian Conference on Information Systems and Technologies, CISTI). IEEE Computer Society. https://doi.org/10.23919/CISTI54924.2022.9866895With the technological advances, organizations have experienced an increasing volume and variety of data, as well as the need to explore it to stay competitive. Data governance (DG) importance emerges to support the data flow, to record and manage knowledge derived from data, as well as establishing roles, accountabilities, and strategies, which further results in better decision-making. Through the definition of strategies to manage data in a consistent manner, data governance establishes the path to an enterprise-wide standardization, providing unchallenging access, management, and analysis of data to derive useful insights. Research on data governance frameworks is limited and lacks a key perspective: how can firms ensure sustainability on their programs. Data governance programs can only be continuously valuable if supported by a holistic framework focused on sustainability. To understand this gap, five frameworks are presented, analyzed and evaluated according to an assessment matrix based on eleven critical success factors (CSF) for data governance. As a result of this study, where we offer a more comprehensive assessment tool, both researchers and practitioners can understand the maturity level of each CSF in the reviewed frameworks and identify which areas need further exploration and how to accomplish higher data governance maturity levels.authorsversionpublishe

Preparing Law Students for Information Governance

Information governance is a holistic business approach to managing and using information that recognizes information as an asset as well as a potential source of risk. Law librarians and legal information professionals are well situated to take leadership roles in information governance efforts, including instructing law students in information governance principles and practices. This article traces the development of information governance and its importance to the legal profession, offers a primer on information governance principles and implementation, and discusses how academic law librarians and other legal educators can teach information governance to law students using problem-based learning or similar pedagogical methods

Towards a Data Governance Framework for Third Generation Platforms

The fourth industrial revolution considers data as a business asset and therefore this is placed as a central element of the software architecture (data as a service) that will support the horizontal and vertical digitalization of industrial processes. The large volume of data that the environment generates, its heterogeneity and complexity, as well as its reuse for later processes (e.g. analytics, IA) requires the adoption of policies, directives and standards for its right governance. Furthermore, the issues related to the use of resources in the cloud computing must be taken into account with the aim of meeting the requirements of performance and security of the different processes. This article, in the absence of frameworks adapted to this new architecture, proposes an initial schema for developing an effective data governance programme for third generation platforms, that means, a conceptual tool which guides organizations to define, design, develop and deploy services aligned with its vision and business goals in I4.0 era.This work is partially funded by Spanish Government through the research project TIN2017-86520-C3-3-R