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

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

Analysis and Design of Data Governance at the Financial Services Authority

This research is a case study conducted at the Financial Services Authority on the implementation of a data governance framework based on the model from The Data Management Association in 2017. The purpose of this study is to produce a data governance framework in managing integrated Financial Services Sector data. This study uses a qualitative approach in describing data governance activities. The research instruments were interviews, questionnaires, and content analysis. The results show that data governance frameworks provide guidelines for various parties to act in accordance with the strategies that have been developed. Data governance program at the Financial Services Authority requires further improvements in the form of establishing a data governance charter, assessing the maturity level of data management capabilities, defining the operational framework, adjusting the roadmap, establishing a change management team, creating mechanisms and procedures for handling data problems, and developing tools and techniques which supports the entire data governance program

Gobernanza de datos en los procesos de negocio para las instituciones de educación superior

Las organizaciones requieren fuentes de datos confiables, coherentes y disponibles que soporten la toma de decisiones y el mejoramiento de sus capacidades operativas. Estas se han dado cuenta de la importancia de gestionar y gobernar sus datos como un recurso a nivel estratégico que optimicen sus procesos. "La gobernanza de datos se define como el ejercicio de autoridad y control (planificación, seguimiento y ejecución) sobre la gestión de los activos de datos". Esto implica que el Gobierno de datos cubre los estándares y principios de la gestión de datos. Una norma define requisitos, especificaciones, pautas o características para un determinado material, producto, proceso o servicio. Existen varios modelos de gobernanza para guiar los procesos de estudio, y de implementación de Gobernanza de datos, tales como: IBM, ORACLE, el Data Governance Institute (DGI), ISO/ IEC JTC 1 / SC 4 , Collibra , DAMA DMBOK , así como los modelos emergentes de gobernanza descentralizada basados en tecnología de blockchain distribuida (Distributed data ledger: Libro de registros distribuido). Sin embargo, existe una vacancia importante cuando la gobernanza de datos se refiere a los procesos educativos, en si mismos, dentro de las Universidades. Esta investigación propone el desarrollo de una ontología para el gobierno de datos, que aplicada a los procesos de enseñanza-aprendizaje en las Universidades, guíe en el uso de modelos de gobierno de datos existentes. Los integrantes de este Proyecto tienen formación de posgrado, han dirigido tesis de posgrado y/o han formado parte de grupos y proyectos de Investigación en Educación.Red de Universidades con Carreras en Informátic

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

TOWARDS A CONTINGENCY MODEL FOR GREEN IT GOVERNANCE

Although practitioners have begun to implement Green IT into their companies, the governance of Green IT varies significantly. No research has been done to explain these differences in Green IT governance. Building upon contingency theory and IT governance, we develop a contingency model for Green IT governance which demonstrates the fit between contingencies and the company-specific configuration of Green IT. In the first step, three archetypes of Green IT governance reaching from centralized over federal to decentralized are presented. In the second step, we identify from literature competitive strategy, firm size, organization structure, performance strategy, environmental impact of industry, environmental strategy, IT infusion, and IT diffusion as contingency factors determining the ideal type of Green IT governance. The contingency model for Green IT governance is validated based on insights from five case studies. With the enhanced understanding of how Green IT governance is shaped by contingency factors, organizations are able to select the most successful Green IT governance form

An Exploratory Study On Using Stage Theory To Develop A Data Management Measurement Framework

The issue of interest in this study is two-fold. First the evolution of Nolan’s Stage Model is presented. Next, the study discusses adapting Nolan’s stage theory into a framework that is an adaptable data management measurement tool. An exploratory measurement tool is developed, tested and refined. This research confirms that a valid measure is possible and that different data management maturity stages have certain characteristics that are important to the emerging knowledge necessary to manage enterprise-wide data as a valuable business resource