Relevant bibliographies by topics / Data Governance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Governance'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Data Governance"

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Otto, Boris. "Data Governance." WIRTSCHAFTSINFORMATIK 53, no. 4 (June 8, 2011): 235–38. http://dx.doi.org/10.1007/s11576-011-0275-1.

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Otto, Boris. "Data Governance." Business & Information Systems Engineering 3, no. 4 (June 8, 2011): 241–44. http://dx.doi.org/10.1007/s12599-011-0162-8.

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Hubbard, Dan, Augie Freda, and Andrea Swanagan. "Data Governance 101: IR's Critical Role in Data Governance." New Directions for Institutional Research 2020, no. 185-186 (March 2020): 51–65. http://dx.doi.org/10.1002/ir.20329.

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Klingenberg, Christiana, and Kristin Weber. "Erfolgsfaktor Data Governance." ERP Management 2021, no. 3 (June 20, 2021): 24–26. http://dx.doi.org/10.30844/erp21-3_24-26.

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Von Master Data Management (MDM) versprechen sich Unternehmen Effizienz, Transparenz und Risikominimierung im Umgang mit ihren Stammdaten. MDM soll dazu beitragen, Stammdaten als „Asset“ im Unternehmen zu bewirtschaften. Der vorliegende Beitrag liefert praktische Tipps, wie MDM-Implementierungen nachhaltig gestaltet werden können, damit die Daten einen Beitrag zum Unternehmenserfolg leisten. Er stellt das qualitätsorientierte Data Governance Framework vor. Das Framework stellt sicher, dass bei einer Implementierung alle Aspekte von MDM adressiert werden inkl. strategischer und organisatorischer Fragestellungen. Die konsequente Ausrichtung an der Datenqualität sorgt dafür, dass alle Unternehmensbereiche Stammdaten nutzenstiftend einsetzen können.
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Khatri, Vijay, and Carol V. Brown. "Designing data governance." Communications of the ACM 53, no. 1 (January 2010): 148–52. http://dx.doi.org/10.1145/1629175.1629210.

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Knoll, Matthias. "Rezension „Data Governance“." HMD Praxis der Wirtschaftsinformatik 57, no. 6 (October 1, 2020): 1302–5. http://dx.doi.org/10.1365/s40702-020-00660-5.

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Pathak, M. "Data Governance Redefined:." European Data Protection Law Review 10, no. 1 (2024): 43–56. http://dx.doi.org/10.21552/edpl/2024/1/8.

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Jang, Kyoung-Ae, and Woo-Je Kim. "Component Development and Importance Weight Analysis of Data Governance." Journal of the Korean Operations Research and Management Science Society 41, no. 3 (August 31, 2016): 45–58. http://dx.doi.org/10.7737/jkorms.2016.41.3.045.

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Liu, Jun. "Social data governance: Towards a definition and model." Big Data & Society 9, no. 2 (July 2022): 205395172211113. http://dx.doi.org/10.1177/20539517221111352.

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With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and dynamics regarding data and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term “social data governance” to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance.
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Al-Ruithe, Majid, Elhadj Benkhelifa, and Khawar Hameed. "A systematic literature review of data governance and cloud data governance." Personal and Ubiquitous Computing 23, no. 5-6 (January 4, 2018): 839–59. http://dx.doi.org/10.1007/s00779-017-1104-3.

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Dissertations / Theses on the topic "Data Governance"

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Blahová, Leontýna. "Big Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203994.

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This master thesis is about Big Data Governance and about software, which is used for this purposes. Because Big Data are huge opportunity and also risk, I wanted to map products which can be easily use for Data Quality and Big Data Governance in one platform. This thesis is not only on theoretical knowledge level, but also evaluates five key products (from my point of view). I defined requirements for every kind of domain and then I set up the weights and points. The main objective is to evaluate software capabilities and compere them.
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Slouková, Anna. "Postup zavádění Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-10498.

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This thesis refers to Data Governance issue and the way of implementing this program. It is logically devided into two parts -- theoretical and practical one. The teoretical part represented by first chapter summarises actual findings about the Data Governance program, it explains what is hidden behind the term Data Governance, cause for Data Governance initiatives emergence, it itemizes particular parts of which the program is composed and basic, mostly software tools, that are necessary for successful program run. Practical part consists of second and third chapter. The second chapter contains enumeration of various types of outputs that grow up either during the implementation of program or in its run itself. It categorizes and deals in detail with processes and activities, organizational structure of the program, dokuments, used metrics and KPIs and IS/IT tools. Third chapter describes the process of implementing the program into an enterprise in detail. It is devided into four consequential phases -- assessment of current state, design, implementation and run of the program. In every chapter, there are inputs, outputs, detailed decomposition into particular activities with references to document tepmlates that are used during theese activities, risks and resources introduced. In two attachments of this thesis, there are two helpful documents -- general document teplate and teplate of a role description -- that serve to better implementation of Data Governance program.
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Reken, Jaroslav. "Role v Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-19114.

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This work is covering the area of Data Governance (DG) with the main focus on roles in DG. First part is capturing the DG field from a basic perspective. This chapter introduces main principles of DG and is considered as a guideline for better understanding of the second chapter. The second chapter contains different approaches on DG and on roles in DG. The approaches are from world leaders in the field of DG like IBM, Teradata, KIK Consulting and The Data Governance Institute. In the summary of second chapter you can find a comparison of these different approaches to organization structure and roles in DG. The third and final chapter contains my own approach to organization structures and roles in DG. You can find there a wide variety of roles divided by different factors. This will give you a very good and unique perspective on roles and it might be also helpful as a guideline for necessary roles in the implementation process of DG program.
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Zosinčuk, Dominik. "Zavádění projektu data governance." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-197446.

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Topic of this thesis is the Data Governance implementation in the large companies. These companies struggle during governing and managing data to get useful insights for the decision making. Data Governance is new approach to managing the companies which helps to solve the data management pain points and helps organizations to work with data effectively and without any problems. Data Governance helps to transform data into asset. This thesis is divided into theoretical and practical part. In the theoretical part are discussed reasons for emerging Data Governance, analysis of approaches to Data Governance by world leading methodologies and possible focus of the Data Governance projects as well as its benefits. Important part of this theoretical part is Data Governance components definition. Implementation of the Data Governance is discussed in the practical part. The goal of the practical part is to describe required artifacts which should exist during the implementation. Described artifacts use the best practice from the existing literature. These deliverables will help to better structure, govern and successfully implement the Data Governance. Delivering these artifacts bring the value for the company. Each project deliverable has definitions of the importance for the project team and the company. Most important benefit of the practical part is aspiration to eliminate pain points during the Data Governance implementation as appropriate project team, cooperation definition, buy-in and deliverables.
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Ullrichová, Jana. "Koncept zavedení Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203901.

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This master´s thesis discusses concept of implementation for data governance. The theoretical part of this thesis is about data governance. It explains why data are important for company, describes definitoons of data governance, its history, its components, its principles and processes and fitting in company. Theoretical part is amended with examples of data governance failures and banking specifics. The main goal of this thesis is to create a concept for implementing data governance and its implementation in real company. That is what practical part consists of.
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DeStefano, R. J. "Improving Enterprise Data Governance Through Ontology and Linked Data." Thesis, Pace University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10097925.

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In the past decade, the role of data has increased exponentially from being the output of a process, to becoming a true corporate asset. As the business landscape becomes increasingly complex and the pace of change increasingly faster, companies need a clear awareness of their data assets, their movement, and how they relate to the organization in order to make informed decisions, reduce cost, and identify opportunity. The increased complexity of corporate technology has also created a high level of risk, as the data moving across a multitude of systems lends itself to a higher likelihood of impacting dependent processes and systems, should something go wrong or be changed. The result of this increased difficulty in managing corporate data assets is poor enterprise data quality, the impacts of which, range in the billions of dollars of waste and lost opportunity to businesses.

Tools and processes exist to help companies manage this phenomena, however often times, data projects are subject to high amounts of scrutiny as senior leadership struggles to identify return on investment. While there are many tools and methods to increase a companies’ ability to govern data, this research stands by the fact that you can’t govern that which you don’t know. This lack of awareness of the corporate data landscape impacts the ability to govern data, which in turn impacts overall data quality within organizations.

This research seeks to propose a means for companies to better model the landscape of their data, processes, and organizational attributes through the use of linked data, via the Resource Description Framework (RDF) and ontology. The outcome of adopting such techniques is an increased level of data awareness within the organization, resulting in improved ability to govern corporate data assets. It does this by primarily addressing corporate leadership’s low tolerance for taking on large scale data centric projects. The nature of linked data, with it’s incremental and de-centralized approach to storing information, combined with a rich ecosystem of open source or low cost tools reduces the financial barriers to entry regarding these initiatives. Additionally, linked data’s distributed nature and flexible structure help foster maximum participation throughout the enterprise to assist in capturing information regarding data assets. This increased participation aids in increasing the quality of the information captured by empowering more of the individuals who handle the data to contribute.

Ontology, in conjunction with linked data, provides an incredibly powerful means to model the complex relationships between an organization, its people, processes, and technology assets. When combined with the graph based nature of RDF the model lends itself to presenting concepts such as data lineage to allow an organization to see the true reach of it’s data. This research further proposes an ontology that is based on data governance standards, visualization examples and queries against data to simulate common data governance situations, as well as guidelines to assist in its implementation in a enterprise setting.

The result of adopting such techniques will allow for an enterprise to accurately reflect the data assets, stewardship information and integration points that are so necessary to institute effective data governance.

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Barker, James M. "Data governance| The missing approach to improving data quality." Thesis, University of Phoenix, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10248424.

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In an environment where individuals use applications to drive activities from what book to purchase, what film to view, to what temperature to heat a home, data is the critical element. To make things work data must be correct, complete, and accurate. Many firms view data governance as a panacea to the ills of systems and organizational challenge while other firms struggle to generate the value of these programs. This paper documents a study that was executed to understand what is being done by firms in the data governance space and why? The conceptual framework that was established from the literature on the subject was a set of six areas that should be addressed for a data governance program including: data governance councils; data quality; master data management; data security; policies and procedures; and data architecture. There is a wide range of experiences and ways to address data quality and the focus needs to be on execution. This explanatory case study examined the experiences of 100 professionals at 41 firms to understand what is being done and why professionals are undertaking such an endeavor. The outcome is that firms need to address data quality, data security, and operational standards in a manner that is organized around business value including strong business leader sponsorship and a documented dynamic business case. The outcome of this study provides a foundation for data governance program success and a guide to getting started.

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Furlan, Patrícia Kuzmenko. "Fatores determinantes para a adoção das governanças de dados e de informação no ambiente big data." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-24092018-081250/.

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No ambiente big data, as organizações se preocupam em extrair valor dos dados e das informações com o intuito de obter vantagens competitivas. No entanto, são necessários esforços organizacionais com relação aos ativos de dados, incluindo a definição de responsabilidades com relação ao uso dos dados, a garantia da qualidade dos dados, dentre outros aspectos contemplados pelos modelos de governança de dados ou de informação. Deste modo, esta pesquisa investigou como as organizações podem adotar as governanças de dados ou de informação no ambiente big data e, para tanto, foram contemplados estudos de casos multisetoriais para identificar os fatores determinantes para a adoção das governanças de dados ou de informação no ambiente big data. Foram investigados os elementos e os conteúdos dos modelos de governança de dados ou de informação e analisados os aspectos dos modelos com relação à inteligência de negócios e ao big data analytics. Notou-se que as ações organizacionais com relação à governança de dados ou de informação são pouco consolidadas, mas conhecidas pelas organizações. Além disto, os modelos de governança de dados ou de informação são adotados por organizações com diferentes níveis de capacidades analíticas. Tais modelos contemplam a definição dos objetivos estratégicos da governança e domínios como o gerenciamento da qualidade dos dados ou das informações, o gerenciamento dos dados (em especial meta-dados), a transformação da mentalidade organizacional com relação aos dados e as informações e necessitam de competências de colaboração e comunicação dos stakeholders. Foram identificados oito fatores determinantes para a adoção das governanças de dados ou de informação no ambiente big data, os quais contemplam práticas estruturais, relacionais e operacionais do modelo de governança: 1 - Organizações grandes, globais, difusas, com estruturas descentralizadas de negócios e portfolio complexo de produtos ou serviços; 2 - Apontar um C-level, definir gerentes na estrutura e determinar data owners e data stewards; 3 - Estabelecer comitê de dados ou outros meios para reunir a alta cúpula e os principais líderes da organização; 4 - Atuação do departamento de TI nas atividades de gerenciamento de dados ou de informação, viabilizando e executando atividades operacionais com relação aos dados e as informações dentre as bases de dados e sistemas de informação; 5 - Atuar ativamente na transformação cultural da organização para data-driven; 6 - Promover a comunicação e a colaboração interna; desenvolver a comunicação com relação à eficácia das políticas e a necessidade de adequação dos stakeholders; 7 - Definir, gerenciar e controlar metadados; 8 - Definir os padrões, as exigências e o controle sobre a qualidade dos dados. A pesquisa oferece uma consolidação teórica relevante para o campo da governança de dados ou da informação, contemplando vasta lista de variáveis da literatura de de dados e governança de informação. Foi também possível expandir o modelo de governança de dados ou de informação englobando os domínios relativos à colaboração e comunicação, mudança cultural. Propõem-se uma expansão na conceituação geral dos termos governança de dados e governança de informação.
In the big data environment, organizations are concerned with extracting value from data and information in order to acquire competitive advantage. However, organizational efforts are required to organize data assets, determine responsibilities with regard to the data assets, ensure data quality, and other aspects. Such activities are covered by data or information governance models. This research investigated how organizations can adopt data or information governance in the big data environment. Thus, it was conducted multi-sectoral case studies to identify determinants factors for the adopting of data or information governance in the big data environment. The research protocol encompassed elements and contents of the data or information governance models and those related to big data value extraction. It was noted that the organizational approaches regarding data or information governance are poorly consolidated, but are well known to organizations. In addition, data or information governance models are adopted by organizations with different levels of analytical capabilities. Those models include the definition of the strategic objectives, and domains like data or information quality management, data management (especially metadata), transformation of the organizational cultural in relation to the data and the information, and collaboration and communication among stakeholders. Eight determinants factor were identified for the adoption of data or information governance in the big data environment, including structural, relational and operational practices of the governance model: 1 - Large, global and diffuse organizations with decentralized business and complex portfolio of products or services; 2 - Define C-level, managers, data owners and data stewards; 3 - Establish a data committee or other means to bring together the top leaders of the organization; 4 - Engagement of the IT department on the data management activities, enabling and executing operational activities in relation to data and information among databases and information systems; 5 - Actively engage in the cultural transformation of the organization into data-driven; 6 - Promote communication and internal collaboration; develop communication on the effectiveness of policies and the need for stakeholder adequacy; 7 - Define, manage and control metadata; 8 - Define standards, requirements and control over data quality. This research provides a relevant theoretical consolidation to the field of data or information governance, contemplating a vast list of research variables on the fields of competitive intelligence, IT governance, data and information governance literatures. It was also possible to expand the data or information governance model through the addition of domains such as collaboration, communication, and cultural transformation. The research also proposes an expansion in the general conceptualization of the terms data governance and information governance.
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Kmoch, Václav. "Data Governance - koncept projektu zavedení procesu." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-73648.

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Companies in these days deal with underlying issue that concerns about questions how to manage volume growth of corporate data needed to decision making processes and how to control credibility and relevance of derived information and knowledge. Other questions deal with problem of responsibility and data security that represents potential risk of information outflow. The Data Governance concepts provide comprehensive answer to these questions. However, making a decision on implementing a Data Governance program is usually triggering many other problems like setting up environments, making determination of project scope, allocating capacity of data experts and finding one's way in non-uniform Data Governance concepts offered by various IT vendors. The aim of this thesis is to draw the unified and universal implementation process that helps with setting up DG projects and makes certain conception about how to run these projects step-by-step. The first and the second part of the thesis are dedicated to describe principles, components and tools of Data Governance and also methods of measuring data quality levels. The third part is offering concrete approach for successful implementation of Data Governance conception into corporate data environment.
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Alfaro, Carranza Rosa Ángela, and Mendoza Libusi Deyanira Ampuero. "Modelo de madurez de Data Governance." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2015. http://hdl.handle.net/10757/347094.

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Data Governance is a concept in evolution which includes people who have large responsibilities within organizations and the processes that these used to be able to manage information. This project proposes the creation of a maturity model of data governance based on the IBM Data Governance Maturity Model. The objective of this model is to help organizations to understand their level of maturity in relation to the management of your data and identify its weaknesses to subsequently take corrective action before opting for the implementation of a Data Governance program.
Data Governance o gobierno de datos es un concepto en evolución que incluye las personas que tienen grandes responsabilidades dentro de organizaciones y los procesos que estas utilizan para poder gestionar la información. El presente proyecto plantea la creación de un Modelo de Madurez de Data Governance basado en el IBM Data Governance Maturity Model. El objetivo de este modelo es ayudar a las organizaciones a conocer su nivel de madurez en relación con la gestión de sus datos e identificar sus puntos débiles para posteriormente tomar medidas correctivas antes de optar por la implementación de un programa de Data Governance.
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Books on the topic "Data Governance"

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Caballero, Ismael, and Mario Piattini, eds. Data Governance. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43773-1.

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Weber, Beatrix, ed. Data Governance. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67556-4.

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Mahanti, Rupa. Data Governance and Data Management. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3583-0.

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Mahanti, Rupa. Data Governance Success. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5086-4.

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Bonnet, Pierre. Enterprise Data Governance. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118622513.

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Lawrence, Dubov, ed. Master data management and data governance. 2nd ed. New York: McGraw-Hill, 2011.

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Mahanti, Rupa. Data Governance and Compliance. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6877-4.

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Bollweg, Lars Michael. Data Governance für Manager. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63562-9.

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West, Tobi, and Aeron Zentner. Data Privacy and Governance. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071859414.

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Bollweg, Lars Michael. Data Governance for Managers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65171-1.

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Book chapters on the topic "Data Governance"

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Weber, Kristin, and Christiana Klingenberg. "Data Governance." In Data Governance, 23–34. München: Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9783446466746.003.

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Frick, Detlev. "Data Governance." In Data Science, 105–19. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33403-1_6.

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Gronwald, Klaus-Dieter. "Data Governance." In Data Management, 103–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68668-3_11.

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Otto, Boris, and Kristin Weber. "Data Governance." In Daten- und Informationsqualität, 277–95. Wiesbaden: Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-9953-8_16.

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Treder, Martin. "Data Governance." In The Chief Data Officer Management Handbook, 79–91. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6115-6_6.

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Mucchetti, Mark. "Data Governance." In BigQuery for Data Warehousing, 305–32. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6186-6_14.

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Otto, Boris, and Kristin Weber. "Data Governance." In Daten- und Informationsqualität, 269–86. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-21994-9_16.

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Weber, Kristin, Boris Otto, and Dominik Lis. "Data Governance." In Daten- und Informationsqualität, 271–91. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30991-6_16.

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Viscusi, Gianluigi, Carlo Batini, and Massimo Mecella. "Data Governance." In Information Systems for eGovernment, 21–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13571-2_2.

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Fleckenstein, Mike, and Lorraine Fellows. "Data Governance." In Modern Data Strategy, 63–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68993-7_8.

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Conference papers on the topic "Data Governance"

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Marawar, Tejas R., Swapnil P. Kale, and Ketan I. Araspure. "E Governance." In 2010 International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2010. http://dx.doi.org/10.1109/dsde.2010.54.

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Rifaie, Mohammad, Reda Alhajj, and Mick Ridley. "Data governance strategy." In the 11th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1806338.1806449.

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Duzha, Armend, Emmanouil Alexakis, Dimosthenis Kyriazis, Louis Fortune Sahi, and Mohamed Ali Kandi. "From Data Governance by design to Data Governance as a Service: A transformative human-centric data governance framework." In ICCBDC 2023: 2023 7th International Conference on Cloud and Big Data Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3616131.3616145.

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Korhonen, Janne, Ilkka Melleri, Kari Hiekkanen, and Mika Helenius. "Data Governance: A Systemic Approach Organizational Design Perspective to Data Governance." In 3rd Annual International Conference on Infocomm Technologies in Competitive Strategies. Global Science Technology Forum, 2012. http://dx.doi.org/10.5176/2251-2136_ict12.13.

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Singi, Kapil, Swapnajeet Gon Choudhury, Vikrant Kaulgud, R. P. Jagadeesh Chandra Bose, Sanjay Podder, and Adam P. Burden. "Data Sovereignty Governance Framework." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387940.3392212.

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Wrobel, Andrzej, Konrad Komnata, and Krzysztof Rudek. "IBM data governance solutions." In 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC). IEEE, 2017. http://dx.doi.org/10.1109/besc.2017.8256387.

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Londońo Peláez, Jorge Mario, María Alejandra Echavarria Arcila, Leonardo Betancur Agudelo, Diana Patricia Giraldo Ramirez, and Laura Orozco Salazar. "Shared-Data Governance Frameworks." In 15th International Conference on Society and Information Technologies. Winter Garden, Florida, United States: International Institute of Informatics and Cybernetics, 2024. http://dx.doi.org/10.54808/icsit2024.01.65.

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Zait, Nurshazareena Shuhada, Muhammad Azmir Mohamed Ghazali, Hin Wong Lee, Normanisah Mat Ghani, Nur Aliah Nur Ismail, Zulhanizam Zakaria, Mohamad Haneef Mohamad Isa, and Dzulkarnain Azaman. "Fortifying Upstream Data Governance - Establishing Centralised Data Governance and Assurance Control Tower." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215302-ms.

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Abstract PETRONAS Upstream is moving towards becoming a data driven organization. In order to ensure Upstream decision making is driven by trusted data, it is imperative to fortify Upstream data governance with structured and guided process and procedure, technique and methodology that define, build and operate data management knowledge areas as per DAMA DMBOK. An innovative concept of establishing Data Control Tower as centralized data governance and assurance was designed and implemented to ensure that PETRONAS Upstream Data Governance is managed at the highest level with the following objectives: Guardrail Upstream data standardization and consistency Oversight of data management activities Centralized data mastering Data quality performance monitoring Data catalog, business glossary and metadata management Data security control
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Feng, Yunzhong, and Xiaohua Feng. "Smart Data Analysis and Data Governance." In CSAE 2021: The 5th International Conference on Computer Science and Application Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487075.3487112.

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Purohit, Pankaj, Fatema Al Nuaimi, and Safeer Nakkolakkal. "Data Governance, Privacy, Data Sharing Challenges." In GOTECH. SPE, 2024. http://dx.doi.org/10.2118/219172-ms.

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Executive Summary Oil and Gas Company spends millions of $ on logging operations activity every year. Managing and tracking of well logs data from field acquisition through processing to interpretation has been a challenge, well log data delivery workflow was manual and not streamlined leading to disruption of the data flow and several data issues such as Timely availability, Quality, Completeness & Loss of data. These issues of Data Governance, Privacy, Data Sharing Challenges cause Geoscientists and Engineers to spend their valuable time on low level tasks such as searching, correcting, re-acquiring the data rather than focusing on their core tasks of analysis and interpretation. Data Management initiated an in house developed automated, integrated, and comprehensive workflow in the form of Corporate Well Log Platform See Figure 1 and 2, to overcome these Data Governance challenges, save $ investment on logging operation activities which provided the right data timely to the business needs. Streamline the processes of tracking, quality checking, archiving, publishing, and managing the well logs is vital to any Oil company. A controlled environment had provided Oil and Gas Company with the opportunity to enrich the data store & conduct major field reviews, carry out faster appraisal and future field development to meet company production target for economic growth of the country. This in-house developed Corporate Well Log has become an important tool as a source of providing valuable information related to sub-surface geology and hydrocarbon resources. The purpose of this study was to investigate the management and tracking of well logs data from the field acquisition through processing to interpretation along with the business and data sharing challenges, process work-flow standardization. The study findings suggest that automated, integrated, and comprehensive web-based workflow solution helps to overcome the challenges, saving approximately $7.5m. by securing $ investment and increasing the data completeness. Moreover, with the timely availability of quality data, completeness & Loss of data over the time cause Geoscientists and Engineers to spend their valuable time on searching, correcting, re-acquiring the data rather than focusing on their core tasks of analysis and interpretation. Data governance through automated processes ensures that important data assets are managed efficiently throughout the enterprise its life cycle while ensuring proper time management for the company. The main results of this project are as follows: In-house e-well logs Solution development & IntegrationEstablishing Well Log Data Governanceworkflow mapping along the well log business processautomated Emails, Reminders, Dashboard, ReportsEnrich corporate well log data repository. The novelty of current research specifies the significance for the implantation of well-logs data process automation for the first time in history of the Oil and Gas Company Group. By establishing such process of "Well Log Data Governance", Oil and Gas companies can enhance core business process of monitoring and preserve E&P data for future generation & field development. However, for achieving such operational excellence, there is a strong need for the automation of business processes and workflows.
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Reports on the topic "Data Governance"

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Yates, Deborah, Elea Himmelsbach, Flor Serale, James Maddison, Jennifer Pougnet, Mahad Alassow, Mark Boyd, and Sonia Duarte. Health data governance playbook. Open Data Institute, February 2022. http://dx.doi.org/10.61557/ejuv7241.

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Sabharwal, Yashvinder, Bruce Kane, David R Hinkler, Derrick Tapscott, Josh Lobel, Julain Goy, Sana Ahmed, Sean Coombs, and Vicki Pearson. Data governance needs in biomanufacturing position paper. BioPhorum, August 2023. http://dx.doi.org/10.46220/2023tr001.

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Smith, M. F. M., and K. L. Davies. Science data and information governance framework 2020-2023. Geoscience Australia, 2020. http://dx.doi.org/10.11636/record.2020.005.

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Williams, Emelia Williams. Designing Governance Tools for Agricultural and Environmental Data. Open Environmental Data Project (OEDP), September 2023. http://dx.doi.org/10.15868/socialsector.42511.

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Wong, Janis, and Dr Mahlet (Milly) Zimeta. ODI Fellow Report: Data governance for online learning. Open Data Institute, September 2021. http://dx.doi.org/10.61557/prjc8161.

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Nasution, Sri. Improving Data Governance and Personal Data Protection through ASEAN Digital Masterplan 2025. Jakarta, Indonesia: Center for Indonesian Policy Studies, 2021. http://dx.doi.org/10.35497/353777.

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Mattmann, Chris. Earth Science Data Systems: Policy for Open Source Software Governance. Washington, D.C.: National Academies Press, December 2018. http://dx.doi.org/10.17226/25217_2.

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Harris, Ruth, Tracy Jones, Macario Flores, and David Bustamante. COVID-19, An Exercise in Data Governance at Sandia National Laboratories. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1808095.

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Williams, Emelia Williams. Legal mechanisms and environmental data governance: Questions to start the conversation. Open Environmental Data Project (OEDP), January 2024. http://dx.doi.org/10.15868/socialsector.43149.

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Gray, Douglas. Improving Cybersecurity Governance Through Data-Driven Decision-Making and Execution (Briefing Charts). Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610301.

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