Relevant bibliographies by topics / Automation in Data Governance

Contents

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

Academic literature on the topic 'Automation in Data Governance'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Automation in Data Governance"

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Researcher. "BUILDING A SCALABLE AUTOMATION FRAMEWORK FOR YOUR ORGANIZATION." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 278–87. https://doi.org/10.5281/zenodo.13777356.

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This comprehensive article explores the critical importance of building scalable automation frameworks in today's rapidly evolving business landscape. It examines the growing automation testing market, projected to reach $20.7 billion by 2021, and discusses the key drivers behind this surge in adoption. The article delves into the necessity for scalability in automation initiatives, outlining a five-step strategy for designing scalable automation frameworks. It also analyzes various tools and platforms for managing large-scale automations, including RPA, intelligent automation, integration pla
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Shah, Samarth, and Shubham Jain. "Data Governance in Lakehouse." Stallion Journal for Multidisciplinary Associated Research Studies 3, no. 5 (2024): 126–45. https://doi.org/10.55544/sjmars.3.5.12.

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Data governance in a lakehouse architecture is crucial for managing the lifecycle, quality, and security of data across hybrid and scalable environments. The lakehouse model combines the benefits of data lakes and data warehouses, enabling organizations to handle large volumes of structured and unstructured data while ensuring analytical accuracy. However, the seamless integration of diverse data sources introduces challenges related to governance, including compliance, privacy, and data lineage. This paper explores the principles and practices essential for implementing effective data governa
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Govindarajan, Gopinath. "Data Quality and Automation in Modern Data Ecosystems." International Journal of Advances in Engineering and Management 7, no. 4 (2025): 228–39. https://doi.org/10.35629/5252-0704228239.

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This article presents a comprehensive exploration of data quality management through automation, examining the evolution from manual processes to sophisticated, technology-driven approaches. Drawing on empirical research across multiple organizations, the article investigates how automated data quality frameworks and machine learning-based anomaly detection can address the complexities of ensuring data accuracy, completeness, consistency, and other critical dimensions in modern data ecosystems. The article introduces a structured implementation framework for organizations seeking to enhance th
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Siramgari, Dayakar Reddy, and Vijay Kartik Sikha. "From Raw Data to Actionable Insights: Leveraging LLMs for Automation." International Journal on Recent and Innovation Trends in Computing and Communication 12, no. 2 (2024): 1018–29. https://doi.org/10.5281/zenodo.14128827.

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This paper explores the transformative role of Large Language Models (LLMs) in automating the data processing lifecycle, from ingestion to insights generation. LLMs streamline data handling by automating ingestion, transformation, and modeling processes, offering efficient, reliable, and timely insights critical for sectors such as healthcare, finance, and telecommunications. This study details the technical architecture of LLM-driven data workflows, addresses challenges in integrating diverse data sources, and emphasizes the necessity of governance frameworks to mitigate ethica
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Deepak Chanda. "Optimizing AI and Robotics-driven Automation Systems: The Synergy of Data Engineering and Data Science in Scalable Intelligent Automation." Journal of Electrical Systems 21, no. 1s (2025): 126–31. https://doi.org/10.52783/jes.8360.

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The intersection of data engineering and artificial intelligence (AI) has revolutionized modern industries using scalable, efficient, and intelligent automation. AI applications rely on robust data engineering frameworks for data ingestion, processing, and storage to feed high-quality inputs to machine learning algorithms. This paper explores the symbiosis between AI and data engineering in terms of automation, robotics, scalability, and real-time analytics. Data integration, governance, and performance optimization issues are considered, along with AI-driven solutions that streamline data wor
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Salsabilla, Ainan, Santi Andriyani, Andra Andriawan, and Meli Indah Sugiarti. "NEEDS ANALYSIS OF ENGLISH MATERIAL FOR 10TH GRADE STUDENTS IN OFFICE AUTOMATION AND GOVERNANCE DEPARTMENT." E-LINK JOURNAL 9, no. 1 (2022): 1. http://dx.doi.org/10.30736/ej.v9i1.607.

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Vocational High School (VHS) is formal education that organizes vocational education at the secondary education level. Learning English at the VHS level is categorized as English for Special Purposes (ESP) which means that English material is expected to meet the students' needs according to their majors. However, many students still have low English proficiency. This study aims to determine the English needs of tenth-grade students majoring in Office Automation and Governance. This study used a descriptive qualitative method. The data were obtained using English tests, questionnaires, and int
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Sandhyarani Ganipaneni, Ravi Kiran Pagidi, Aravind Ayyagiri, Prof.(Dr) Punit Goel, Prof.(Dr.) Arpit Jain, and Dr Satendra Pal Singh. "Machine Learning for SAP Data Processing and Workflow Automation." Darpan International Research Analysis 12, no. 3 (2024): 744–75. http://dx.doi.org/10.36676/dira.v12.i3.131.

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In the rapidly evolving landscape of enterprise resource planning, the integration of Machine Learning (ML) into SAP data processing and workflow automation presents significant opportunities for enhancing operational efficiency and decision-making. This paper explores the methodologies and applications of ML algorithms in optimizing SAP environments, focusing on data processing, predictive analytics, and automation workflows. Firstly, we examine the role of ML in automating data extraction, transformation, and loading processes, which traditionally require substantial manual intervention. By
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Mokale, Mahesh. "Developing Advanced Tooling for Data Governance in Media and Telecommunications." Journal of Software Engineering and Simulation 8, no. 12 (2012): 32–38. https://doi.org/10.35629/3795-08123238.

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Data governance is a fundamental aspect of modern media and telecommunications industries, ensuring compliance with stringent regulatory frameworks, enhancing data security, and optimizing data utilization for business insights. With the unprecedented surge in data volume, diversity, and complexity, traditional governance approaches are no longer sufficient to manage the evolving landscape of digital operations. Organizations must adopt advanced tooling that integrates artificial intelligence (AI), machine learning (ML), automation, and cloud-based technologies to establish scalable, efficient
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Shivpuja, Amit. "The Transformative Role of AI and Generative AI in Modern Data and AI Governance." European Journal of Computer Science and Information Technology 13, no. 33 (2025): 117–24. https://doi.org/10.37745/ejcsit.2013/vol13n33117124.

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This article examines the transformative role of Artificial Intelligence (AI) and Generative AI in modernizing data and AI governance frameworks within organizations. As enterprises face mounting challenges in managing expanding data ecosystems, these technologies offer innovative solutions for enhancing governance efficiency and effectiveness. The article explores four key areas: current governance challenges, natural language interfaces, AI-powered automation, and business-centric decision support systems. Through a comprehensive analysis of recent research, this article demonstrates how AI-
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Agarwal, Anant. "AI-POWERED DATA MANAGEMENT AND GOVERNANCE IN RETAIL." International Journal of Data Mining & Knowledge Management Process 15, no. 2 (2025): 89–101. https://doi.org/10.5121/ijdkp.2025.15207.

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Artificial intelligence (AI) is transforming the retail industry’s approach to data management and decisionmaking. This journal explores how AI-powered techniques enhance data governance in retail, ensuring data quality, security, and compliance in an era of big data and real-time analytics. We review the current landscape of AI adoption in retail, underscoring the need for robust data governance frameworks to handle the influx of data and support AI initiatives. Drawing on literature and industry examples, we examine established data governance frameworks and how AI technologies (such as mach
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Dissertations / Theses on the topic "Automation in 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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Veen, Michiel van der. "Automation in shallow seismic data acquisition /." [S.l.] : [s.n.], 2000. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=13413.

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Yang, Jinsong. "Data Distribution Service for Industrial Automation." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-15483.

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In industrial automation systems, there is usually large volume of data which needs to be delivered to right places at the right time. In addition, large number of nodes in the automation systems are usually distributed which increases the complexity that there needs to be more point-to-point Ethernet-connections in the network. Hence, it is necessary to apply data-centric design and reduce the connection complexity. Data Distributed Service for Real-Time Systems (DDS) is a data-centric middleware specification adopted by Object Management Group (OMG). It uses the Real-Time Publish-Subscribe p
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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 contai
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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 an
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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
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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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Stutz, Peter. "Office automation." Master's thesis, University of Cape Town, 1989. http://hdl.handle.net/11427/17222.

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Bibliography: p. 100-104.<br>Office automation systems have become an essential tool for the operation of the modern office. With the emphasis of a modern office being placed on efficiency and ease of communication, office automation systems have become the backbone of successful businesses. COSNET is a prototype office automation system designed and implemented at the Department of the University of Cape Town and runs on Personal Computers that are linked to a NCR UNIX TOWER, which acts as the host. This dissertation investigates the different facilities supported by some of the office automa
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林旭明 and Yuk-ming Lam. "Automation in soil testing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209774.

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Seashore, Jonathan. "The automation of obtaining customer billing data." [Denver, Colo.] : Regis University, 2006. http://165.236.235.140/lib/JSeashore2006.pdf.

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Books on the topic "Automation in Data Governance"

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P, Baker George. Contractibility and asset ownership: On-board computers and governance in U.S. trucking. National Bureau of Economic Research, 2000.

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

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

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Sargiotis, Dimitrios. Data Governance. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67268-2.

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

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

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Bonnet, Pierre. Enterprise Data Governance. 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. McGraw-Hill, 2011.

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Antony, Richard T. Principles of data fusion automation. Artech House, 1995.

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

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Book chapters on the topic "Automation in Data Governance"

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Albrecht, Eduardo. "Digitized Biographies." In Political Automation. Oxford University Press, 2025. https://doi.org/10.1093/9780197696989.003.0009.

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Abstract This chapter explores the harvesting of data associated to our social interactions. The chapter starts by scrutinizing historical initiatives such as the Third Department in Imperial Russia, the Dang’an of Maoist China, and early attempts by the US government to monitor citizens following the communist Red Scares in the nation. Next, it looks at how the automation of data collection techniques allowed for building on these traditions and led to the development of numerous programs today, notably the NSA’s ThinThread, the Chinese Social Credit System, and India’s Aadhaar. The chapter c
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Singleton, Alex D., and Seth E. Spielman. "Urban Governance." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_15.

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AbstractIn this chapter, we discuss how the availability of new urban data has the potential to transform the governance of cities. Such effects are realized in several ways: by increasing transparency; creating greater scope to appropriately set and measure municipal policy outcomes; and by design of well-planned and managed digital infrastructure, better empower citizens to hold decision-makers to account. However, such potential is not without risks, and without critical reflection, the proliferation of new data and their integration into software delivering algorithmic insight or automatio
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Albrecht, Eduardo. "Africa." In Political Automation. Oxford University Press, 2025. https://doi.org/10.1093/9780197696989.003.0008.

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Abstract This chapter includes interviews with Patricia Boshe, co-founder and co-director of the African Law and Technology Institute, a research institute that studies the nexus between law, technology, and society in Africa, and Kwami Ahiabenu II, director of Penplusbytes, a nonprofit promoting innovation and good governance across Africa through the effective use of information communication technologies. In Boshe’s interview, she discusses the ease with which governments in Africa can use surveillance and data collection technologies without transparency, and describes governments’ use of
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Lazarotto, Barbara. "The Role of Technology in Citizens’ Right to Good Administration: Examining the Impact of Smart Governments." In Public Governance and Emerging Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84748-6_3.

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Abstract This chapter examines the implementation and impact of “smart governments” within the European Union, focusing on the integration of digital technology in public administration. It investigates the operational strategies adopted by these digital governments and their implications for the fundamental right to good administration. The study explores the increasing reliance on technology, driven by neoliberal policies, leading to the automation and digitalization of public services. While these advancements promise enhanced efficiency, transparency, and accessibility, they also pose sign
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Gao, Yifu, Jiuguang Song, Jia Gao, and Juan Wang. "The Research of Spam-Mail Governance Based on the Big Data Analysis of Enterprise E-mail System." In Advances in Intelligent Automation and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_137.

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Tocco, Fabrice, and Laurent Lafaye. "Data Platform Solutions." In Designing Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93975-5_23.

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AbstractPrivate and public organizations hoard troves of data yet remain unable to unlock its full business potential. Data exchange platforms powered by adapted technology and driven by data exchange strategies act as catalysts to develop data ecosystems and data spaces, accelerate data circulation, and liberate its value.Data spaces are powerful business, innovation, and societal enablers, whose growth and success rely on their ability to foster and develop trust.Data exchange platforms contribute a lot to building trust as they provide the required tools and automation to data acquirers, da
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Brennan, Rob, Simon Quigley, Pieter De Leenheer, and Alfredo Maldonado. "Automatic Extraction of Data Governance Knowledge from Slack Chat Channels." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02671-4_34.

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Schillaci, Zachary. "LLM Adoption Trends and Associated Risks." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_13.

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AbstractThe emergence of Large Language Models (LLMs) is expected to impact the job market significantly, accelerating automation trends and posing a risk to traditionally creative-oriented jobs. LLMs can automate tasks in various fields, including design, journalism, and creative writing. Companies and public institutions can leverage generative models to enhance productivity and reduce workforce requirements through machine-assisted workflows and natural language interactions. While technical skills like programming may become less important in certain roles, generative models are unlikely t
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Sindoni, Giuseppe, and Matteo Massenzio. "An Open Data platform for decision making in local public administration." In Proceedings e report. Firenze University Press and Genova University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0106-3.51.

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This paper presents the Milan Open Data (OD) platform as a means to provide statistics and data in the framework of “Data-Driven Milan”, a city where policy decisions are taken in an “informed and aware” way using data. Open Data are ever more important in providing citizen communities with useful information. Over the last 10 years, the municipality of Milan has developed its OD platform from an experimental portal to a fully-fledged portal with more than 1,600 datasets, implemented a Linked Open Data (LOD) system and 8 advanced data visualization projects, and produced OD policies and operat
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Cediel, Ana. "Domestic Tax Regulation in the Face of the Crypto Economy: Challenges Going Forward." In Law, Governance and Technology Series. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74889-9_18.

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AbstractOur tax system clings to a traditional economy linked to pre-digital criteria such as territoriality. It tries to update itself by introducing elements that attempt to deal with the new issues without resolving them head-on. The lack of tax regulation produces a sudden injustice barely resolved by soft law derived from binding administrative resolutions, such as the responses offered by the General Directorate of Taxes.MiCA demonstrates the tax system’s obsolescence in a broad and community sense. The lack of provisions for classifying new economic goods for tax purposes fragments thei
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Conference papers on the topic "Automation in Data Governance"

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Wider, Arif, Katharine Jarmul, and Atif Akhtar. "Towards Automating Federated Data Governance." In 2024 IEEE International Conference on Web Services (ICWS). IEEE, 2024. http://dx.doi.org/10.1109/icws62655.2024.00019.

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Yan, Guodong, Fengrong Zhong, Yan Mao, and Jianguang Ma. "Data security governance in big data industry mechanism logic and order construction." In International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), edited by Weihua Mou. SPIE, 2024. https://doi.org/10.1117/12.3052276.

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Gao, Yuequn, Huifen Ren, and Jianguang Ma. "Ethical governance pattern of big data technology based on intelligent information processing." In International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), edited by Weihua Mou. SPIE, 2024. https://doi.org/10.1117/12.3052355.

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Widen, William H., and Marilyn C. Wolf. "Corporate Governance and Management of AI-Driven Product Development: Vehicle Automation." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546756.

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Herrera, Framcisco. "Responsible Artificial Intelligence Systems: From Trustworthiness to Governance." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546553.

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Bassi, Carlos, Solange Alves-Souza, and Luiz Sergio de Souza. "Data Governance to Be a Data-Driven Organization." In 16th International Conference on Knowledge Management and Information Systems. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012900700003838.

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Bugbee, Kaylin, Rahul Ramachandran, Aaron Kaulfus, et al. "Enabling Dynamic Data Governance in Science: Design, Implementation, and Future Directions of the Modern Data Governance Framework." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640434.

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Thuraisingham, Bhavani, and Teena Thomas. "Social Media Governance and Fake News Detection Integrated with Artificial Intelligence Governance." In 2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2024. http://dx.doi.org/10.1109/iri62200.2024.00048.

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Ntshangase, Sthembile, Nomalisa Ndhlovu, Siphelele Myaka, Oyena Mahlasela, Nokuthaba Siphambili, and Sthembile Mthethwa. "An Evaluation of DL T Governance Models." In 2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2024. http://dx.doi.org/10.1109/icabcd62167.2024.10645266.

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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 &amp; 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,
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Reports on the topic "Automation in Data Governance"

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Carey-Wilson, Thomas, Elena Simperl, Arunav Das, and Ioannis Reklos. Understanding data governance in AI: Mapping governance. Open Data Institute, 2024. https://doi.org/10.61557/irbt3007.

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Yates, Deborah, Elea Himmelsbach, Flor Serale, et al. Health data governance playbook. Open Data Institute, 2022. http://dx.doi.org/10.61557/ejuv7241.

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Chen, Jinfeng. Exploring the relationship between Data Governance and Information Governance. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-707.

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Booth, Harold, and Adam Halbardier. Trust model for security automation data 1.0 (TMSAD). National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7802.

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Sabharwal, Yashvinder, Bruce Kane, David R Hinkler, et al. Data governance needs in biomanufacturing position paper. BioPhorum, 2023. http://dx.doi.org/10.46220/2023tr001.

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Mark J. Stunder, Perry Sebastian, Brenda A. Chube, and Michael D. Koontz. Integration of Real-Time Data Into Building Automation Systems. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/809900.

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Jaimovich, Nir, Itay Saporta-Eksten, Henry Siu, and Yaniv Yedid-Levi. The Macroeconomics of Automation: Data, Theory, and Policy Analysis. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27122.

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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), 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, 2021. http://dx.doi.org/10.61557/prjc8161.

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