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Journal articles on the topic 'Automation in Data Governance'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Dinesh, Thangaraju. "Data Classification: Enabling Robust Data Governance and Access Management in Enterprise Environments." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 8, no. 6 (2022): 1–12. https://doi.org/10.5281/zenodo.14613689.

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In the era of data-driven decision-making, enterprises face challenges in managing and protecting their data assets amidst regulatory compliance requirements and increasing cybersecurity threats. Data classification is the foundation for enabling robust access management, ensuring data quality, enforcing consistent access control policies, and strengthening data governance. This paper highlights the importance of data classification in modern enterprises, outlines key challenges, and proposes a technical framework for implementing a scalable data classification solution. By integrating automat

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Papagianneas, Straton. "Smart Governance in China’s Political-Legal System." China Law and Society Review 6, no. 2 (2023): 146–80. http://dx.doi.org/10.1163/25427466-06020002.

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Abstract The belief in quantitative indicators based on standardized data as an effective tool has become more entrenched than ever before, in both public and corporate governance, because of a drive to achieve more efficiency and accountability. The power of automated computation systems and the ubiquitous availability of big data have magnified the potential and capacities for quantification. The People’s Republic of China (prc) has enthusiastically embraced these advanced technologies. The rapid digitization and automation of social governance in China, called “smart governance,” entail new

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Lakshmi Ayyappan. "Data Warehouse Automation: Streamlining Multi-Cloud ETL Workflows for Real-Time Analytics." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1534–43. https://doi.org/10.32628/cseit251112166.

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This article examines the transformative impact of automation technologies on data warehouse management and multi-cloud ETL workflows in enterprise environments. The article explores how organizations leverage advanced automation solutions to address the growing complexity of real-time analytics and data processing requirements. Through comprehensive article analysis of implementation strategies, the article demonstrates how modern data warehouse automation incorporates artificial intelligence, machine learning, and sophisticated orchestration mechanisms to enhance operational efficiency and d

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Ogunwole, Olufunmilayo, Ekene Cynthia Onukwulu, Micah Oghale Joel, Augustine Ifeanyi Ibeh, and Chikezie Paul-Mikki Ewin. "Advanced Data Governance Strategies: Ensuring Compliance, Security, and Quality at Enterprise Scale." International Journal of Social Science Exceptional Research 2, no. 1 (2023): 156–63. https://doi.org/10.54660/ijsser.2023.2.1.156-163.

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In the digital age, enterprise data governance has emerged as a critical component for ensuring organizations' compliance, security, and data quality. As businesses generate vast amounts of data, traditional governance models are proving inadequate, and organizations face increasing challenges in managing this data effectively. This paper explores advanced data governance strategies, focusing on the evolving landscape shaped by emerging technologies, regulatory pressures, and cybersecurity threats. It provides a comprehensive review of strategic pillars that underpin robust governance framewor

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Ogeawuchi, Jeffrey Chidera, Abel Chukwuemeke Uzoka, Chisom Elizabeth Alozie, Oluwademilade Aderemi Agboola, Toluwase Peter Gbenle, and Samuel Owoade. "Systematic Review of Data Orchestration and Workflow Automation in Modern Data Engineering for Scalable Business Intelligence." International Journal of Social Science Exceptional Research 1, no. 1 (2022): 283–90. https://doi.org/10.54660/ijsser.2022.1.1.283-290.

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This paper presents a systematic review of the advancements in data orchestration and workflow automation within modern data engineering, particularly focusing on their role in enabling scalable business intelligence systems. As organizations increasingly rely on data-driven insights to drive decision-making, the need for efficient, accurate, and real-time data processing has become paramount. Data orchestration frameworks and automation tools such as Apache Airflow, Apache NiFi, and cloud-native platforms like AWS Step Functions have revolutionized how data is integrated, transformed, and del

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Bagja, Amir, Zaenul Amri, Khairul Imtihan, Muhamad Rodi, and Siska Yuni Rusniatun. "Enhancing Public Sector IT Governance through COBIT 2019: A Case Study on Service Continuity and Data Management in the Central Lombok." Journal of Information Systems and Informatics 6, no. 4 (2024): 2761–76. https://doi.org/10.51519/journalisi.v6i4.924.

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This study evaluates the IT governance maturity of the Central Lombok Civil Service Police Unit (Satpol PP) using the COBIT 2019 framework, focusing on improving service continuity and data security in a resource-constrained public sector context. The assessment, conducted across key domains such as service delivery, data security, and compliance, revealed that Satpol PP operates at Level 3 (Defined) maturity. While processes are documented and standardized, significant gaps remain in automation, proactive risk management, and real-time monitoring. These limitations hinder the organization's a

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Khadarvali Shaik. "AI-driven data governance for multi-cloud environments." International Journal of Science and Research Archive 15, no. 2 (2025): 773–88. https://doi.org/10.30574/ijsra.2025.15.2.1284.

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This document investigates how Artificial Intelligence (AI) helps reinforce data governance across multiple cloud settings. Organizational adoption of multi-cloud platforms leads to mounting difficulties for proper data management across various platforms. AI technology provides innovative solutions that help organizations solve issues about data-scattering compliance risks and security vulnerabilities. The research investigated data governance optimization through AI automation using a combination of case studies with industry experts' data analytics and systematic interviews. The study demon

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de, Antônio Márcio José Padovan, Joel Porto Alves, Dircelene Teixeira do Nascimento, and Mírian Cristina de Moura Garrido. "Logistics Automation, AI, and Industry 4.0: Ethical Considerations in a Technological Innovation Scenario." Journal of Urban Mobility, Logistics and Sustainable Smart Cities 2, no. 1 (2025): 38–52. https://doi.org/10.5281/zenodo.14889814.

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This article explores the intersection between Logistics, Industry 4.0, and Artificial Intelligence (AI), highlighting how advanced technologies have transformed logistics processes and increased efficiency. The research, conducted through a systematic literature review, analyzed patterns and significant themes related to the ethical implications of logistics automation in the context of Industry 4.0. The results indicate that the growing adoption of cyber-physical systems and automated algorithms in logistics operations raises critical issues concerning ethical governance, algorithmic account

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Marumolwa, Letlhogonolo, and Carl Marnewick. "Unveiling Dark Data in Organisations." International Journal of Service Science, Management, Engineering, and Technology 16, no. 1 (2025): 1–32. https://doi.org/10.4018/ijssmet.386167.

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The rapid growth of dark data in organisations presents both opportunities and challenges. While dark data contains hidden insights that could improve decision-making, it also leads to compliance, security, and storage risks. This study explores the sources of dark data, its challenges to organisations, and strategies for mitigation of its risks. The findings reveal that legacy systems, unstructured data, and governance gaps are major contributors to dark data accumulation. The study highlights artificial intelligence-driven solutions, role-based access controls, and improved data literacy as

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Chakraborty, Soumen. "Data Stewardship Co-Pilot: Transforming Enterprise Data Governance with Generative AI and Agentic Frameworks." European Journal of Computer Science and Information Technology 13, no. 20 (2025): 1–15. https://doi.org/10.37745/ejcsit.2013/vol13n20115.

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The convergence of Generative AI and agentic frameworks is fundamentally transforming enterprise data governance, offering a solution to the exponential growth in data complexity that traditional manual methods cannot address. This emerging paradigm shift introduces the concept of a Data Stewardship Co-Pilot—an intelligent, AI-powered partner that guides organizations through comprehensive data governance while democratizing access to complex data environments. The evolution from manual processes to intelligent automation employs specialized horizontal and vertical AI agents that autonomously

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Chakraborty, Soumen. "Data Stewardship Co-Pilot: Transforming Enterprise Data Governance with Generative AI and Agentic Frameworks." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 1–14. https://doi.org/10.37745/ejcsit.2013/vol13n22114.

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The convergence of Generative AI and agentic frameworks is fundamentally transforming enterprise data governance, offering a solution to the exponential growth in data complexity that traditional manual methods cannot address. This emerging paradigm shift introduces the concept of a Data Stewardship Co-Pilot—an intelligent, AI-powered partner that guides organizations through comprehensive data governance while democratizing access to complex data environments. The evolution from manual processes to intelligent automation employs specialized horizontal and vertical AI agents that autonomously

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Researcher. "NEXT-GENERATION CLOUD TECHNOLOGIES: EMERGING TRENDS IN AUTOMATION AND DATA ENGINEERING." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1499–507. https://doi.org/10.5281/zenodo.14215882.

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This comprehensive article examines the emerging trends in cloud technologies, focusing on automation and data engineering within modern enterprise architectures. The article explores the evolution from traditional computing models to sophisticated federation frameworks, highlighting the transformative impact across various industry sectors. The investigation encompasses next-generation cloud infrastructure, intelligent automation advances, modern data engineering paradigms, and industry-specific applications. The article analyzes the integration of edge computing, zero-trust security models,

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Wang, Jing. "Study of Ethical Risks and Governance Framework of Generative AI in Financial Reporting Automation." International Journal of Education and Humanities 20, no. 1 (2025): 53–57. https://doi.org/10.54097/mb60fy75.

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The rapid application of generative AI in financial reporting automation raises important ethical governance issues. This study reveals the three core risks of data security leakage, algorithmic bias amplification, and responsibility boundary blurring, and constructs a multi-level governance framework based on technical protection, compliance regulation, and process control. The study innovatively proposes a dynamic ethical assessment mechanism and emphasises the importance of collaborative human-machine supervision. Empirical analyses show that a sound governance system can effectively preven

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Darshan Prakash Patel. "Enhancing healthcare data interoperability with blockchain for compliance automation." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 2744–51. https://doi.org/10.30574/wjarr.2025.26.1.1328.

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This article examines how blockchain technology can revolutionize healthcare data management through enhanced interoperability and automated compliance mechanisms. Healthcare organizations currently face critical challenges with data fragmentation, regulatory adherence, and security vulnerabilities that blockchain architecture addresses through its fundamental characteristics. The decentralized framework creates a secure environment where healthcare stakeholders can exchange information with confidence while maintaining strict privacy controls. Key blockchain components—distributed ledgers, co

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Deepika Annam. "AI-Powered Data Observability & Governance Agent for Cloud Analytics: Transforming Enterprise Data Management." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 804–11. https://doi.org/10.32996/jcsts.2025.7.3.88.

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AI-powered data observability and governance agents represent a transformative approach to managing the increasing complexity of enterprise data ecosystems in cloud analytics environments. As organizations increasingly rely on data-driven decision-making, the challenges of maintaining visibility, quality, and compliance have become more pronounced, necessitating advanced solutions that can scale with expanding data volumes and evolving regulatory requirements. AI-driven observability provides automated monitoring, intelligent root cause analysis, and proactive incident resolution capabilities

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Ogeawuchi, Jeffrey Chidera, Abel Chukwuemeke Uzoka, Chisom Elizabeth Alozie, Oluwademilade Aderemi Agboola, Samuel Owoade, and Oyinomomo-emi Emmanuel Akpe. "Next-generation data pipeline automation for enhancing efficiency and scalability in business intelligence systems." International Journal of Social Science Exceptional Research 1, no. 1 (2022): 277–82. https://doi.org/10.54660/ijsser.2022.1.1.277-282.

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This paper explores the role of next-generation data pipeline automation in enhancing the efficiency and scalability of business intelligence (BI) systems. With the increasing volume and complexity of data, traditional manual extraction, transformation, and loading (ETL) processes have proven inadequate to meet the demands of modern BI applications. Automation offers a transformative solution, enabling faster, more reliable, and scalable data processing workflows. This paper introduces a conceptual framework for data pipeline automation that incorporates microservices, real-time data processin

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Permataisari, Fadah Fenny. "Pengembangan Media Pembelajaran Interaktif Berbasis Quizizz Pada Materi Pelajaran Otomatisasi Tata Kelola Kepegawaian Kelas XI." Jurnal Syntax Admiration 5, no. 10 (2024): 4055–73. http://dx.doi.org/10.46799/jsa.v5i10.1597.

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The purpose of this research is to develop interactive learning media based on wordwall in the subject of Personnel Governance Automation and to determine the feasibility of Quiziz-based learning media in the subject of Personnel Governance Automation class XI Office Management at SMK Satya Bhakti 2 Jakarta. The type of research used is research and development (RnD) using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development method. This research took place at SMK Satya Bhakti 2 Jakarta which was carried out in June 2024. Data collection was conducted by means of i

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Chukwuani, V. N. "The Transformational Impact of Automation and Artificial Intelligence on the Accounting Profession." International Journal of Accounting and Financial Risk Management 5, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.14546797.

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This study investigates the transformational impact of automation and artificial intelligence (AI) on the accounting profession, with a focus on evolving skillsets, ethical considerations, and long-term implications. Employing a qualitative research approach, this research aims to understand how these technologies redefine traditional accounting roles and foster new strategic and advisory functions. Automating repetitive tasks such as data entry and reconciliations, automation, and AI liberate significant time for accountants to engage in more complex, analytical activities that drive business

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Swetha Chinta. "The role of generative AI in oracle database automation: Revolutionizing data management and analytics." World Journal of Advanced Research and Reviews 4, no. 1 (2019): 054–63. http://dx.doi.org/10.30574/wjarr.2019.4.1.0075.

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This research article examines the transformative role of Generative AI in Oracle Database Automation, highlighting its potential to revolutionize data management and analytics. As organizations increasingly rely on data-driven decision-making, efficient and effective database management solutions have become paramount. Integrating Generative AI technologies with Oracle Database systems offers significant benefits, including enhanced data processing capabilities, improved accuracy in analytics, and the automation of routine tasks. This article explores the mechanisms of integration, presents c

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Jumiyanto, Widodo, Sutaryadi, Huda Atma Dirgatama Chairul, and Wahyu Wirawan Arif. "Feasibility test application of information systems in the media as a learning in vocational school." Journal of Education and Learning (EduLearn) 14, no. 1 (2020): 28–33. https://doi.org/10.11591/edulearn.v14i1.14674.

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This study aims to determine the level of eligibility of the application of staffing information systems as a learning media for automation of staffing governance in the Vocational. This research method uses quantitative research methods. Data collection techniques are done using a questionnaire and analysis of needs using quantitative data analysis. Based on the results of the feasibility test on the application of the staffing information system as a learning media for automation of the governance of staffing above it was concluded that the application of the staffing information system can

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Kiran Babu Macha. "Leveraging robotic process automation to optimize government operations and empower citizens: A framework for enhancing service delivery and ensuring compliance." International Journal of Science and Research Archive 6, no. 2 (2022): 150–65. https://doi.org/10.30574/ijsra.2022.6.2.0231.

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The integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in governmental operations is transforming the efficiency of the public sector, service delivery, and policy implementation. This review research systematically examines the primary themes, benefits, and constraints of AI and RPA in governance, emphasizing efficiency, cost reduction, security, and regulatory compliance. Research indicates that AI-driven automation enhances decision-making, predictive analytics, fraud detection, and citizen engagement, while also improving the automation of public services. How

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Mariam, Gadmi, Loulid Adil, and Bendarkawi Zakaria. "THE INTEGRATION OF ARTIFICIAL INTELLIGENCE (AI) INTO EDUCATION SYSTEMS AND ITS IMPACT ON THE GOVERNANCE OF HIGHER EDUCATION INSTITUTIONS." International Journal of Professional Business Review 9, no. 12 (2024): e05176. https://doi.org/10.26668/businessreview/2024.v9i12.5176.

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Objective: The research aims to explore the integration of Artificial Intelligence (AI) within educational systems and analyze its impact on the governance of higher education institutions (HEIs), particularly focusing on decision-making, data protection, and administrative efficiency. Theoretical Framework: The article presents key theories on the transformative role of AI in educational governance, particularly focusing on how AI-driven data analysis and automation enhance decision-making and administrative efficiency. It also addresses theories related to ethical governance, emphasizing dat

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Kiran Kumar Chitrada. "Architecting the Future: Intelligent Data Modeling for Scalable Enterprises." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 591–98. https://doi.org/10.32996/jcsts.2025.7.7.66.

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Enterprise organizations face unprecedented challenges in managing data architectures that support rapidly evolving digital transformation initiatives, cloud-native deployments, and real-time analytics requirements. Traditional relational and dimensional modeling frameworks demonstrate significant limitations when confronted with distributed, heterogeneous data environments that characterize contemporary business operations. Intelligent data modeling emerges as a transformative paradigm that leverages machine learning algorithms, natural language processing capabilities, and graph-based semant

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Rudianto, Rudianto, Misrofingah Misrofingah, Diansanto Prayoga, Juliana Juliana, Saipul Al Sukri, and Kok Shiong Pong. "Understanding Management Marketing in Digitalization and Automation Times." Ekuilibrium : Jurnal Ilmiah Bidang Ilmu Ekonomi 17, no. 2 (2022): 110–21. http://dx.doi.org/10.24269/ekuilibrium.v17i2.2022.pp110-121.

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This study discusses some of the latest trends in business marketing governance in the era of Automation by emphasizing what is happening in business today compared to the past. This study is based on published data in the literature and internet-based data from various research articles, newspaper reports, and various website sites that actively discuss marketing trend issues in digital technology days. We understand that the business now and in the past still have the same goal, but what makes the difference is the marketing work system that used to be conventional but has now switched to di

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Leghemo, Iveren M., Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu, and Chima Azubuike. "Continuous Data Quality Improvement in Enterprise Data Governance: A Model for Best Practices and Implementation." Journal of Engineering Research and Reports 27, no. 2 (2025): 29–45. https://doi.org/10.9734/jerr/2025/v27i21391.

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Continuous Data Quality Improvement (CDQI) is essential for maintaining the integrity, accuracy, and reliability of enterprise data. In today's data-driven organizations, ensuring high-quality data across various systems and departments is critical for decision-making, operational efficiency, and regulatory compliance. This review presents a model for CDQI within the framework of enterprise data governance, outlining best practices and implementation strategies for sustained improvements in data quality. The proposed model integrates key components such as data quality assessment, improvement

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Prof., Kshama Ananda Gir. "Ethical Considerations in AI and Automation." International Journal of Advance and Applied Research S6, no. 23 (2025): 370–73. https://doi.org/10.5281/zenodo.15227337.

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<em>Artificial Intelligence (AI) and automation have significantly transformed industries, leading to increased efficiency and innovation. These advancements have reshaped sectors such as healthcare, finance, manufacturing, and education by streamlining operations, reducing human error, and enabling data-driven decision-making. However, the rapid integration of AI and automation raises critical ethical concerns, including data privacy, algorithmic bias, job displacement, and accountability.</em> <em>One of the most pressing ethical concerns in AI is bias and fairness. AI systems learn from his

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Srujana, Manigonda. "Data Privacy and Sovereignty in Financial Technology: Governance Strategies for Global Operations." International Journal on Science and Technology 12, no. 2 (2021): 1–8. https://doi.org/10.5281/zenodo.14474439.

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In the era of globalized financial technology, data privacy and sovereignty have emerged as critical challenges for organizations navigating complex regulatory landscapes. With varying regional regulations such as GDPR and CCPA, businesses must strike a balance between compliance, operational efficiency, and user trust. This paper presents governance strategies rooted in real-world experience from critical industries such as financial technology and manufacturing, emphasizing the importance of robust data processing pipelines, quality assurance, and traceability. It explores practical solution

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Joseph, Sunday Abayomi, Titilayo Modupe Kolade, Onyinye Obioha Val, Olubukola Omolara Adebiyi, Olumide Samuel Ogungbemi, and Oluwaseun Oladeji Olaniyi. "AI-Powered Information Governance: Balancing Automation and Human Oversight for Optimal Organization Productivity." Asian Journal of Research in Computer Science 17, no. 10 (2024): 110–31. http://dx.doi.org/10.9734/ajrcos/2024/v17i10513.

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This study employs a mixed-methods approach to examine the optimal balance between AI-powered automation and human oversight in information governance frameworks, aiming to enhance organizational productivity, efficiency, and compliance. Quantitative data collected from 384 respondents were analyzed using Pearson correlation, regression models, and Structural Equation Modeling (SEM). The results reveal strong positive correlations between AI automation levels and both organization size (r = 0.55, p < .01) and AI adoption duration (r = 0.62, p < .01). Regression analysis indicates that hi

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Gudipudi, Sujith. "Toward Autonomous Business Intelligence: Research Trends in Automation and Cloud Integration." European Journal of Computer Science and Information Technology 13, no. 49 (2025): 163–77. https://doi.org/10.37745/ejcsit.2013/vol13n49163177.

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Business Intelligence infrastructure is experiencing a fundamental transformation as autonomous systems progressively replace manual intervention paradigms. This evolution extends far beyond basic automation to create self-managing, self-optimizing analytics environments. Cloud integration serves as a critical enabler, allowing for serverless architectures and event-driven responses that continuously adapt to changing conditions. The shift toward autonomy delivers substantial advantages across multiple dimensions: accelerated decision cycles, enhanced analytical accuracy, reduced operational c

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Shafeeq, Ur Rahaman. "Beyond the Data Lake: Harnessing Real-Time Analytics and Automation for Dynamic Decision-Making." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 75, no. 5 (2019): 1–9. https://doi.org/10.5281/zenodo.14352006.

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The revolution going from traditional static data lakes to agile, real-time analytics engines that is really changing how organizations derive value from their data. Advanced integration of real-time analytics with automation in dynamic decision-making is discussed in this article. Using real-time streaming of data, machine learning algorithms, and intelligent automation, the raw data transformation into actionable insights can be facilitated for organizations in real time. These are the very latest innovations that enable companies to make business operations more efficient, significantly enh

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Vinay Gali. "Cloud ERP Implementations: A Comprehensive Guide to Oracle Financials and Master Data Management." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 1636–42. https://doi.org/10.47392/irjaem.2025.0264.

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Cloud-based Enterprise Resource Planning (ERP) systems have revolutionized how organizations manage their financial operations, data governance, and compliance requirements. Among the leading solutions, Oracle Financials Cloud, when integrated with Master Data Management (MDM) systems, offers unparalleled capabilities in automation, real-time analytics, and financial control. This review presents a comprehensive examination of Oracle Financials Cloud architecture, MDM integration, implementation strategies, and empirical performance outcomes across industries. Drawing from over a decade of res

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Karpenko, Oleksandr, Nataliia Vasiuk, and Anton Osmak. "ARTIFICIAL INTELLIGENCE AS A DIGITAL TOOL FOR THE PROJECT APPROACH IN PUBLIC ADMINISTRATION AND LOCAL GOVERNANCE." Strategy of Economic Development of Ukraine, no. 55 (December 28, 2024): 189–200. https://doi.org/10.33111/sedu.2024.55.189.200.

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The article substantiates the necessity of applying artificial intelligence (AI) technologies as a digital tool for the project-based approach in local governance. Local self-government plays a critical role in ensuring the functioning of communities, particularly during the current crisis caused by the Russian military invasion. Integrating modern AI-based management approaches enhances process automation, big data analysis, and resource optimization, improving efficiency. The study identifies key directions for AI application in local governance: project planning, control, and timeline manag

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Florence Ifeanyichukwu Olinmah, Bisayo Oluwatosin Otokiti, Olayinka Abiola-Adams, and Benjamin Monday Ojonugwa. "Designing Regulatory Risk Reporting Frameworks Using Automation Tools in Banking Sector Compliance." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 5 (2024): 353–67. https://doi.org/10.32628/ijsrset24105476.

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In the evolving landscape of banking sector compliance, regulatory risk reporting has become increasingly complex due to heightened international standards and the proliferation of digital operations. This paper proposes a comprehensive framework for designing regulatory risk reporting systems leveraging automation tools to address persistent challenges such as data fragmentation, reporting delays, and human error. It systematically integrates core design principles including accuracy, transparency, timeliness, and traceability with automation technologies such as robotic process automation an

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Somanathan, Sureshkumar. "Data Science in Multi-Cloud Governance: Insights for Security, Scalability, and Risk Mitigation." Nanotechnology Perceptions 20, S2 (2024): 1172–79. https://doi.org/10.5281/zenodo.15270464.

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As companies search for more resilience, scalability, and flexibility in their IT systems, multi-cloud systems are becoming more and more used. Particularly with relation to security, scalability, and risk reduction, managing governance throughout several cloud platforms presents significant challenges. Conventional governance systems sometimes fall short in providing quick understanding of security concerns, compliance issues, and resource optimization. By use of advanced analytics, machine learning, and predictive modelling to translate raw multi-cloud data into actionable insights, data sci

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Kolade, Titilayo Modupe, Nsidibe Taiwo Aideyan, Seun Michael Oyekunle, Olumide Samuel Ogungbemi, Dooshima Louisa Dapo-Oyewole, and Oluwaseun Oladeji Olaniyi. "Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security." Asian Journal of Research in Computer Science 17, no. 12 (2024): 36–57. https://doi.org/10.9734/ajrcos/2024/v17i12528.

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This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the

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Legito. "Examining the Effects of Robotic Process Automation on Operational Efficiency and Business Process Optimization (Literature Study)." West Science Interdisciplinary Studies 1, no. 02 (2023): 84–93. https://doi.org/10.58812/wsis.v1i2.91.

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This research study investigates the influence of Robotic Process Automation (RPA) on operational efficiency and business process optimization. The study adopted a systematic literature review approach to collect and analyze relevant academic articles, industry reports, and conference proceedings. Findings reveal that RPA implementation significantly improves operational efficiency by automating repetitive and rule-based tasks. This automation reduces manual effort, minimizes errors, and speeds up process execution, leading to increased productivity, faster response times, and cost savings for

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Pavan Kumar Bollineni. "The Future of Data Platforms: AI-Driven Automation and Self-Optimizing Systems." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 483–88. https://doi.org/10.32996/jcsts.2025.7.2.50.

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The evolution of data platforms is entering a new era characterized by AI-driven automation and self-optimizing capabilities that address the unprecedented challenges of exponential data growth. As organizations struggle with increasingly complex data ecosystems, traditional management approaches are becoming inadequate. This document presents how next-generation data platforms leverage artificial intelligence to transform data operations through four key innovations: metadata intelligence serving as the nervous system of modern platforms; self-healing data pipelines that autonomously detect a

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Ravi Teja Balla. "Enabling Cognitive Process Automation Using LLMs in ERP Systems with Generative AI." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1467–74. https://doi.org/10.30574/wjaets.2025.15.3.1045.

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This article examines the integration of Large Language Models (LLMs) and Generative AI within Enterprise Resource Planning (ERP) systems, highlighting the transformative impact on cognitive process automation. As organizations transition toward autonomous operations, generative AI capabilities embedded within cloud infrastructure and applications create new paradigms for intelligent automation across finance, procurement, and human resources functions. The architectural framework combines foundation models with domain-specific enterprise data to enable conversational interfaces, document unde

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Ahmad, Shafi, Dillidorai Arumugam, Srdan Bozovic, et al. "Microsoft Purview: A System for Central Governance of Data." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3624–35. http://dx.doi.org/10.14778/3611540.3611552.

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Modern data estates are spread across data located on premises, on the edge and in one or more public clouds, spread across various sources like multiple relational databases, file and storage systems, and no-SQL systems, both operational and analytic; this phenomenon is referred to as data sprawl. Data administrators who wish to enforce compliance across the entire organization have to inventory their data, identify what parts of it are sensitive, and govern the sensitive data appropriately --- across the entirety of their sprawling data estate. Today, governance of data is completely siloed;

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Ramesh, Betha. "Beyond ETL: Orchestrating End-to-End Data Products with Modern Automation Frameworks." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 12, no. 3 (2024): 1–11. https://doi.org/10.5281/zenodo.15084298.

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The traditional Extract, Transform, Load (ETL) paradigm that has governed data integration for decades is rapidly becoming insufficient for modern enterprise data needs. As organizations transition from siloed data management to comprehensive data products, there exists a critical need for orchestration capabilities that transcend basic ETL functionality. This paper examines the evolution from ETL-centric approaches to holistic data product orchestration, evaluates emerging automation frameworks that facilitate this transition, and proposes an architecture for end-to-end data product lifecycle

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