Data Governance

This study examines the role of financial education and employability skills in enhancing corporate financial performance over the last century. By tracing the evolution of corporate training programs, the paper highlights how investments in workforce education have shifted from operational costs to strategic assets. Case studies and historical examples illustrate how financially literate and skilled employees contribute to resilience, innovation, and profitability within organizations. This analysis provides evidence that a combination of financial literacy and employability skills fosters improved decision-making, engagement, and risk management across organizational levels, supporting long-term financial growth.

Members of the organic supply chain need high-quality data to make correct investment decisions, but data with sufficient depth and quality are not widely available in Europe. The quality of available data is a key concern for both data collectors and data users. The aim of this study is to identify whether the commonly used quality attributes (accuracy, coherence, comparability, timeliness, punctuality, accessibility, relevance), which have been developed from the perspective of data collectors, are also appropriate from the perspective of end users of organic market data. A further aim is to assess whether the data quality needs of end users are being met by the existing data. The results of two surveys carried out in Europe, one of data collectors and one of end users, are presented. Sales data at retail level (values and volumes) are used as an illustrative example and the perceptions of end users are compared with the reported data collection approaches, quality checks and availability of data. Correlation analysis and principal component analysis were used to investigate the relationship between users' perceptions of the data quality attributes and their overall perceptions of data quality. The findings suggest that data quality checks do help to improve the quality of data as perceived by end users but that people will use whatever data they can get, even if it has poor quality. This could have potentially negative consequences, such as a lack of confidence in the organic market, if important decisions are based on poor quality data. The analysis also suggests that the commonly used attributes represent two dimensions of data quality: 'fitness for use' which encompasses accuracy, relevance, comparability and punctuality; and 'convenience', which encompasses affordability, comparability, timeliness and accessibility. The attribute of comparability belongs to both dimensions as it contributes to both fitness for use and convenience. Data collectors wishing to improve the quality of their data should focus on enhancing fitness for use first and then on the convenience of their data for users.

A scientifically informed approach to decision-making is key to ensuring the sustainable management of ecosystems, especially in the light of increasing human pressure on habitats and species. Protected areas, with their long-term institutional mandate for biodiversity conservation, play an important role as data providers, for example, through the long-term monitoring of natural resources. However, poor data management often limits the use and reuse of this wealth of information. In this paper, we share lessons learned in managing long-term data from the Italian Alpine national parks. Our analysis and examples focus on specific issues faced by managers of protected areas, which partially differ from those faced by academic researchers, predominantly owing to different mission, governance, and temporal perspectives. Rigorous data quality control, the use of appropriate data management tools, and acquisition of the necessary skills remain the main obstacles. Common protocols for data collection offer great opportunities for the future, and complete recovery and documentation of time series is an urgent priority. Notably, before data can be shared, protected areas should improve their data management systems, a task that can be achieved only with adequate resources and a long-term vision. We suggest strategies that protected areas, funding agencies, and the scientific community can embrace to address these problems. The added value of our work lies in promoting engagement with managers of protected areas and in reporting and analysing their concrete requirements and problems, thereby contributing to the ongoing discussion on data management and sharing through a bottom-up approach.

In this digital era, artificial intelligence (AI) is revolutionizing all the fields known to man. Data governance and data lineage practices are no exception. AI is making its mark in the area of Data governance and Lineage, making them more efficient and reliable. This paper explores the integration of AI into data governance frameworks, emphasizing its role in automating data quality checks, enhancing regulatory compliance, and ensuring data security. Furthermore, the study examines how AI driven data lineage provides comprehensive visibility into data movements, improving transparency and accountability. By detailing the synergy between AI, data governance, and data lineage, the paper highlights how these advancements enable organizations to manage data more effectively and gain actionable insights.

Organizations are mostly dependent on data warehouses to store and analyze information. As the complexity of data warehouses increase, new challenges are faced in data security and privacy as legacy security approaches are lacking the capabilities to address the unique requirements. This paper provides an overview of the current research on leveraging artificial intelligence (AI) to enhance data security and privacy within data warehouses. This paper will discuss the challenges, review current AI techniques used for securing data warehouses, and propose potential solutions for balancing AI driven insights with the protection of individual privacy.

Extracting, transforming, and loading data is a crucial task in data-driven organizations. However, the process of building an ETL workflow can be complex and time-consuming, especially when dealing with diverse data sources and formats. In this research paper we will discuss about a reusable ETL framework that uses custom table format to simplify the development and deployment of ETL processes. This paper will also discuss about the benefits

Adoption of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) internationally and in Australia has enabled the conversion of vast amounts of complex, and heterogeneous electronic medical record (EMR) data into a standardised structured data model. This helps simplify governance processes and facilitates rapid, repeatable cross-institution analysis through shared end-to-end analysis packages without the sharing of raw data. Combined with pseudonymisation and standardised data quality assessments, the OMOP-CDM provides a powerful model to support ethical real-world ‘big’ data research. The continued adoption of OMOP-CDM, ongoing development efforts, and the emphasis on sound governance practices all contribute to the realisation of OMOP’s utility in unlocking valuable EMR data. These factors collectively support a wide range of applications, from health service operational reporting to diverse clinical, epidemiological, and translational research projects.

APIs enhance healthcare connectivity. This study revealed that API-led connections could enhance healthcare data exchange, efficiency, and collaboration. Incompatible hospitals result from fractured systems, disparate data formats, and divergent protocols. These obstacles hinder efficiency, decision-making, and patient care. APIs facilitate the secure exchange of real-time medical data among healthcare enterprises, addressing multiple challenges. Module-based API connectivity enables the transmission of healthcare data. API integration facilitates communication of patient data among doctors, insurers, laboratories, and others, hence enhancing decisionmaking. This study illustrates REST and HL7 FHIR APIs. The protocols enable healthcare organizations to create scalable APIs for future requirements without involving legacy systems. Medical data is sensitive; hence, API-driven healthcare integration employs security measures. Healthcare providers protect patient data through the implementation of OAuth 2.0 and OpenID Connect in response to API cybersecurity concerns. Trust in healthcare patients and HIPAA necessitate secure data transmission. The complexities of patient consent and business data exchange may be exacerbated by healthcare APIs. This study demonstrates that API-led integration enhances clinical, administrative, and patient outcomes. APIs for EHR data sharing may enable physicians to focus on patient care by reducing administrative tasks. Imaging, laboratory, and pharmacological data enhance diagnosis and treatment. integrate these technologies with hospital infrastructure to facilitate efficient, patient-centered, and cost-effective care. API integration modifies healthcare interoperability. APIs facilitate secure, real-time data exchange for healthcare, patient outcomes, and research. To obtain these benefits, healthcare organizations must surmount the technological, operational, and regulatory challenges of API use. API integration has the potential to enhance interoperability within healthcare IT systems.

Penggunaan internet yang tinggi, menuntut
pemerintah menghadirkan berbagai layanan
berbasis teknologi digital. Alih-alih memberikan
kemudahan pada masyarakat, tumbuh pesatnya
berbagai aplikasi ternyata malah membuat
masyarakat kesulitan karena harus mengakses
berbagai aplikasi. Riuhnya aplikasi pemerintah
dijagat maya disebabkan beberapa hal yaitu:
inovasi telah membawa semangat mendigitalkan
berbagai layanan, adanya dukungan anggaran
untuk membangun aplikasi digital , belum
o p t ima l n ya imp l eme nt a s i i nt e g ra s i d a n
keterbatasan SDM TI. Adapun solusi yang
ditawarkan: perubahan mindset dalam berinovasi;
evaluasi dan monitoring layanan digital;
pengintegrasian layanan publik berbasis digital
dan optimalisasi SDM TI dalam pengintegrasian
aplikasi

Puji syukur kehadirat Allah SWT yang telah melimpahkan rahmat dan karunia-Nya, sehingga penulis dapat menyelesaikan makalah ini yang berjudul " PENERAPAN BIG DATA DALAM BIDANG KESEHATAN " tepat pada waktunya. Makalah ini disusun untuk memenuhi salah satu tugas dalam mata Big Data di Program Studi Sistem Informasi, Universitas Hasanuddin. Di dalam makalah ini, penulis membahas tentang Penerapan Big Data di sektor Kesehatan yang bertujuan untuk memberikan pemahaman mendalam mengenai potensi Big Data dalam meningkatkan efisiensi,efektivitas,dan kualitas layanan kesehatan Penulis menyadari bahwa makalah ini masih jauh dari sempurna. Oleh karena itu, kritik dan saran yang membangun sangat diharapkan demi penyempurnaan dan pengembangan makalah ini di masa yang akan datang. Akhir kata, penulis mengucapkan terima kasih kepada dosen pembimbing Mata Kuliah Ibu Angdy S.kom ., M.Si selaku dosen pembimbing mata kuliah, kepada rekanrekan mahasiswa, serta kepada semua pihak yang telah memberikan dukungan, baik secara langsung maupun tidak langsung, selama penyusunan makalah ini. Semoga makalah ini dapat memberikan manfaat bagi pembaca dan menjadi referensi yang berguna dalam pengembangan ilmu pengetahuan di bidang Big Data dan Kesehatan.

AI-driven Master Data Management (MDM) frameworks have emerged as a cornerstone for ensuring data integrity, real-time synchronization, and enhanced business value. By leveraging machine learning algorithms and advanced analytics, these frameworks streamline data governance, reduce inconsistencies, and enable actionable insights. This paper explores the role of AI in transforming traditional MDM systems, reviews existing literature, and identifies the benefits, challenges, and future prospects of these frameworks.

Smart cities aim to provide more digitalized, equitable, sustainable, and liveable cities. In smart cities data evolves as an important asset and citizens data in particular is being used to provide data-driven mobility services. Likewise, in smart cities data is produced and used by individuals having no ownership or control over these data thus affecting their security and privacy. Also, actors such as mobility service providers, companies, third parties, etc. often leverage individuals' data without regarding users' autonomy. Accordingly, data sovereignty which entails the possibility to govern and keep control over owned data is gaining widespread attention. As a result, stakeholders in the mobility sector need to share data whilst keeping control to manage data access and usage. Although, existing initiatives are promoting sovereign participation using a federated infrastructure. However, achieving an effective governance usage control and access mechanisms can be challenging especially in the transportation sector. It is thus imperative for individuals to have control, ownership, and custody over their data. This has necessitated the notions of data sovereignty. Therefore, this article employs a systematic review to examine how data sovereignty can be achieved for individuals when they use urban mobility services in smart cities by enabling the secure sharing of data by giving control of data over to individuals. Additionally, this article designs a data control scheme that can be applied to realize data sovereign mobility services. Evidence from this study provides technical and non-technical requirements needed in realizing data sovereignty in smart cities.

The Nigeria Electronic Management of Immunization Data (EMID) system was optimized to address challenges with managing COVID-19 vaccination data, including interface issues and slow data synchronization. n Resolving synchronization and speed issues helped facilitate the integration of a routine immunization module to meet the country's vision of an integrated primary health care service data management platform. n The EMID system optimization and integration of COVID-19 and routine immunization data into 1 system has resulted in a significant increase in the number of vaccinations documented, improved data synchronization, faster data entry speed, better data quality and integrity, and a streamlined health workforce to deliver COVID-19 vaccines and routine immunizations.

In the contemporary business landscape, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into data-driven strategies has emerged as a pivotal factor for organizational success and competitive advantage. This paper delineates a comprehensive framework for leveraging AI and ML to enhance business analytics, improve decision-making processes, and foster organizational growth. The framework proposed herein serves as a strategic guide for businesses seeking to harness the transformative potential of these technologies. AI and ML technologies have revolutionized the domain of business analytics by providing sophisticated tools for data processing, pattern recognition, and predictive modeling. The application of AI algorithms facilitates the extraction of actionable insights from vast and complex datasets, enabling organizations to make informed decisions with unprecedented accuracy. Machine learning models, with their capacity for adaptive learning and iterative refinement, offer dynamic analytical capabilities that are crucial for navigating the rapidly evolving business environment.

The quality of data becomes an essential component for the success of an organisation in a world that is largely influenced by data, where data analytics is becoming increasingly popular in the process of informing strategic decisions. The failure to improve the quality of the data can lead to undesirable outcomes such as poor decisions, ineffective strategies, dysfunctional operations, lost commercial prospects, and abrasion of the consumer. In the process of organisations shifting their focus towards transformative methods such as generative artificial intelligence, several use cases may emerge that have the potential to aid the improvement of data quality. Streamlining procedures such as data classification, metadata management, and policy enforcement can be accomplished by the incorporation of generative artificial intelligence into data governance frameworks. This, in turn, reduces the workload of human data stewards and minimises the possibility of human error. In order to ensure compliance with legal standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), generative artificial intelligence may analyse enormous datasets by utilising machine learning algorithms to discover patterns, inconsistencies, and compliance issues.

The volume and complexity of data is always increasing, and organisations are becoming more adept at using data, which in turn is creating new needs for various methods of data aggregation, manipulation, storage, and presentation. The reality of running a business is becoming too much for data management systems to handle on its own, and progressive businesses have come to this realisation. On the other hand, they have started to see that robust data governance is crucial to solving the data problem. Past efforts at data governance were unsuccessful due to IT-driven, inflexible processes and fragmented, system-by-system approaches. Governance has traditionally been informal, happening in silos around specific enterprise repositories, and there has been a lack of organisational support and structure. With the rise of cloud computing and its widespread use, experts are paying more attention to the topic of data governance, which is surprisingly understudied. Initial studies aimed at creating efficient cloudbased data governance systems are detailed in this article. The significance of data governance in the cloud is discussed in this paper in a way that is both informative and accessible to cloud providers and consumers. Next, the research provides a theoretical basis for the system's development along with a five-stage procedure.

The purpose of this research was to determine how well AI systems perform in predicting risks and how they help with business continuity planning. Disasters, cyberattacks, and economic swings are just a few of the many risks that businesses face. The purpose of this study was to identify the ways in which several AI componentsincluding incident response planning, AI-powered data analytics, and natural language processing (NLP)-improve risk assessment and bolster business continuity in such a setting. Using AI and ML to foresee and lessen the impact of potential risks has grown more important in this age of investment sector uncertainty. This paper outlines a comprehensive methodology architecture, detailing each step from data collection and preprocessing to model development, deployment, and continuous monitoring. We explore the frequency of various AI/ML techniques, with supervised learning and time series analysis emerging as predominant methods. Performance metrics indicate that neural networks and gradient boosting models excel in accuracy, precision, and recall. Resource allocation analysis reveals significant emphasis on model development and data preprocessing. This integrated approach ensures robust risk management by employing diverse techniques and strategically distributing resources, thereby enhancing the ability to predict and mitigate risks effectively.

The moral, ethical, and legal protections of artificial intelligence (AI) have come under scrutiny due to recent developments in the field. A more ethical approach to managing AI technology is urgently required, as is the development of better measures for evaluating AI system privacy and security. To tackle these issues, we suggest a model for AI maturity and a framework for AI trust to improve confidence in AI system design and administration. For AI to be trusted, people and machines must first reach a mutual understanding on the system's performance. Improved openness and confidence in unregulated "black box" AI systems are goals of the framework's "entropy lens" research, which is based on information theory. In highly competitive and unpredictable settings, human trust in AI systems can be diminished due to their high entropy. This study uses insights from entropy research to enhance the reliability and efficiency of autonomous human-machine teams and systems, particularly those including hierarchical components and their interconnections. Using this perspective to boost faith in AI also reveals untapped potential for team efficiency. We provide two examples to show that the AI framework can accurately gauge confidence in AI system design and administration. For its outstanding capacity to produce realistic data, Generative Artificial Intelligence (GAI) has set off a revolutionary wave in many fields, such as machine learning, healthcare, commerce, and the entertainment industry. An exhaustive analysis of the privacy and security issues related to GAI is provided by this survey. It offers five crucial viewpoints that are necessary for a thorough comprehension of these complexities. Various generative model types, GAI designs, practical applications, and current advances in the field are covered in the study. It also notes existing security methods and suggests long-term fixes with an emphasis on participation from users, developers, institutions, and lawmakers.

The governance of data is a vital component for every organization, but it is of utmost significance in situations when there are a significant number of users both within the organization and beyond its boundaries or boundaries. To ensure that only authorized users are able to access the required data, it is essential necessary to implement governance policies that are both effective and efficient. Although many organizations have governance boards, problems continue to exist, which results in weaknesses inside the system. This study presents an inquiry into the ways in which modern technologies, such as artificial intelligence and machine learning, have the potential to improve data governance procedures within enterprises. Specifically, the study focuses on how these technologies can improve operations. There is an analysis of the limitations that are inherent in conventional methods, as well as insights into the tactics that might be applied to improve governance practices, which are both included in the framework of this debate.

The governance of data is a vital component for every organization, but it is of utmost significance in situations when there are a significant number of users both within the organization and beyond its boundaries or boundaries. To ensure that only authorized users are able to access the required data, it is essential necessary to implement governance policies that are both effective and efficient. Although many organizations have governance boards, problems continue to exist, which results in weaknesses inside the system. This study presents an inquiry into the ways in which modern technologies, such as artificial intelligence and machine learning, have the potential to improve data governance procedures within enterprises. Specifically, the study focuses on how these technologies can improve operations. There is an analysis of the limitations that are inherent in conventional methods, as well as insights into the tactics that might be applied to improve governance practices, which are both included in the framework of this debate.

The quality of data becomes an essential component for the success of an organisation in a world that is largely influenced by data, where data analytics is becoming increasingly popular in the process of informing strategic decisions. The failure to improve the quality of the data can lead to undesirable outcomes such as poor decisions, ineffective strategies, dysfunctional operations, lost commercial prospects, and abrasion of the consumer. In the process of organisations shifting their focus towards transformative methods such as generative artificial intelligence, several use cases may emerge that have the potential to aid the improvement of data quality. Streamlining procedures such as data classification, metadata management, and policy enforcement can be accomplished by the incorporation of generative artificial intelligence into data governance frameworks. This, in turn, reduces the workload of human data stewards and minimises the possibility of human error. In order to ensure compliance with legal standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), generative artificial intelligence may analyse enormous datasets by utilising machine learning algorithms to discover patterns, inconsistencies, and compliance issues.

The volume and complexity of data is always increasing, and organisations are becoming more adept at using data, which in turn is creating new needs for various methods of data aggregation, manipulation, storage, and presentation. The reality of running a business is becoming too much for data management systems to handle on its own, and progressive businesses have come to this realisation. On the other hand, they have started to see that robust data governance is crucial to solving the data problem. Past efforts at data governance were unsuccessful due to IT-driven, inflexible processes and fragmented, system-by-system approaches. Governance has traditionally been informal, happening in silos around specific enterprise repositories, and there has been a lack of organisational support and structure. With the rise of cloud computing and its widespread use, experts are paying more attention to the topic of data governance, which is surprisingly understudied. Initial studies aimed at creating efficient cloudbased data governance systems are detailed in this article. The significance of data governance in the cloud is discussed in this paper in a way that is both informative and accessible to cloud providers and consumers. Next, the research provides a theoretical basis for the system's development along with a five-stage procedure.

The purpose of this research was to determine how well AI systems perform in predicting risks and how they help with business continuity planning. Disasters, cyberattacks, and economic swings are just a few of the many risks that businesses face. The purpose of this study was to identify the ways in which several AI components— including incident response planning, AI-powered data analytics, and natural language processing (NLP)—improve risk assessment and bolster business continuity in such a setting. Using AI and ML to foresee and lessen the impact of potential risks has grown more important in this age of investment sector uncertainty. This paper outlines a comprehensive methodology architecture, detailing each step from data collection and preprocessing to model development, deployment, and continuous monitoring. We explore the frequency of various AI/ML techniques, with supervised learning and time series analysis emerging as predominant methods. Performance metrics indicate that neural networks and gradient boosting models excel in accuracy, precision, and recall. Resource allocation analysis reveals significant emphasis on model development and data preprocessing. This integrated approach ensures robust risk management by employing diverse techniques and strategically distributing resources, thereby enhancing the ability to predict and mitigate risks effectively.

The moral, ethical, and legal protections of artificial intelligence (AI) have come under scrutiny due to recent developments in the field. A more ethical approach to managing AI technology is urgently required, as is the development of better measures for evaluating AI system privacy and security. To tackle these issues, we suggest a model for AI maturity and a framework for AI trust to improve confidence in AI system design and administration. For AI to be trusted, people and machines must first reach a mutual understanding on the system's performance. Improved openness and confidence in unregulated "black box" AI systems are goals of the framework's "entropy lens" research, which is based on information theory. In highly competitive and unpredictable settings, human trust in AI systems can be diminished due to their high entropy. This study uses insights from entropy research to enhance the reliability and efficiency of autonomous human-machine teams and systems, particularly those including hierarchical components and their interconnections. Using this perspective to boost faith in AI also reveals untapped potential for team efficiency. We provide two examples to show that the AI framework can accurately gauge confidence in AI system design and administration. For its outstanding capacity to produce realistic data, Generative Artificial Intelligence (GAI) has set off a revolutionary wave in many fields, such as machine learning, healthcare, commerce, and the entertainment industry. An exhaustive analysis of the privacy and security issues related to GAI is provided by this survey. It offers five crucial viewpoints that are necessary for a thorough comprehension of these complexities. Various generative model types, GAI designs, practical applications, and current advances in the field are covered in the study. It also notes existing security methods and suggests long-term fixes with an emphasis on participation from users, developers, institutions, and lawmakers.

The popularity of open-source AI code and models is on the rise, particularly among smaller firms, research institutions, and individual users. This is so even if tech companies are progressively controlling and dominating the growing market for generative AI. Unfortunately, they are unable to make this data publicly available for training purposes due to limited computational resources and concerns about data protection. Unfortunately, they are often unable to disclose high-quality data that could be used for training purposes. One possible solution to these two issues could be to train generative AI using crowd-sourcing principles and federated learning techniques to build a distributed architecture that protects privacy. We address in this paper the ways in which these two important enablers, in conjunction with other new technologies, might be put together to form a community-driven ecosystem for generative AI. In this way, even minor players in the ecosystem will be able to safely provide training data for generative AI models. In addition to outlining future research objectives in AI moderation, the report also discusses relevant non-technical issues, such as community duty and intellectual property rights.

The governance of data is a vital component for every organization, but it is of utmost significance in situations when there are a significant number of users both within the organization and beyond its boundaries or boundaries. To ensure that only authorized users are able to access the required data, it is essential necessary to implement governance policies that are both effective and efficient. Although many organizations have governance boards, problems continue to exist, which results in weaknesses inside the system. This study presents an inquiry into the ways in which modern technologies, such as artificial intelligence and machine learning, have the potential to improve data governance procedures within enterprises. Specifically, the study focuses on how these technologies can improve operations. There is an analysis of the limitations that are inherent in conventional methods, as well as insights into the tactics that might be applied to improve governance practices, which are both included in the framework of this debate.

The last decade, software project delivery has seen a significant shift from the Waterfall methodology to Agile software development. Despite these changes, the importance of software testing and quality remains paramount. This article delves into the critical role of Key Performance Indicators KPIs in software testing, evaluating both historically used and newly required KPIs to ensure project success. It covers various KPIs such as test case preparation productivity, defect density, and test automation coverage, as well as Agilespecific metrics like sprint burndown and test coverage velocity. The benefits of utilizing these KPIs include improved product quality, enhanced predictability, faster time to market, and better risk management. Additionally, the article discusses essential considerations for accurate data collection, stakeholder alignment, and continuous evaluation. By integrating and refining these metrics, teams can achieve greater efficiency, quality, and continuous improvement in Agile environments.

Resumo e essência: Ter uma educação de alto padrão é essencial, pois contribui para o desenvolvimento pessoal e social dos indivíduos, além de favorecer o avanço da sociedade como um todo. Um ensino de excelente qualidade alcança as metas de: Aumentar a taxa de emprego, aprimorar a educação de cidadãos conscientes de seus direitos e deveres fortalecer a democracia e a cidadania, aumenta a democracia e a cidadania, contribui para o crescimento econômico, melhora a qualidade de vida, forma indivíduos capazes de resolver problemas e inovar, Ajuda na formação de indivíduos comprometidos com a vida e o progresso da sociedade. Honestamente, uma instituição de ensino de excelência é aquela que proporciona um ensino de alto padrão, que se preocupa verdadeiramente em assegurar o aprendizado dos conteúdos para a formação de cidadãos úteis. É crucial investir na qualidade da educação, pois os estudantes que têm acesso a um ensino de alto padrão possuem mais probabilidades de obter êxito em suas trajetórias profissionais e pessoais. Portanto, uma instituição de ensino que proporciona um ensino de alto padrão possui maior credibilidade e atrai mais alunos. Nesta breve explicação escrita, vou detalhar os três estágios de um ensino de excelência, além de explicar como e por que é importante investir na excelência acadêmica nas escolas brasileiras.

Insurance industry undergoes major regulatory changes regarding risk management like Solvency II which require managing data quality. This paper reports an experience feedback about the development of an enterprise architecture and data quality framework suitable for Insurance Industry and COTS 1 environments. The framework, inspired by TOGAF 9.1, is tailored to provide systemic views of enterprise organization, systems and data and to develop joint governance for enterprise architecture and data quality. The paper describes development approach and framework components including metamodel, repository, data quality, tools and governance. it may stand as a proposal for a TOGAF data quality extension.

Insurance industry undergoes major regulatory changes regarding risk management like Solvency II which require managing data quality. This paper reports an experience feedback about the development of an enterprise architecture and data quality framework suitable for Insurance Industry and COTS 1 environments. The framework, inspired by TOGAF 9.1, is tailored to provide systemic views of enterprise organization, systems and data and to develop joint governance for enterprise architecture and data quality. The paper describes development approach and framework components including metamodel, repository, data quality, tools and governance. it may stand as a proposal for a TOGAF data quality extension.

The rise of digital globalization has transformed human civilization into an interconnected network, where information, culture, and commerce transcend national boundaries. This book explores the philosophical foundations, ethical dilemmas, and socio-economic impact of this digital era, asking:
• Has digital globalization truly democratized knowledge, or has it deepened inequality?
• Is digital interconnectedness strengthening cultural exchange or erasing local identities?
• What does it mean to be a "global citizen" in a world where algorithms control narratives?
By bridging philosophy, economics, technology, and sociology, this book challenges readers to rethink the meaning of sovereignty, freedom, and identity in a digitally borderless world.
For centuries, globalization has been driven by trade, exploration, and politics. However, the digital revolution has accelerated this process at an unprecedented rate—making geographical barriers nearly irrelevant. This book examines:
• The philosophical shift from physical globalization to digital globalization.
• The role of AI, big data, and social media in shaping global interactions.
• The ethical concerns of surveillance, privacy, and digital colonialism.
• How digital platforms have created a new form of economic imperialism.
Through a critical analysis of technological advancements and philosophical theories, this book presents a holistic perspective on how digital globalization is shaping human history.
The emergence of the internet, social media, and cloud computing has led to an era of hyper-connectivity, where businesses, governments, and individuals operate beyond national constraints. However, this digital expansion has also raised profound philosophical questions about:
• Sovereignty – Do nations still have full control over their economies and cultures in a digital-first world?
• Freedom – Is digital connectivity a tool for liberation or a means of surveillance and control?
• Identity – How do local traditions and cultural uniqueness survive in a homogenized digital space?
This book explores how the digital transformation has shifted power dynamics from governments to tech corporations and algorithms, questioning whether human agency still exists in a world dictated by data.
The rapid advancement of digital globalization has led to several major shifts in human interaction, economy, and governance:
1. The Platform Economy – Companies like Amazon, Google, and Alibaba have eliminated traditional borders in commerce, leading to economic dependency on digital giants.
2. Cultural Hybridization vs. Cultural Erasure – While digital globalization promotes cross-cultural exchange, it also threatens local traditions and languages.
3. The Digital Divide – Access to technology is not equal, creating a new form of global inequality between connected and disconnected societies.
4. Big Data & Algorithmic Governance – AI-driven systems now influence elections, public policies, and social behavior.
5. Cybersecurity and Digital Sovereignty – Nations struggle to protect their citizens from digital threats while balancing openness and regulation.
These phenomena highlight both the promises and dangers of an increasingly digitalized world.
This book introduces five groundbreaking concepts that reshape our understanding of digital globalization:
1. The Paradox of Digital Freedom – How digital connectivity can both empower individuals and subjugate them to corporate and state control.
2. The Algorithmic Hierarchy – The emergence of a new class system based on data ownership and digital literacy.
3. The Illusion of Borderless Knowledge – While information is accessible globally, gatekeeping by tech giants and governments still dictates what we see.
4. The New Digital Colonization – How developed nations dominate the digital economy, forcing developing countries into technological dependency.
5. The Ethics of Virtual Citizenship – Can a person be a "digital citizen" of multiple nations, and what rights come with it?
By introducing these unique theories, this book establishes a new foundation for understanding digital globalization.

Key Indicators
The book identifies five measurable indicators of digital globalization’s impact:
1. The Concentration of Digital Power – How a handful of corporations control most of the global digital infrastructure.
2. The Spread of Digital Misinformation – Analyzing the role of fake news, deepfakes, and algorithmic bias in shaping perceptions.
3. The Growth of the Gig Economy – Measuring how digital globalization is reshaping labor markets and job security.
4. The Expansion of E-Governance – Examining how governments adapt to digital policies and surveillance practices.
5. The Decline of Privacy – Evaluating how personal data is commodified in the global marketplace.
For Scholars & Academics
• Provides a theoretical framework for digital globalization.
• Introduces a cross-disciplinary approach combining philosophy, economics, and technology.
For Policymakers & Governments
• Offers insights into the risks and benefits of a borderless digital world.
• Proposes ethical guidelines for digital governance.
For Entrepreneurs & Business Leaders
• Helps companies navigate global digital economies and ethical AI practices.
• Explores the future of digital trade and economic sovereignty.
For the General Public
• Encourages individuals to critically assess their digital identity and rights.
• Provides strategies for digital literacy and ethical online engagement.
Table of Contents 
Chapter 1: The Foundations of Digital Globalization (Page 1)
• Defining Digital Globalization (Page 5)
• The Shift from Physical to Digital Global Economy (Page 30)
Chapter 2: The Role of Technology in a Borderless World (Page 55)
• The Internet as a New Global Superpower (Page 60)
• The Rise of Social Media Empires (Page 90)
Chapter 3: The Political and Economic Impact of Digital Expansion (Page 125)
• The End of Economic Nationalism? (Page 130)
• Global Trade in the Era of Cryptocurrencies (Page 170)
Chapter 4: Philosophical Dilemmas of a Hyper-Connected World (Page 215)
• Individual Autonomy vs. Algorithmic Influence (Page 220)
• Digital Democracy: Reality or Illusion? (Page 250)
Chapter 5: Cultural Transformation in the Digital Age (Page 295)
• The Death of Local Culture? (Page 300)
• The Reinvention of Identity in Digital Spaces (Page 340)
Chapter 6: Ethical Concerns of a Digital-First Society (Page 400)
• Surveillance vs. Freedom (Page 405)
• Data Ownership and The Right to Digital Privacy (Page 460)
Chapter 7: The Future of Globalization in the Digital Era (Page 505)
• Can Digital Sovereignty Exist? (Page 510)
• Navigating a Post-Digital World (Page 570)
Prof. Dr. Yoesoep Edhie Rachmad, Ph.D., DBA, is a distinguished academic and researcher with over 293,000 citations, an h-index of 436, and. an i10-index of 639. He holds a Doctor of Philosophy in Humanity (2016), a Ph.D. in Information Technology, and a DBA in Management Technology. With a strong background in knowledge development, he has contributed to numerous theoretical advancements. Prof. Yoesoep serves as a Research Professor at the United Nations Global Compact (id-137635), United Nations Global Market (id-709131), and United Nations ECOSOC (id-677556). He founded the Education Training Centre in 1999 and specializes in blockchain, digital health, and healthcare transformation. Recognized as one of the Top 5 Most Influential Scientists in the World, he received the Global Influential Scientist Award in both 2023 and 2024 for his groundbreaking interdisciplinary research, significantly advancing fields such as technology, mental health, tourism, and entrepreneurship.
Prof. Dr. Yoesoep Edhie Rachmad was rigorously selected by the Protocol of the United Nations Academic Professor Council, with internationally published proceedings including: Ethical Considerations in Management 5.0: Balancing Technological Advancements with Human Values Globally (Mar 2022), The Role of Artificial Intelligence and Human Collaboration in Management 5.0: A Global Perspective (Feb 2022), Navigating the Complexities of Management 5.0: Challenges and Opportunities for International Organizations (Jan 2022), Revolutionizing Global Business: The Impact of Management 5.0 on International Corporate Strategies (Apr 2022), Life in the New Normal: The Role of Society 6.0 Management in Leading Global Recovery and Sustainability Post-COVID-19 (2021), The World Paused: Analyzing the Psychosocial Impact of the COVID-19 Pandemic on Global Safety and Security (2020), Transforming Workforce Skills in the Era of Society 5.0: Integrating Technology and Artificial Intelligence in Competency Development (2019), Emotional Understanding and Leadership in Start-Up Unicorns: A Study on the Role of Empathy and Emotional Intelligence Among Young Entrepreneurs (2018), and Consumer Behavior Psychology in the Era of Marketing 6.0: Analyzing the Role of AI and Machine Learning in Influencing Consumer Choices and Loyalty (2017).
As of 2024, Prof. Dr. Yoesoep Edhie Rachmad, Ph.D., DBA, has received numerous awards in the field of tourism, recognizing his contributions to sustainable, inclusive, and culturally and technologically advanced tourism. These accolades acknowledge his efforts in raising global awareness about the importance of preserving natural and cultural heritage. He has led innovations in tourism through cultural preservation, ecotourism promotion, the implementation of digital technologies, and the development of community-based tourism to create positive social and environmental impacts. These awards include the Global Tourism Visionary Award from the United Nations World Tourism Organization (UNWTO), Excellence in Sustainable Tourism Development Award from the Global Sustainable Tourism Council (GSTC), Outstanding Contribution to Cultural Tourism Award from UNESCO, Pioneer in Ecotourism Award from the International Ecotourism Society (TIES), Le...

AI-driven Master Data Management (MDM) frameworks have emerged as a cornerstone for ensuring data integrity, real-time synchronization, and enhanced business value. By leveraging machine learning algorithms and advanced analytics, these frameworks streamline data governance, reduce inconsistencies, and enable actionable insights. This paper explores the role of AI in transforming traditional MDM systems, reviews existing literature, and identifies the benefits, challenges, and future prospects of these frameworks.

This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles-fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.

The Jabodetabek region of Indonesia has had notable growth in the Information and Communication Technology (ICT) sector, primarily due to the efforts of Small and Medium-sized Enterprises (SMEs). SMEs encounter a complex network of legal obstacles that impede their growth and ability to compete. Using a mixed-methods approach that includes both quantitative surveys and qualitative interviews, this study explores these issues. The findings draw attention to important legal difficulties, such as limited access to legal services, contract conflicts, cybersecurity concerns, intellectual property issues, and regulatory compliance. The competitiveness and development of SMEs are significantly impacted by these issues. Comprehensive legal and legislative changes are advised to address these problems. These include simplified rules, strict enforcement of intellectual property rights, effective dispute resolution procedures, cybersecurity assistance, and improved access to legal services. This study highlights the significance of a supportive legal framework for innovation and economic growth, adding to our understanding of the legal aspects of ICT SME development in Jabodetabek.

Ключові слова: електронна демократія (участь), електронне урядування, цифрова економіка, місцеве самоврядування, урядування (управління) на основі даних Наосновізастосуванняміждисциплінарногопідходуобґрунтованодоцільністьнауковоготазаконодавчогозабезпеченнястимулюванняе-розвиткугромадтауправлінняїхресурсаминаосновіданихтаIKT. Устаттіобґрунтованодоцільністьзапровадженнявнауковийобігтазапропонованоавторськевизначення поняттяе-розвиткугромадувузькомуташирокомузначеннях;визначенойогоскладові.Метоютакого розвиткуєутвердженнядецентралізаціїтазабезпеченнябалансусоціальних,економічнихіекологічних показниківрозвиткугромадвУкраїні.Категорія«е-розвиток»єсистемоутворюючоющодонаявнихконцепцій,методологій,цифровихтанаступногопоколінняможливостейвикористанняданихвусіхсферах життєдіяльностігромадзметоювизначеннятадосягненняключовихцілейїхрозвитку.Визначеночинники,якігальмуютьупровадженняпринципуурядуваннянаосновіданихтапровіднихпрактикуправління даними на рівні місцевого самоврядування. Окреслено перспективні напрями впровадження принципу урядуваннянаосновіданихтаочікуванірезультативідзростанняефективностіоцінкитааналізутакихданих.Уконтекстізастосуванняпринципуурядуваннянаосновіданихрозкритозначення,завданнямоніторингутаоцінкие-розвиткугромад.Середтакихзавданьвиокремленорозробкузаконутапідзаконнихактів; впровадження необхідних організаційно-адміністративних механізмів; проведення роз'яснювальної роботи,популяризаціяданихіїхсоціальноїзначущостідлявирішенняпитаньмісцевогозначеннятаін. Такожустаттіприділеноувагупитаннямкадровогозабезпеченняпроцесууправліннянаосновіданих,а саме:підкресленоключовурользастосуванняпрофесійногопідходудоуправлінняданими.Запропонова-нозапровадженняроліCDOякнарівніцентралізованоїплатформи,такінарівніключовихрозпорядників даних-органівмісцевогосамоврядування.

Natural Language Processing (NLP) is revolutionizing IT documentation and knowledge management by enabling automation in documentation generation, enhancing information retrieval, and improving knowledge structuring. Traditional IT documentation practices often face challenges such as unstructured content, redundancy, and difficulty in retrieving relevant technical information. NLP addresses these inefficiencies by leveraging advanced techniques like Named Entity Recognition (NER), Topic Modeling, and Transformer-based models to improve the accuracy and accessibility of IT knowledge repositories. This paper explores the applications of NLP in IT documentation, evaluates its impact on knowledge management, and discusses the challenges associated with its implementation, such as domain adaptation and integration with existing IT systems. A literature review provides an in-depth analysis of state-of-theart NLP techniques, while case studies and empirical data illustrate the effectiveness of NLP-driven IT knowledge management systems. Furthermore, this paper highlights future research directions, including the fine-tuning of large language models (LLMs) for IT documentation and the integration of multi-modal AI approaches for enhanced contextual understanding and automation.

In the banking and finance sector, the integration of cloud computing and artificial intelligence (AI) technologies
within data warehousing solutions is revolutionizing data management, processing, and security. This
convergence is essential not only for handling complex datasets but also for meeting the growing demands for
scalability and enhanced security—both critical to modern financial systems. This article examines how cloud-AI
fusion addresses unique challenges in banking data warehousing, focusing on strategies to ensure scalability and
secure sensitive financial data. By exploring case studies and best practices, the article demonstrates how the
integration of cloud and AI transforms banking data warehousing to meet regulatory standards and accommodate
increased demand.

The financial services industry is undergoing a significant transformation driven by the integration of Artificial
Intelligence (AI) and advanced data warehousing techniques. This paper examines the impact of AI in advancing
data governance and enhancing regulatory compliance in financial services. We explore how AI addresses
persistent challenges such as data quality, privacy protection, and real-time regulatory compliance. The paper
also presents a detailed framework for implementing AI-powered data governance systems and highlights several
use cases demonstrating the advantages of AI in managing compliance, risk, and operational efficiency. Our
findings emphasize that AI-enhanced data warehouses can significantly improve regulatory adherence,
operational workflows, and risk management strategies in the financial services sector.

Presenting a background on diffusion of innovation in the AEC industry and an expanded discussion on factors hindering and motivating innovation highlighting the clash between fragmentation and integration.  Correlating a revised categorization of 4.0 concepts and technologies discussed in previous chapters in terms of their corresponding stage of diffusion across all project phases.  Discussing how technological advancements revealed opportunities for novel and more collaborative project delivery methods such as IPD (Integrated Project Delivery), and how this has the potential to speed up integral 4.0 innovations in a reciprocal feedback manner.  Introducing a broad vision on revising the design and construction curricula to allow for more cross-disciplinary information flow from disciplines that are traditionally left out from training and practice.  Suggest future directions both in industry and academia for introducing interdisciplinary research methods which will allow engineers to take the leadership back in developing their own design and engineering tools (from software engineers) that will allow them to lead the AEC in the information age.

Medical Device manufacturers have been interested in artificial intelligence (AI). However, there is a constant need to evaluate its use and performance due to system complexity, the variety of their architecture, as well as ethical and legal problems. This study offers a narrative commentary on the past, present, and future applications of machine learning (ML) algorithms and artificial neural networks (ANN) in medical devices. Finding challenges and issues with AI integration in medical devices was one of the study's main research goals. From clinical engineering to medical applications, artificial intelligence is transforming healthcare. Prior to realizing the full potential, though, ethical, legal, and social issues must be addressed. Its application must also be scrutinized and regulated in terms of fair access, privacy, suitable uses and users, liability, bias, and inclusivity. The goal of this study is to comprehend technology's accessibility, recognize artificial intelligence's enormous potential in the healthcare industry, and keep tabs on recent scientific advancements to motivate fellow researchers. Up until now, privacy and security, trust, bias, and accountability and accountability issues have dominated ethical discourses on artificial intelligence and health. As the technology's scope continues to grow, more issues will surely surface. Artificial Intelligence is relatively new when it comes to medical devices. Manufacturers of medical devices are predicted to abandon their conventional business models until 2030 in Favor of new digital artificial intelligence techniques. It is necessary to create a regulatory framework before introducing AI-based MDs to the market. The process of defining AI regulations and policies pertaining to MDs is still in its early stages, according to prominent regulatory bodies globally. In order to facilitate the adoption of regulatory frameworks and standardize the market, international standards pertaining to AI in MDs are required. Organizations like IEEE, ISO, and IEC are working to standardize data quality management and the use of AI in ways that impact human welfare. Even with acknowledged barriers, it is possible to draw the conclusion that AI has already fundamentally altered the way traditional medicine is practiced, greatly raised the caliber of medical care, and ensured universal health. It remains to be seen how the human population will be affected by the potential for future development of medical AI in addressing issues like chronic illnesses, infectious pandemics, and the aging population.

Mobile workflow optimization has emerged as a critical factor in modern business operations, transforming how organizations manage and execute their processes across distributed environments. This comprehensive article explores the fundamental components, implementation strategies, and future directions of mobile workflow optimization. The article examines cloud integration, artificial intelligence implementation, security frameworks, and technical considerations that drive successful mobile workflow deployments. Through detailed analysis of real-world implementations, the research demonstrates how organizations improve operational efficiency, user satisfaction, and resource utilization while maintaining robust security measures. The investigation encompasses development methodologies, monitoring strategies, and emerging technologies reshaping the mobile workflow landscape, providing insights into best practices and future considerations for organizations pursuing the mobile-first operational strategy.

The healthcare industry is transforming towards holistic health solutions, focusing on integrating physical, behavioral, and social determinants of health. This white paper explores the role of artificial intelligence (AI) in driving community health and its capacity to create a more comprehensive and equitable healthcare system. AI offers powerful tools for synthesizing data, generating insights, and personalizing care, thus enabling a multidimensional approach that considers the complete health of individuals and communities. Two primary challenges impede the progress of AI-driven healthcare: the fragmentation of health data and the lack of equitable access to digital health tools. Empowering communities with AI-driven solutions can bridge gaps in healthcare delivery, reduce disparities, and improve overall health outcomes. This paper will discuss the current limitations in the healthcare system, examine how AI is being leveraged to address these challenges, and present examples of successful AI implementations in community health.

In today's data-driven world, organizations rely on multiple platforms for data collection, storage, processing, and reporting. However, these platforms often generate inconsistent reports, leading to data quality issues, misaligned metrics, and challenges in decision-making. Standardizing data reporting across multiple platforms is essential for ensuring consistency, accuracy, and reliability of data, which is crucial for informed decision-making, regulatory compliance, and operational efficiency. This white paper explores the importance of standardizing data reporting, highlights the key challenges organizations face, and presents effective methodologies for implementing reporting standardization. By defining uniform reporting formats, establishing a data governance framework, and leveraging automation tools, organizations can streamline data reporting, improve data quality, and enhance the integration of data across different systems. Ultimately, standardizing data reporting fosters greater organizational alignment, improves strategic decision-making, and ensures compliance with industry regulations.

Ensuring continuous compliance in financial data pipelines is a critical challenge for financial institutions, as they navigate increasingly complex regulatory landscapes. The growing volume and complexity of data make it difficult to maintain compliance through traditional, manual auditing processes. This white paper explores the role of automation and real-time auditing in managing compliance within financial data pipelines. By leveraging automated compliance checks, real-time monitoring, and advanced auditing tools, financial organizations can ensure continuous adherence to regulatory requirements, reduce the risk of non-compliance, and increase operational efficiency. The paper outlines key strategies, technologies, and best practices for automating compliance processes, integrating real-time auditing, and maintaining transparency and accountability in financial data operations. Through these advancements, financial institutions can streamline their compliance efforts, reduce manual intervention, and ensure data integrity and security while meeting evolving regulatory standards.

In the rapidly advancing financial technology (FinTech) sector, data pipelines are essential for powering real-time transactions, analytics, and regulatory reporting. However, building robust data pipelines that balance security, compliance, and performance is a complex challenge. This white paper explores the key components and best practices for developing secure, compliant, and high-performing data pipelines in the FinTech industry. It outlines the importance of data governance, encryption, automated compliance checks, and performance optimization, while also highlighting the need for scalability and flexibility in pipeline design. By focusing on these critical areas, organizations can build data pipelines that not only support fast-paced financial operations but also meet stringent regulatory requirements and protect sensitive customer data. The paper provides a framework for FinTech companies to create resilient data infrastructures that enable innovation, reduce risks, and ensure compliance in an increasingly regulated environment.

With a vision to become the 'premier law and technology research centre in Asia' and combine law, computer science, and digital humanities scholarship, the digital economy is one of four clusters of focus under the recently established Singapore Management University (SMU) Centre for Digital Law (CDL) at the Yong Pung How School of Law (YPHSL). To investigate digital economy-related issue areas in collaboration with the University of Lucerne's Trade Law 4.0 project, the forthcoming white paper 'Data Free Flow with Trust (DFFT): Cross-Border Cooperation and Digital Trade on the Edge' opens with a provocative statement: Globally, 'economic governance' is becoming synonymous with 'data governance'.

In high-stakes industries such as financial services and manufacturing, the integrity, accuracy, and timeliness of data are critical for operational success and risk management. Automated data pipeline monitoring and validation have become essential tools in maintaining data quality across these sectors. This white paper explores the importance of automating data pipeline monitoring and validation, focusing on the unique challenges and requirements of both financial and manufacturing industries. It discusses the technologies and methodologies employed to ensure data accuracy, compliance, and security while mitigating risks. By leveraging tools such as real-time monitoring, machine learning, and automated alerts, organizations can enhance operational efficiency, reduce costs, and ensure compliance with regulatory standards. The paper highlights key solutions, benefits, and best practices for automating data pipeline monitoring, offering valuable insights for industry professionals seeking to optimize their data management systems in high-risk environments.

In the financial services sector, multi-platform reporting environments have become essential to manage and analyse data from diverse sources and systems. These environments enable organizations to leverage multiple tools and platforms for data integration, processing, and reporting. However, they also introduce challenges such as data inconsistencies, governance complexities, and compliance risks. This paper examines how financial institutions can implement effective data governance frameworks to address these challenges. By integrating advanced tools for data lineage, quality checks, and automated reporting, organizations can ensure consistency, transparency, and regulatory compliance across platforms. A case study illustrates the successful
implementation of data governance in a multi-platform ecosystem, highlighting measurable improvements in reporting accuracy, operational efficiency, and compliance adherence. The paper concludes with best practices and future trends in managing data governance for complex
reporting environments.

Blockchain technology has emerged as a powerful tool for enhancing data governance in financial pipelines by ensuring transparency, security, and data integrity. This paper examines how blockchain's decentralized and immutable ledger can address key challenges such as data breaches, compliance failures, and inefficient audit processes in the financial sector. It highlights blockchain's ability to enable real-time data monitoring, automated compliance checks through smart contracts, and secure transaction histories. By integrating blockchain into data governance frameworks, financial institutions can enhance regulatory compliance, reduce fraud, and improve operational efficiency, paving the way for a more secure and transparent financial ecosystem.