Data governance in predictive toxicology: A review

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

As the world embraces and welcomes emerging technologies in managing businesses through big data initiatives and Internet of Things (IoT), one important aspect still remains and that is the data collated and analyzed. Data is no doubt an essential asset that has contributed to the success of this new approach, however, businesses and enterprises regardless of their size will need to ensure that their data is of high quality and reliable. Previous studies have indicated that huge investments are being made to collate and capture business data, however, the quality of data being captured and stored in many systems across the organization failed to address the issue of quality and reliability. There is a significant gap in the literature in ensuring that the business data are being properly managed and governed. With the introduction of data analytics embedded into the various facets of the organization’s operations, the data governance aspect becomes more critical. An interpretivist qualitative case study was conducted in one of Malaysia’s leading electricity utility company, which uses an Enterprise Resource Planning (ERP) solution to address the organization’s business needs in relation to operational data. In-depth interviews were conducted in obtaining insights on the issues of data management and governance. As a result, a comprehensive enterprise data governance framework for the success of ERP is being considered to serve as a guideline for utility companies implementing Enterprise Data Governance. Keywords: Data Governance, Determinants, Enterprise Resource Planning (ERP), Success, Utility

Towards model governance in predictive toxicology

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 their data quality capabilities while navigating integration challenges with existing architectures. The article reveals significant improvements in defect detection, incident reduction, and operational efficiency when automated solutions replace traditional manual methods, with most organizations achieving a return on investment within 8-14 months. Beyond technical benefits, the article highlights how quality automation catalyzes broader organizational changes in data governance, stakeholder engagement, and quality culture. As data volumes grow and business dependencies on high-quality data increase, the emerging technologies and trends identified in this research—including quality-ascode practices, federated management approaches, and self-healing systems—will likely shape the future landscape of data quality management, transforming it from a technical function to a strategic business capability.

This paper explores the importance of data governance in influencing ethical decision-making in the context of information technology (IT). Data governance is used as a management technique to ensure the quality of organisational data by establishing and implementing policies, procedures and standards. Three main types of data governance are discussed: silo-oriented data governance, functional data governance, and platform data governance. The impact of each type on ethical IT decision-making is analysed, highlighting the challenges and benefits associated with each approach. Ethical IT decision-making hinges on robust data governance. Silo-oriented governance fosters fragmentation, conflicting standards, and security vulnerabilities. Functional Data Governance tailors policies to departments, enhancing accountability and risk mitigation but may lack unified ethics. Platform Data Governance builds on this, ensuring clearer accountability, data privacy, and unified ethical standards. To foster ethical IT decision-making, prioritize Platform Data Governance, establish a data platform for security, cultivate a unified ethical culture, and enforce organizational standards. These steps promote accountability, mitigate risks, and safeguard data integrity, crucial for ethically sound IT decisions in today's complex landscape.

Purpose: The aim of the study was to assess the impact of data governance on data quality in healthcare institutions. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that institutions with robust data governance protocols experience fewer instances of data inconsistency and errors. This, in turn, improves the reliability of clinical decision-making processes and patient outcomes. Moreover, compliant data governance practices contribute to better data integration across various healthcare systems and departments, facilitating comprehensive patient care and longitudinal health monitoring. By promoting transparency and accountability in data management, healthcare institutions can mitigate risks associated with data breaches and ensure compliance with regulatory standards, thereby fostering trust among patients and stakeholders alike. Implications to Theory, Practice and Policy: Control theory, information theory and agency theory may be used to anchor future studies on assessing the impact of data governance on data quality in healthcare institutions. Healthcare institutions should prioritize the adoption of comprehensive data governance frameworks that integrate policy development, stakeholder engagement, and technological infrastructure. Policymakers should align regulatory frameworks with evolving data governance standards to promote interoperability and data exchange across healthcare networks.

Abstract- Transparency, regulatory compliance, and trust in financial ecosystems depend on the integrity of financial reporting pipelines. Financial reporting fraud remains a widespread problem with serious repercussions for markets and stakeholders. Ensuring data quality at every stage is crucial for operational efficiency and successful fraud detection, as businesses depend increasingly on automated data pipelines for real-time financial reporting. With an emphasis on fraud detection capabilities, this paper investigates the development and application of comprehensive data quality frameworks suited to financial reporting pipelines. Post-hoc audits and manual checks are frequently the mainstays of traditional fraud detection methods, which are inadequate for managing large volumes of financial data at high speeds. The credibility of analytics and regulatory reporting can also be weakened by missing, erroneous, and inconsistent data in reporting systems, which can conceal fraudulent activity or produce false positives. Therefore, a strong data quality framework must incorporate domain-specific integrity constraints, anomaly detection logic, lineage tracing mechanisms, and enforce standard data validation rules. In line with anti-fraud goals, this study suggests a structured framework for data quality that combines rule-based, statistical, and metadata-driven validation procedures. Five essential pillars—completeness, accuracy, consistency, timeliness, and integrity—are incorporated into the framework presented in this paper. To identify questionable anomalies and deviations early, each pillar is mapped to particular validation mechanisms, including cross-ledger balancing, reference reconciliation, threshold-based monitoring, schema enforcement, and duplication checks. By incorporating these dimensions into ETL pipelines, organizations can proactively evaluate and score the quality of incoming data and flag records that might point to fraudulent manipulation, such as manipulated ledger entries, underreported liabilities, or revenue misstatements. The framework is integrated into a modular architecture that guarantees practical applicability with contemporary cloud-native data platforms and legacy systems. It uses tools like SQL-based integrity rules to identify transaction irregularities, Apache Atlas to track lineage and transformations, and Apache NiFi to orchestrate validation workflows. Financial controllers and compliance officers are also given access to real-time metrics and dashboards to monitor fraud risk indicators and data quality scores. This study uses a synthetic financial dataset enhanced with known fraud scenarios to assess the efficacy of the suggested framework. The analysis shows that while low-quality data introduces a lot of noise and lowers model reliability, high data quality scores are strongly correlated with fewer false positives in fraud detection models. The study also demonstrates how incorporating data quality validation early in the pipeline lifecycle enhances reporting output trust and speeds up the identification of fraudulent trends. This framework aids in the creation of safe, audit-ready, and compliant financial reporting pipelines by coordinating data quality assurance with fraud detection objectives. Additionally, it fills a significant void in managing financial data, where data quality is frequently viewed as an operational issue rather than a security or compliance requirement. The research's conclusions apply to financial institutions, regulators, auditors, and tech companies who want to improve financial reporting systems' resistance to fraud. This paper argues for a paradigm change in which data quality frameworks are essential to financial reporting fraud detection rather than being merely incidental. Adopting such frameworks will be crucial to creating robust, transparent, and reliable financial ecosystems as the financial sector accelerates digital transformation. Future research could build on this work by incorporating machine learning methods into the data quality scoring process, enabling even more automation and accuracy in fraud detection workflows. Keywords: Data quality, fraud detection, financial reporting pipelines, ETL validation, financial integrity, data lineage, anomaly detection, audit compliance, data governance, metadata-driven validation.

As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.

Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework.

As enterprises undergo rapid digital transformation, the volume, velocity, and variety of data have grown exponentially, leading to unprecedented challenges in data management, security, and compliance. Traditional, manual data governance frameworks are no longer sufficient to handle the scale and complexity of modern data ecosystems, often resulting in data silos, poor data quality, and regulatory non-compliance. This paper proposes a comprehensive AI-Driven Unified Data Governance Framework designed for modern enterprise platforms. By integrating artificial intelligence (AI) and machine learning (ML) at the core of the data governance lifecycle, the proposed framework automates critical functions such as data discovery, classification, quality assurance, lineage tracking, and policy enforcement. We present a layered architectural model that seamlessly operates across hybrid and multi-cloud environments, supporting paradigms like data mesh and data fabric. Through four detailed enterprise case studies spanning financial services, healthcare, global retail, and smart manufacturing, we demonstrate the empirical benefits of AI-driven governance, including up to 72% reduction in compliance reporting time and significant improvements in data quality and operational efficiency. Furthermore, we provide a comparative analysis of leading enterprise governance platforms and introduce a six-level AI Governance Maturity Model. Finally, the paper explores future trends, including the integration of Large Language Models (LLMs), autonomous self-healing data pipelines, and federated learning, providing a strategic technology roadmap for the next decade of enterprise data governance.

Big Data technologies and approaches have an important impact on the organization and governance of the enterprise. With such a high volume of structured & unstructured data, real time and mutualization needs, it is quite complicated to keep a high quality of data by respecting the governance rules and best practices. In addition, new team roles and organization must be applied in order to adapt to the new Big Data decisional constraints. In this direction, we present in this paper an overview of DECIDE, a decisional Big Data Methodology. We focus, particularly, on its team workforce, data quality, storage and governance fundamentals, rules and steps.

Chapter 3 - Data quality, the essential “ingredient”

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

In an era where data has become a strategic organizational asset, the ability to convert high-quality data into measurable business value remains a critical challenge. This study examines how robust data governance frameworks drive analytics-led business performance by strengthening data quality and analytics capabilities. Using a quantitative research design, data were collected from organizations across multiple industries and analyzed through reliability testing, factor analysis, and structural equation modeling. The findings reveal that data governance significantly improves data quality and analytics capability, which in turn have a strong positive impact on business performance. The results also confirm the mediating role of analytics capability in transforming data quality into organizational value, and highlight the moderating effect of digital maturity on governance effectiveness. The study contributes to both theory and practice by providing empirical evidence of the integrated pathway from governance to quality, analytics, and performance. The findings offer practical insights for managers and policymakers to design data governance strategies that move beyond compliance toward sustainable competitive advantage and value creation.