2020· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
The findings are that companies that implement organized governance systems record dramatic advancement in the quality of data, compliance rates, and the accuracy in analytics, and the need to incorporate governance frameworks in enterprise analytics strategies to promote sustainable data-driven change is highlighted.
Abstract
Before 2019, enterprises were becoming more and more dependent on analytics platforms to derive actionable insights out of large amounts of both structured and unstructured data. Nevertheless, lack of strong data governance systems tended to cause inconsistencies, compliance issues and reduced trust in analytical products. This article is an in-depth examination of data governance models designed to support enterprise analytics platforms, its architectural elements, implementation plans, and the effects it has on operations. The paper highlights the importance of governance frameworks that guarantee quality, integrity, accessibility, and security of data in distributed systems. The suggested framework blends policy management, metadata management, data stewardship, and compliance monitoring into a single governance framework. It also highlights how governance practices can be aligned with business goals, regulation needs, and technology. This study offers a literature review and methodology review to determine the main issues around data silos, non-standardization, and scale limitations in large organizations. A model of governance lifecycle is presented that includes stages of data acquisition, data validation, data storage, data processing and consumption. The framework utilizes rule-based validation, role-based access control, and audit trail, to promote transparency and accountability. Also, the paper identifies the importance of enterprise data catalogs and lineage tracking to enhance data discoverability and traceability. The findings are that companies that implement organized governance systems record dramatic advancement in the quality of data, compliance rates, and the accuracy in analytics. A comparative analysis indicates quantifiable improvements in operative efficiency and effectiveness in decision making. The paper wraps up by highlighting the need to incorporate governance frameworks in enterprise analytics strategies to promote sustainable data-driven change.
Industry 4.0 industrial environments rely on highly interconnected and data-intensive ecosystems in which effective data governance is critical to ensure data quality, security, interoperability, compliance, and controlled data sharing. Although several data governance reference frameworks are widely adopted in practice, they were primarily conceived for generic corporate contexts, and their suitability for industrial settings remains insufficiently assessed. This paper presents a coverage-oriented benchmarking of established data governance frameworks with respect to the specific challenges of industrial data governance. Eleven challenges have been identified in prior literature, operationalised into thirtythree measurable items and evaluated using a five-level Likert scale (0-4) based exclusively on authoritative primary framework documentation.. Item-level scores were aggregated into normalised challenge-level and overall coverage indices, enabling systematic comparison across frameworks. The analysis benchmarks four reference frameworks: DAMA-DMBOK, ISACA-COBIT 5, the Data Governance Institute (DGI) framework, and IBM's data governance framework. Results reveal substantial but non-uniform coverage across frameworks: DAMA-DMBOK achieves nearcomplete coverage, whereas IBM, DGI, and ISACA-COBIT 5 exhibit moderate and heterogeneous coverage, with recurrent gaps in privacy conditions, human-related governance risks, scalability across expanding industrial data ecosystems, and operational rules for cross-boundary data sharing and reuse. The proposed benchmarking approach provides a transparent and reproducible basis for framework selection and adaptation in Industry 4.0 contexts and highlights the need for integrative governance models tailored to industrial data ecosystems.
The demand for data-driven insights in government has highlighted the importance of collective analytics. This study attempts to explore the key challenges of collective analytics in the context of Indian e-governance and the framework for addressing them. The study is based on a literature review, references to two cases, and expert views obtained from professionals involved with analytics solutions in government. In this study, analytics projects are considered as dashboard-based analytics. Based on the content analysis of expert responses, 14 key challenges of collective analytics in e-governance have been identified. The novelty of the present study is the focused exploration of challenges and their framework related to collective analytics in e-governance-a topic that received limited attention in the extant literature. This study brings forth the fact that unless the challenges of collective analytics in e-governance, including those related to data visualization, data quality, capacity building, technological capabilities, and inter-agency communications, are recognized, the implementation of collective analytics can be challenging. This study provides the basic understanding needed for data-driven governance through collective analytics. The output of the study will be helpful to the managers, e-governance experts, academicians, planners, and policymakers to understand the dynamics of collective analytics in government for handling discussed challenges well in advance. This study will also helpful to reduce the cost and time of the collective analytics project for effective decision-making.
Ashutosh Prasad Maurya, Pradeep Kumar Suri· International Journal of Inf...· 0 citations
This paper presents MDI’s Data Governance Transformation (DGT) project, an already-in-use data governance policy & infrastructure framework developed to support robust implementation of Privacy Enhancing Technologies (PETs), govern rapid AI development, and confront precedent shattering data use in the United States. As government agencies increasingly rely on complex and distributed data ecosystems, traditional data management approaches have proven insufficient to ensure data quality, accessibility, privacy, and interoperability. This framework establishes a cross-functional data governance structure that includes clearly defined roles, a RACI (Responsible, Accountable, Consulted, Informed) matrix, a standardized change control process, and an architecture rooted in medallion-style data layering. It also includes practical guidance on the following: conducting a data inventory, aligning with cloud and privacy requirements, and coordinating with contractors to ensure data portability and reproducibility. By embedding PETs and clear accountability mechanisms, this framework not only supports compliance with regulatory mandates but also enables data-driven decision-making and responsible use of AI. The framework serves as a replicable model for other government and non-government entities seeking to implement or refresh their data governance strategies to meet the demands of modern public service delivery.
J. Pasner· International Journal of Pop...· 0 citations
Customer Relationship Management (CRM) systems have become the mainstay of customer-centric organizations, enabling them to collect, manage, and analyze customer information from multiple channels and touchpoints. As more companies are turning to data for their decision-making, high-quality, consistent, safe, and reliable customer data have not only become key factors that influence business outcomes and customer satisfaction, but also play a role in complying with regulations. In this regard, data governance and trust are crucial in ensuring that customer data remain correct, accessible, safe, and ethically handled throughout their lifecycle. This article explores how data governance structures and trust-enhancing practices are becoming part and parcel of CRM tools nowadays, most importantly concentrating on those methods that allow scalable handling of customer data even in rapidly changing business circumstances. The paper aims to identify primary governance measures, quantify their overall impact on data quality and organizational trust, and determine how today's businesses can maintain a balance between data openness and protection/privacy. A qualitative methodology supported by a comprehensive case study of a CRM system implementation has been the method of this work exploring governance policies, data stewardship frameworks, metadata handling procedures, access control mechanisms, and compliance tactics which are the main factors for successful customer data management. The findings indicate that organizations that articulate their governance frameworks clearly, standardize their data management processes, and publicly set up accountability lines, experience a substantial increase in the accuracy of data, operational efficiency, customer trust, and regulatory preparedness. Also, the study highlights the role of technologically advanced tools like AI, automation, and live monitoring in enhancing governance accuracy and customer data resources trust. By delivering strategic insights and scalable governance models, this paper supports the growing body of research on CRM data management and provides practical recommendations for business executives, data professionals, and technology partners to build dependable, strong, and future-proof CRM systems leading to continuous business success and enhanced customer relations.
Srichandra Boosa· International Journal of AI,...· 0 citations
This study aims to design an effective governance framework for the utilization of big data as a complement to official statistical data, while preventing new complexities in data management and utilization processes. The research employs a quantitative descriptive approach based on the COBIT 2019 framework, focusing on the governance objectives selected through the design factor process, namely EDM01 (Ensured Governance Framework Setting and Maintenance) and EDM05 (Ensured Stakeholder Engagement). The object of this research was the official statistics office in Balikpapan City, East Kalimantan, with the scope limited to governance policy design and excluding technical software or tool implementation. Data were collected through questionnaires based on the COBIT 2019 Process Assessment Model, supporting documents, and stakeholder discussions, then analyzed through design factor analysis, capability level assessment, and gap analysis. The results indicate that both EDM01 and EDM05 are currently at Capability Level 2 (Performed Process), with most activities classified as Partially Achieved and Largely Achieved, indicating that governance processes have been implemented but are not yet fully standardized and consistently managed. Based on these findings, the target state (To-Be) is set at Capability Level 3 (Defined). Recommendations include formulating Big Data governance policies, establishing governance structures, developing standard operating procedures, implementing periodic monitoring and evaluation, and strengthening stakeholder engagement and communication to support structured, integrated, and accountable big data governance.
Ridha Asih, F. Samopa· Journal of social research· 0 citations
This study proposes a unified governance framework for high-dimensional data-driven intelligent platforms by integrating insights from platform ecosystem theory, algorithmic governance, and data governance research. Rather than conceptualizing intelligent platforms as neutral technological tools, the paper positions them as decision infrastructures that structure authority, accountability, and regulation through data architectures and algorithmic control. Using tourism and healthcare platforms as comparative governance contexts, the analysis demonstrates that platforms perform equivalent institutional functions, including regulation, coordination, monitoring, accountability, and optimization, despite differences in service domains and data content. A central contribution of this research is the introduction of data efficiency as a governance performance criterion, emphasizing the capacity of platforms to transform complex, high-dimensional data into interpretable, actionable, and institutionally usable decisions. By shifting evaluation from predictive accuracy toward governance outcomes such as transparency, compliance, coordination, and trust, the study reframes intelligent platforms as socio-technical governance systems. The findings confirm that platform governance logic is institutional rather than sector-specific and that algorithms operate as operational governors within platform ecosystems. This framework advances management science by providing a transferable model for understanding how intelligent platforms govern through high-dimensional data in smart service environments.
Shengyu Gu· Frontiers in Public Manageme...· 0 citations