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.
Unless the challenges of collective analytics in e-governance, including those related to data visualization, data quality, capacity building, capacity building, technological capabilities, and inter-agency communications, are recognized, the implementation of collective analytics can be challenging.
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
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.
Srichandra Boosa· International Journal of AI,...· 0 citations
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, and reframes intelligent platforms as socio-technical governance systems.
Shengyu Gu· Frontiers in Public Manageme...· 0 citations