Toward an Integrated Intelligent Data Governance Architecture for Decision-Centric Digital Systems
Keywords:
Intelligent Data Governance, Artificial Intelligence Governance, Decision Intelligence, Digital Ecosystems, Adaptive Governance Architecture.Abstract
The rapid advancement of artificial intelligence (AI), big data analytics, cloud computing, Internet of Things (IoT), and autonomous digital technologies has transformed modern digital ecosystems into highly interconnected and decision-centric environments. However, the widespread adoption of intelligent systems has also introduced significant governance challenges, including fragmented data governance, cybersecurity risks, interoperability limitations, privacy concerns, algorithmic bias, lack of transparency, and weak accountability mechanisms. Existing governance frameworks often operate independently across data governance, AI governance, cybersecurity, and decision-support systems, making them inadequate for managing dynamic and intelligent digital infrastructures. This study aims to propose an integrated intelligent data governance architecture that supports adaptive, secure, transparent, and decision-centric digital systems. This study employs a qualitative-conceptual methodology using Design Science Research (DSR), Systematic Literature Review (SLR), and framework development approaches. Secondary data were collected from scientific journals, conference proceedings, governance frameworks, industry reports, and international standards such as ISO, OECD AI Principles, NIST AI RMF, GDPR, COBIT, and DAMA-DMBOK. Data analysis was conducted using thematic analysis, comparative analysis, architectural analysis, and governance layer modeling. The findings reveal that intelligent digital ecosystems require integrated governance mechanisms combining data governance, AI governance, cybersecurity, explainable AI, interoperability, decision intelligence, and adaptive feedback systems. The proposed architecture consists of seven interconnected layers that collectively improve governance transparency, accountability, operational resilience, digital trust, and decision quality. The study concludes that integrated intelligent governance architectures are essential for supporting sustainable, secure, and trustworthy digital transformation in modern AI-driven environments.
Downloads
References
Abbadi, I. M., & Martin, A. (2011). Trust in the Cloud. Information Security Technical Report, 16(3–4), 108–114.
Akula, R., & Garibay, I. (2021). Audit and assurance of AI algorithms: a framework to ensure ethical algorithmic practices in artificial intelligence. ArXiv Preprint ArXiv:2107.14046.
Choudhary, R. R., Mamodiya, U., Srivastava, P., & Ahmad, S. (n.d.). Advancing Security in Industry 4.0. In Cognitive Security for Industrial IoT (pp. 33–49). CRC Press.
Faruq, M. O., & Mollah, M. H.-O.-R. (2021). POST-GDPR DIGITAL COMPLIANCE IN MULTINATIONAL ORGANIZATIONS: BRIDGING LEGAL OBLIGATIONS WITH CYBERSECURITY GOVERNANCE. American Journal of Scholarly Research and Innovation, 1(01), 27–60.
Hevner, A., Vom Brocke, J., & Maedche, A. (2019). Roles of Digital Innovation in Design Science Research: A. Hevner et al. Business & Information Systems Engineering, 61(1), 3–8.
Intezari, A., & Gressel, S. (2017). Information and reformation in KM systems: big data and strategic decision-making. Journal of Knowledge Management, 21(1), 71–91.
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493.
Janssen, M., & Kuk, G. (2006). A complex adaptive system perspective of enterprise architecture in electronic government. Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), 4, 71b-71b.
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976.
Leikas, J., Koivisto, R., & Gotcheva, N. (2019). Ethical framework for designing autonomous intelligent systems. Journal of Open Innovation: Technology, Market, and Complexity, 5(1), 18.
Liang, X. (2020). Ascend AI Processor Architecture and Programming: Principles and Applications of CANN. Elsevier.
McIntosh, B. S., Ascough II, J. C., Twery, M., Chew, J., Elmahdi, A., Haase, D., Harou, J. J., Hepting, D., Cuddy, S., & Jakeman, A. J. (2011). Environmental decision support systems (EDSS) development–challenges and best practices. Environmental Modelling & Software, 26(12), 1389–1402.
McMeekin, N., Wu, O., Germeni, E., & Briggs, A. (2020). How methodological frameworks are being developed: evidence from a scoping review. BMC Medical Research Methodology, 20(1), 173.
Mendonça, M. G., & Basili, V. R. (2002). Validation of an approach for improving existing measurement frameworks. IEEE Transactions on Software Engineering, 26(6), 484–499.
Muravev, M., Kuciuk, A., Maksimov, V., Ahmad, T., & Aakula, A. (2020). Blockchain’s role in enhancing transparency and security in digital transformation. J. Sci. Tech, 1(1), 865–904.
Parimi, S. K., & Yallavula, R. (2021). Data-Governed Autonomous Decisioning: AI Models for Real-Time Optimization of Enterprise Financial Journeys. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 88–100.
Rahman, M. M., & Ashfaq, S. (2021). Data-driven decision support in information systems: Strategic applications in enterprises. International Journal of Scientific Interdisciplinary Research, 2(2), 1–33.
Scholl, H. J., Kubicek, H., & Cimander, R. (2011). Interoperability, enterprise architectures, and IT governance in government. International Conference on Electronic Government, 345–354.
Tounsi, W. (2019). Cyber-Vigilance and digital trust: cyber security in the era of cloud computing and IoT. John Wiley & Sons.
Vagia, M., Transeth, A. A., & Fjerdingen, S. A. (2016). A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed? Applied Ergonomics, 53, 190–202.
van de Hoven, J., Comandé, G., Ruggieri, S., Domingo-Ferrer, J., Musiani, F., Giannotti, F., Pratesi, F., & Stauch, M. (2021). Towards a digital ecosystem of trust: Ethical, legal and societal implications. Opinio Juris In Comparatione, 1/2021, 131–156.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Bambang Saras Yulistiawan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
