This research offers a pragmatic blueprint for digital transformation in resource-constrained settings, contributing to the discourse on leveraging data architectures for improved public service delivery and evidence-based policymaking.
Abstract
Purpose: This study investigates the technical and governance challenges of integrating fragmented public service data systems (e.g., HRMIS, payroll, service delivery databases) into a unified analytics platform, using Kenya as a case study. Methodology: The research employs a qualitative case study methodology, analysing policy documents, technical reports, and comparative international practice, drawing on global best practices from Singapore's Smart Nation initiative and Estonia's X-Road. Findings: The study proposes a scalable, hybrid data architecture leveraging APIs, modular microservices, and cloud infrastructure, incorporating data privacy and security protocols compliant with Kenya's Data Protection Act (2019). A conceptual simulation estimates a potential 15–25% reduction in administrative service processing times achievable through such integration, contingent on a federated data governance model with a central coordinating body and clear data-sharing agreements. Originality/Value: This research offers a pragmatic blueprint for digital transformation in resource-constrained settings, contributing to the discourse on leveraging data architectures for improved public service delivery and evidence-based policymaking. Keywords: Data Architecture, Public Service Analytics, Interoperability, Data Governance, Kenya, Digital Transformation, Cloud Computing, API Integration, Resource-Constrained Settings, Data Fabric
The research uses API-driven digital platforms and cloud-native architectures to make government services more accessible and easier to integrate, illustrating how API-first and cloud-native ecosystems enable accessible, inclusive and sustainable public-sector innovation.
Sruthi Baddam· International Journal of Mod...· 0 citations
This systematic literature review presents a unified, cross-layer analysis of the factors affecting data quality in Internet of Things (IoT) ecosystems-spanning architectural, analytical, strategic, and brokerage components. Unlike previous studies that treat these dimensions separately, it provides an integrated perspective with a focus on the Publish–Subscribe (PS) communication model-a widely used yet underexplored IoT data exchange mechanism. From an initial 1,027 records, 367 studies are included after screening and eligibility assessment. Particular attention is given to identifying systemic weaknesses in PS brokerage architectures, especially concerning IoT data quality management. The review emphasises understanding IoT architecture and data for quality frameworks. It begins with a detailed analysis of IoT architecture, identifying potential bottlenecks affecting data quality across the perception, network, middleware, and application layers, followed by a structured evaluation of data quality assessment strategies-including statistical, probabilistic, neural network, and deep learning methods. It also assesses analytical approaches that encompass both real-time and historical data processing, applied to stationary and non-stationary IoT data. The review then advances to an in-depth examination of the PS paradigm, identifying systemic vulnerabilities in current centralised brokerage systems that hinder accurate data quality evaluation. To address these challenges, it explores blockchain integration, highlighting its potential to enhance data analytics and decentralisation in IoT data handling. Finally, it synthesises emerging efforts to develop advanced quality assessment models within brokerage environments, aimed at mitigating broker visibility and analytical limitations, as well as addressing data degradation and concept drift during dynamic data processing and verification.
Rabbia Idrees, Muhammad Bilal Amin· Cluster Computing· 0 citations
Current Research Information Systems (CRIS), such as Elsevier's Pure platform, provide centralized research records but are often underused for governance-driven analytics. This paper presents GARI (Governed, Analytics-Ready Institutional Research Information Infrastructure), a four-layer framework for transforming a commercial CRIS into an analyticsready institutional platform through source and identifier management, entity linkage, governance and quality gates, and reporting analytics. We demonstrate GARI through the University CRIS (MUREX Portal), a Pure-based production deployment covering 4,606 person records, including 4,325 current researchers, 62,091 research outputs, and 1,768 funding-related records (856 awards and 912 funding applications) as of 30 June 2026. Institutional extensions include data stewardship roles, scheduled synchronization, quality checks, downstream analytics, and dashboard reuse. A consolidated profile-readiness query combined affiliation and linked-output criteria with three identifier layers-ORCID iD, the NRIIS integration identifier, and Scopus Author ID-showing readiness of 651/4,325 (15.1%), 3,698/4,325 (85.5%), 3,750/4,325 (86.7%), and 3,683/4,325 (85.2%) for open interoperability, national-system synchronization, bibliometric analytics, and national-bibliometric linkage, respectively. Internal workflow mapping also indicates that administrative processing time for internal-grant intake, excluding the fixed committee-review period, decreased by approximately 70%. Knowledge-graph and retrieval-augmentedgeneration functions remain prototype-stage extensions. The case suggests that a commercial CRIS supports strategic analytics only after an institution layers governance, identifier discipline, and integration pipelines onto the vendor platform.
Wanaruk Chaimayo, Ratchuda Chaisutthanon, Pattaranan Hanma et al.· 2026 7th International Confe...· 0 citations
The case indicates that university digital transformation is strengthened when technological implementation is integrated with formal governance, systematic assessment, evidence-based planning, and institutional accountability.
Jennifer Célleri-Pacheco, Fernanda Tusa Jumbo, Oswaldo Chuquirima Camacho et al.· Future Internet· 0 citations
This article proposes and analyses a conceptual, policy-governed federated architecture derived from the architectural work of the Horizon Europe NOUS project and maps architectural functions onto major European reference initiatives.
P. Lamo, Diego Valdeolmillos, Marta Plaza Hernández et al.· Open Research Europe· 0 citations
This research sets out to investigate how Multi-Party Computation can be integrated into a data warehouse system to enable secure, privacy-preserving analyses and demonstrates that such a system can be viable, if it accounts well for performance issues of the technology.
Unknown authors· 0 citations
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