Designing Scalable Data Architectures for Integrated Public Service Analytics in Resource-Constrained Contexts: A Kenyan Case Study
TL;DR
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