Designing Enterprise Data Architecture for AI-First Government and Higher Education Institutions
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I3P111Keywords:
Enterprise Data Architecture, Data Mesh, Data Lakehouse, Mlops, Llmops, Data Governance, Privacy-By-Design, Zero-Trust SecurityAbstract
The architecture of enterprise data at AI-first government and higher education organizations will have to balance AI ambitious agendas with data infrastructure that is stable and responsible. In this paper, I will suggest a layered reference architecture, which combines data mesh and lakehouse designs with a cloud-native platform based on the ISO/IEC 25012, DAMA-DMBOK, and the NIST AI Risk Management Framework. Methodologically, the work follows a design-science approach, synthesizing recent industry and policy literature, deriving a multi-stakeholder requirements matrix, and mapping representative public-sector and higher-education use cases such as fraud and risk detection, student success analytics, and smart-campus operations onto the proposed design. It is an ingestion and integration architecture, a governance and security architecture, metadata and semantic modeling, an analytical data store warehouse, lakehouse, vector stores, and a common AI/ML platform including MLOps and LLMOps, which consumes value through APIs, dashboards, and apps with AI infused in them. According to illustrative evaluation founded on 2023-2024 benchmark ranges, an AI-first EDA is capable of achieving: Reducing tens of seconds to low tens of seconds of analytical query latency, improving data completeness and timeliness to the mid-90s range, and risk and fraud detection model AUC to 0.84-0.88 plus higher concurrency and lowering the cost of infrastructure by 20-30%. Meanwhile, the paper identifies the most pertinent risks related to privacy, ethical AI, security risks, and skill gaps, asserting that the zero-trust securities, as well as long-term change management are the complements to the technical modernization. The blueprint suggested is therefore a workable, governance-consistent way of governments and universities shifting out of their secluded AI experiments into credible, systemic AI-empowered business processes
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