AI-Powered Personal Data Analyst

Individuals create personal data through expenses, calendars, fitness applications, learning platforms, digital notes, and othereveryday services. Although these records can support better decisions, they are often scattered across applications and presentedwithout a unified explanation. This paper proposes an AI-powered personal data analyst that consolidates user-authorized data,organizes it into a consistent structure, and produces understandable summaries, trends, and recommendations. The proposedframework includes data ingestion, validation, normalization, privacy controls, analytical processing, optional machine-learningcomponents, and an interactive dashboard. Descriptive statistics and rule-based insights form the initial baseline; anomaly detectionand forecasting can be introduced only when sufficient, suitable data are available. The design emphasizes transparent explanations,user consent, data minimization, and a clear distinction between observed facts and model-generated estimates. The paper outlinesthe system architecture, workflow, implementation options, evaluation metrics, limitations, and future scope. It is a design proposaland does not claim measured performance or completed user testing. A practical prototype should be evaluated with consented data,representative test cases, and privacy and usability checks before wider deployment.