The paper explores a variety of data analytics methods—such as Benford’s Law for detecting manipulation, Markov Switching Models for economic cycle analysis, time-series anomaly detection for data integrity, and volatility analysis for stability—to assess the quality of economic data. It highlights the use of advanced techniques like Bayesian inference and resampling (e.g., bootstrap methods) alongside cross-source comparisons—such as GDP validation with satellite data, inflation checks with online pricing, and employment trends with job posting data—to identify discrepancies and enhance reliability. Thematic analysis reveals an evolving landscape of economic data assessment, progressing from probabilistic approaches to resampling techniques, with interdisciplinary integration of data analytics, econometrics, and business policy to address data quality challenges like noise, biases, and structural shifts. While these methods improve economic forecasting and policy decisions, limitations persist, including sensitivity to model assumptions, regional biases in alternative data sources, and challenges in capturing informal economies, prompting future research into advanced machine learning and hybrid validation models.