<![CDATA[This study aims to examine the effect of data engineering maturity levels and analytics workflow automation on operational prediction accuracy through the mediating role of data quality in warehouse logistics in Tangerang. A quantitative research approach was employed using data collected from 75 respondents involved in warehouse operations. The data were gathered through a structured questionnaire based on a Likert scale and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that data engineering maturity levels have a positive and significant effect on data quality, and analytics workflow automation also significantly influences data quality. Furthermore, data quality has the strongest positive effect on operational prediction accuracy. Direct effects show that data engineering maturity and analytics workflow automation also significantly influence prediction accuracy, although their effects are weaker compared to the indirect effects through data quality. Mediation analysis confirms that data quality partially mediates these relationships. These findings highlight that improving operational prediction accuracy in warehouse logistics is not solely dependent on advanced analytical tools but is strongly influenced by the quality of data generated through mature data engineering practices and automated analytics workflows. This study contributes to the literature by integrating technological capability and data quality perspectives and provides practical implications for logistics companies in enhancing data-driven decision-making and operational efficiency.]]>
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