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All Journal BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Journal of Artificial Intelligence and Digital Business Innovative: Journal Of Social Science Research West Science Interdisciplinary Studies West Science Interdisciplinary Studies The Eastasouth Journal of Information System and Computer Science Innotech Jurnal Sistem Informasi Komputer
Wardhani, Diky
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Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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The Influence of Strategic Planning and Analytical Maturity on Organizational Performance in Implementing Business Intelligence in Indonesia Mardiani, Eri; Arisanti, Ivon; Wardhani, Diky; Diwyarthi, Ni Desak Made Santi
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.180

Abstract

This research delves into the dynamics of intricate Business Intelligence (BI) deployments in Indonesian startups, emphasizing the interplay of organizational performance, analytical maturity, and strategic planning. Structural Equation Modeling with Partial Least Squares (SEM-PLS) was utilized in the study, which had 187 individuals from several industries, to examine the linkages and extract significant findings. The results showed three strong positive relationships: organizational performance is positively impacted by strategic planning, analytic maturity has a considerable impact on organizational performance, and strategic planning influences analytic maturity. Large impact sizes and statistical robustness characterize these practically meaningful associations. This study adds to our understanding of BI adoption in the particular setting of startups by highlighting the crucial roles that mature analytics and strategic planning play in fostering organizational success.
The Impact of Data Engineering Maturity and Analytics Pipeline Automation on Operational Prediction Accuracy through Data Quality in Warehousing Logistics in Tangerang Wardhani, Diky; Bunyamin, Ilham Akbar; Andiani, Paramita
West Science Interdisciplinary Studies Vol. 4 No. 04 (2026): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v4i04.2787

Abstract

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.
The Impact of Data Engineering Maturity and Analytics Pipeline Automation on Operational Prediction Accuracy through Data Quality in Warehousing Logistics in Tangerang Wardhani, Diky; Bunyamin, Ilham Akbar; Andiani, Paramita
West Science Interdisciplinary Studies Vol. 4 No. 04 (2026): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v4i04.2787

Abstract

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.
Co-Authors Akhmad Pandhu Wijaya, Akhmad Pandhu Andiani, Paramita Arisanti, Ivon Arnes Yuli Vandika Bunyamin, Ilham Akbar Chandrasekar, Dito Fahreza, Muhammad Reza Faiz Muqorrir Kaaffah Judijanto, Loso Mardiani, Eri Nevandri, Rifqi Nadhir Prialam , Abdi Rika Astuti Safarudin, Muhamad Sigid Santi Diwyarthi, Ni Desak Made Saputra, Dedi Dwi Simarangkir, Manase Sahat H