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This study discusses the increasing complexities and difficulties in SM, especially in effectively evaluating and controlling strategies. Therefore, the research proposes the development and use of an analytical tool that combines text analysis and advanced data analysis to streamline the evaluation process. The purpose of this investigation is to improve the tool’s analytical accuracy and process, which might make it a useful tool for SM across a range of industries. Methodologies adopted include both CRISP-DM and RAD. A set of steps have been implemented, starting from data collection, preprocess, pipeline to model building, evaluation and deployment. New York’s MMR data, which consists of both PDF performance reports and CSV KPIs data are used. Ensemble models are found to be highly effective as the models complement each other’s strengths and weaknesses. This study sets the foundation for future developments in SM tools by highlighting data-driven decision-making and its efficiency in the evaluation of organizational strategies.

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Multi-Modal Data Analytics Approach for Enhanced KPI Analysis and Performance Report Evaluation in Strategic Management

  • 会议论文

  • 发表日期：

    2025年

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  • HemaLatha KrishnaNair,
  • Shuh Jing Lim,
  • Vanishri Arun

摘要

This study discusses the increasing complexities and difficulties in SM, especially in effectively evaluating and controlling strategies. Therefore, the research proposes the development and use of an analytical tool that combines text analysis and advanced data analysis to streamline the evaluation process. The purpose of this investigation is to improve the tool’s analytical accuracy and process, which might make it a useful tool for SM across a range of industries. Methodologies adopted include both CRISP-DM and RAD. A set of steps have been implemented, starting from data collection, preprocess, pipeline to model building, evaluation and deployment. New York’s MMR data, which consists of both PDF performance reports and CSV KPIs data are used. Ensemble models are found to be highly effective as the models complement each other’s strengths and weaknesses. This study sets the foundation for future developments in SM tools by highlighting data-driven decision-making and its efficiency in the evaluation of organizational strategies.