Stanford Database Library of American Journal of Applied Science and Technology

Vol. 6 No. 04 (2026)
Articles

Harnessing Integrated Reporting Platforms and Dynamic UI Components for Timely Managerial Decisions

PDF
  • Dr. Amina Hassan
Dr. Amina Hassan
Department of Public Health, University of Nairobi, Kenya

Published 2026-04-08

Keywords

  • Integrated Reporting Systems,
  • Dynamic User Interfaces,
  • Real-Time Analytics,
  • Managerial Decision-Making

How to Cite

Dr. Amina Hassan. (2026). Harnessing Integrated Reporting Platforms and Dynamic UI Components for Timely Managerial Decisions. Stanford Database Library of American Journal of Applied Science and Technology, 6(04), 16–19. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/1347

Copyright (c) 2026 Dr. Amina Hassan

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The increasing reliance on data-driven decision-making in modern organizations has intensified the need for integrated reporting platforms and dynamic user interface (UI) components capable of delivering timely and actionable insights. This study examines the role of integrated reporting systems combined with adaptive UI technologies in enabling efficient managerial decision-making. The research is grounded in the intersection of business intelligence, machine learning, and human-computer interaction, emphasizing the transformation of raw data into strategic knowledge.

Integrated reporting platforms consolidate data from heterogeneous sources, providing a unified analytical environment for decision-makers. Dynamic UI components enhance this functionality by enabling interactive data exploration, real-time updates, and user-centric customization. This paper critically evaluates these systems through theoretical perspectives and empirical insights derived from studies on financial prediction, healthcare analytics, and enterprise dashboards. The work of Gondi et al. (2026) is particularly central, illustrating how dashboard-driven reporting systems facilitate real-time managerial insights and operational efficiency.

The study further explores the application of machine learning models in predictive analytics, drawing upon research by Dhokane and Sharma (2022), Hiransha (2018), and Zhang et al. (2019). These approaches demonstrate the capability of integrated systems to support forecasting and decision optimization. Additionally, insights from public health and mobile application studies (Institute for Public Health, 2019; Chandrashekar, 2018) highlight the importance of data accessibility and user engagement in effective decision-making.

The findings indicate that organizations leveraging integrated reporting platforms with dynamic UI components achieve improved decision speed, enhanced data comprehension, and greater strategic alignment. However, challenges related to system complexity, data quality, and user adaptability remain significant. The paper concludes by proposing a conceptual framework for optimizing these systems and identifying future research directions in intelligent decision-support technologies.

PDF

References

  1. Dhokane, R. M., Sharma, O. P. “A Comprehensive Review of Machine Learning for Financial Market Prediction Methods.” IEEE, ( 2022 ).
  2. Gondi, Sravanthi, Pankaj Arora and Pavan Kumar Rajagopal PrakashKumar. "Utilizing Peoplesoft Kibana and Fluid Dashboards for Real-Time Decision Making." Advances in Consumer Research 3, no. 3 (2026): 657-671.
  3. Hiransha, M., “NSE stock market prediction using deep-learning models.” Procedia Computer Science, 132 ( 2018 ): 1351–1362.
  4. Institute for Public Health, “National Health and Morbidity Survey (NHMS) 2019: Non-communicable diseases, Healthcare demand, and Health literacy—Key Findings,” iptk.moh.gov.my, 2019. https://iptk.moh.gov.my/images/technical_report/2020/4_Infographic_Booklet_NHMS_2019_-_English.pdf
  5. Khan, ZH. “Price prediction of share market using artificial neural network.” International Journal of Computer Applications (IJCA), ( 2011 ).
  6. P. Chandrashekar, “Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps,” mHealth, vol. 4, no. 6, pp. 6–6, Mar. 2018, doi: https://doi.org/10.21037/mhealth.2018.03.02
  7. Raju, S. S., Srikanth, M., Guravaiah, K., Pandiyaan, P., Teja, B., Tarun, K. S. “A Three-Dimensional Approach for Stock Prediction Using AI/ML Algorithms: A Review Comparison.” IEEE, ( 2022 ).
  8. Sisodia, P. S., Gupta, A., Kumar, Y., Ameta, G. K. “Stock Market Analysis and Prediction for Nifty50 Using LSTM Deep Learning Approach.” Proceedings of the 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), vol. 2, pp. 156–161, February 2022.
  9. World Health Organization, “Transforming Mental Health for All,” Jun. 2022. Available: https://iris.who.int/bitstream/handle/10665/356119/9789240049338-figeng.pdf?sequence=1
  10. Zhang, K., Zhong, G., Dong, J., Wang, S., Wang, Y. “Stock Market Prediction Based on Generative Adversarial Network.” Procedia Computer Science, 147, 400–406, ( 2019 ).