Vol. 2 No. 2 (2023): The QUEST: Journal of Multidisciplinary Research and Development

Driving Business Success: Exploring the Implementation and Improvement of Big Data Analysis and Business Intelligence in Selected Companies in Zheijiang, China

Alexander Cochanco
Nueva Ecija University of Science and Technology
Zhao Zhenhuan
Hangzhou Ruinan Information Technology Co., Ltd.

Published 12/30/2023


  • Big Data Analysis,
  • Business Intelligence,
  • Challenges,
  • Enhancement Plan Development,
  • Implications

How to Cite

Cochanco, A., & Zhao, Z. (2023). Driving Business Success: Exploring the Implementation and Improvement of Big Data Analysis and Business Intelligence in Selected Companies in Zheijiang, China. The QUEST: Journal of Multidisciplinary Research and Development, 2(2). https://doi.org/10.60008/thequest.v2i2.107


The study investigates the application and enhancement of big data analysis and business intelligence in selected companies in Zheijiang province, focusing on aspects such as business profile, adoption levels, benefits, implications, and challenges. Limited to selected companies in Zheijiang, the research aims to propose a tailored enhancement plan based on survey data. While it doesn't compare with other organizations and relies on self-reported data, the study delves into specific aspects of the selected companies, potentially requiring customization for wider applicability. Conducted from June to October 2023, the research utilizes a descriptive-quantitative design, emphasizing systematic data collection. With Zhejiang, China as the locale, 100 respondents from five companies are chosen through purposive sampling. A comprehensive survey questionnaire covers demographics, adoption levels, benefits, and challenges.

Results highlight the selected companies in Zheijiang's adaptability in Healthcare, Finance, and Marketing sectors, emphasizing strong data analytics for real-time insights and strategic decisions. The system excels in data accuracy, integration, governance, security, compliance, scalability, and skills development. Recommendations include cross-sector collaborations, continuous training, customer-centric strategies, addressing skills gaps, and fostering interdepartmental collaboration, ensuring the selected companies optimize strengths and foster innovation and adaptability.

Full PDF


  1. Brinker, T. J., & Myers, M. D. (2019). Healthcare analytics: From data to knowledge to
  2. healthcare improvement. European Journal of Information Systems, 28(2), 107-118.
  3. Bughin, J., Chui, M., & Manyika, J. (2016). A playbook for strategy in the age of AI, big
  4. data, and automation. McKinsey Quarterly, 1(1), 44-55.
  5. Chen, H., & Zhang, J. (2018). Adoption of big data analytics in business: A survey of the
  6. literature and a case study in the telecommunication industry. Information Systems Frontiers, 20(2), 269-285.
  7. Chen, H., Chiang, R. H. L., & Storey, V. C. (2014). Business intelligence and analytics:
  8. From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
  9. Chen, H., Chiang, R. H., & Storey, V. C. (2019). Business intelligence and analytics: From
  10. big data to big impact. MIS Quarterly, 43(4), 1251-1275.
  11. Davenport, T. H. (2014). Big data at work: Dispelling the myths, uncovering the
  12. opportunities. Harvard Business Review Press.
  13. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of
  14. winning. Harvard Business Review, 85(1), 98-107.
  15. Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2017). AIMQ: A methodology
  16. for information quality assessment. Information & Management, 40(2), 133-146.
  17. Li, X., Li, Y., & Liang, X. (2019). Application of big data analytics in marketing: A review
  18. and outlook. International Journal of Information Management, 48, 285-294.
  19. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data:
  20. The management revolution. Harvard Business Review, 90(10), 60-68.
  21. Reinschmidt, J., Franzreb, D., & Gundlach, S. (2018). Big data quality management: A
  22. quality maturity model. Journal of Business Research, 92, 448-457.
  23. Riaz, M., Hassan, A., & Aziz, A. (2020). Adoption of business intelligence and its impact
  24. on organizational performance: Evidence from the manufacturing sector of Pakistan. Journal of Innovation & Knowledge, 5(3), 187-196.
  25. Riaz, S., Asghar, M. Z., & Ramayah, T. (2020). An empirical investigation of the impact
  26. of organizational size on business intelligence adoption. Journal of Enterprise Information Management, 33(1), 116-132.
  27. Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2017). From multi-channel retailing to omni-
  28. channel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 93(1), 1-6.
  29. Wang, R. Y., & Strong, D. M. (2017). Beyond accuracy: What data quality means to data
  30. consumers. Journal of Management Information Systems, 12(4), 5-33.