Real-Time Analytics Dashboards for Emergency Business Continuity and Crisis Decision Support
DOI:
https://doi.org/10.32628/IJSRST25123103Keywords:
Big Data Analytics, Business Continuity, Cloud Computing, Crisis Decision Support, Data IntegrationAbstract
This paper proposes a framework for designing and implementing real-time analytics dashboards to support emergency business continuity and crisis decision-making. Leveraging technologies such as big data analytics, artificial intelligence (AI), and cloud computing, the framework enables organizations to monitor critical metrics, predict disruptions, and make informed decisions during crises. A systematic literature review synthesizes insights on dashboard design, data integration, and crisis management, while case studies across logistics, healthcare, and finance validate the framework’s applicability. Findings demonstrate that real-time dashboards can reduce response times by up to 50%, improve decision accuracy by 40%, and enhance operational resilience. The study contributes to the literature on crisis management and offers practical guidelines for organizations aiming to strengthen business continuity.
Downloads
References
E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, and E. D. Balogun, “Real-time data analytics for enhancing supply chain efficiency,” Journal of Supply Chain Management and Analytics, vol. 10, no. 1, pp. 49–60, 2023.
I. A. Omar, R. Jayaraman, M. S. Debe, K. Salah, I. Yaqoob, and M. Omar, “Automating Procurement Contracts in the Healthcare Supply Chain Using Blockchain Smart Contracts,” IEEE Access, vol. 9, pp. 37397–37409, 2021, doi: 10.1109/ACCESS.2021.3062471.
Y. Yang, C. Peng, E. Z. Cao, and W. Zou, “Building Resilience in Supply Chains: A Knowledge Graph-Based Risk Management Framework,” IEEE Trans Comput Soc Syst, vol. 11, no. 3, pp. 3873–3881, Jun. 2024, doi: 10.1109/TCSS.2023.3334768.
O. A. Oluokun, O. Akinsooto, O. B. Ogundipe, and S. Ikemba, “Leveraging Cloud Computing and Big Data analytics for policy-driven energy optimization in smart cities,” 2024.
M. Barika, S. Garg, A. Y. Zomaya, L. Wang, A. V. A. N. Moorsel, and R. Ranjan, “Orchestrating big data analysis workflows in the cloud: Research challenges, survey, and future directions,” ACM Comput Surv, vol. 52, no. 5, p. 95, Sep. 2019, doi: 10.1145/3332301/SUPPL_FILE/BARIKA.ZIP.
O. G. Kayas, “Workplace surveillance: A systematic review, integrative framework, and research agenda,” J Bus Res, vol. 168, p. 114212, Nov. 2023, doi: 10.1016/J.JBUSRES.2023.114212.
B. Herbane, D. Elliott, and E. M. Swartz, “Business Continuity Management: time for a strategic role?,” Long Range Plann, vol. 37, no. 5, pp. 435–457, Oct. 2004, doi: 10.1016/J.LRP.2004.07.011.
I. Fidel-Anyanna, G. Onus, U. Mikel-Olisa, and N. Ayanbode, “Theoretical frameworks for addressing cybersecurity challenges in financial institutions: Lessons from Africa-US collaborations,” International Journal of Social Science Exceptional Research, vol. 3, no. 1, pp. 51–55, 2024, doi: https://doi.org/10.54660/IJSSER.2024.3.1.51-55.
B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “Integrating AI-driven risk assessment frameworks in financial operations: A model for enhanced corporate governance,” International Journal of Scientific Research in Computer Science, 2023.
S. Akter et al., “A framework for AI-powered service innovation capability: Review and agenda for future research,” Technovation, vol. 125, p. 102768, Jul. 2023, doi: 10.1016/J.TECHNOVATION.2023.102768.
S. Strohmeier, “Smart HRM–a Delphi study on the application and consequences of the Internet of Things in Human Resource Management,” International Journal of Human Resource Management, vol. 31, no. 18, pp. 2289–2318, Oct. 2020, doi: 10.1080/09585192.2018.1443963/ASSET/4B541280-025B-46F9-8D19-DF7B827DAB0E/ASSETS/IMAGES/LARGE/RIJH_A_1443963_F0005_B.JPG.
G. Assunção, B. Patrão, M. Castelo-Branco, and P. Menezes, “An Overview of Emotion in Artificial Intelligence,” IEEE Transactions on Artificial Intelligence, vol. 3, no. 6, pp. 867–886, Dec. 2022, doi: 10.1109/TAI.2022.3159614.
M. Sony and S. Naik, “Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model,” Technol Soc, vol. 61, p. 101248, May 2020, doi: 10.1016/J.TECHSOC.2020.101248.
K. T. Yang, “Artificial Neural Networks (ANNs): A new paradigm for thermal science and engineering,” J Heat Transfer, vol. 130, no. 9, Sep. 2008, doi: 10.1115/1.2944238/467746.
M. I. Ononiwu, O. C. Onwuzulike, and K. Shitu, “The role of digital business transformation in enhancing organizational agility,” https://wjarr.co.in/sites/default/files/WJARR-2024-2670.pdf, vol. 23, no. 3, pp. 285–308, Sep. 2024, doi: 10.30574/WJARR.2024.23.3.2670.
Olufunke Anne Alabi, Funmilayo Aribidesi Ajayi, Chioma Ann Udeh, and Christianah Pelumi Efunniyi, “Data-driven employee engagement: A pathway to superior customer service,” World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 923–933, Sep. 2024, doi: 10.30574/wjarr.2024.23.3.2733.
O. Oke and O. Awoyemi, “Cybersecurity in Government and Corporate Communications: A Risk Mitigation Framework for Public Relations in the Digital Age,” Journal of Frontiers in Multidisciplinary Research, vol. 5, no. 1, pp. 50–59, 2024, doi: 10.54660/.IJFMR.2024.5.1.50-59.
J. Valkenburgh, “Enhancing Business Dashboards with Explanatory Analytics & AI Exploring the Use of AI and Explanatory Analytics to Enhance Business Decision-Making”.
O. Oke and O. A. Olanrewaju Awoyemi, “The Development Communication Framework for Public Sector Transformation: A Case Study of Nigeria and the U.S.,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 5, no. 1, pp. 1375–1383, 2024, doi: 10.54660/.IJMRGE.2024.5.1.1375-1383.
C. O. Ozobu, F. E. Adikwu, O. Odujobi, F. O. Onyekwe, E. O. Nwulu, and A. I. Daraojimba, “Leveraging AI and Machine Learning to Predict Occupational Diseases: A Conceptual Framework for Proactive Health Risk Management in High-Risk Industries”, doi: 10.54660/.IJMRGE.2023.4.1.928-938.
N. Woods and G. Babatunde, “A robust ensemble model for spoken language recognition,” Applied Computer Science, vol. 16, no. 3, pp. 56–68, 2020, doi: 10.23743/acs-2020-21.
O. Odujobi, F. O. Onyekwe, C. O. Ozobu, F. E. Adikwu, and E. O. Nwulu, “A Conceptual Model for Integrating Ergonomics and Health Surveillance to Reduce Occupational Illnesses in the Nigerian Manufacturing Sector,” International Journal of Multidisciplinary Research and Growth Evaluation., vol. 5, no. 1, pp. 1425–1437, 2024, doi: 10.54660/.IJMRGE.2024.5.1.1425-1437.
E. C. Onukwulu, J. E. Fiemotongha, A. N. Igwe, and P.-M. Ewim, “The strategic influence of geopolitical events on crude oil pricing: An analytical approach for global traders International Journal of Management and Organizational Research The strategic influence of geopolitical events on crude oil pricing: An analytical approach for global traders,” Article in International Journal of Management and Organizational Research, 2022, doi: 10.54660/IJMOR.2022.1.1.58-74.
S. A. Shah, D. Z. Seker, S. Hameed, and D. Draheim, “The rising role of big data analytics and IoT in disaster management: Recent advances, taxonomy and prospects,” IEEE Access, vol. 7, pp. 54595–54614, 2019, doi: 10.1109/ACCESS.2019.2913340.
Rhoda Adura Adeleye, Tula Sunday Tubokirifuruar, Binaebi Gloria Bello, Ndubuisi Leonard Ndubuisi, Onyeka Franca Asuzu, and Oluwaseyi Rita Owolabi, “MACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY,” Finance & Accounting Research Journal, vol. 6, no. 2, pp. 112–124, Feb. 2024, doi: 10.51594/farj.v6i2.783.
O. A. Alabi, F. A. Ajayi, C. A. Udeh, and C. P. Efunniyi, “Predictive Analytics in Human Resources: Enhancing Workforce Planning and Customer Experience,” International Journal of Research and Scientific Innovation, vol. XI, no. IX, pp. 149–158, 2024, doi: 10.51244/IJRSI.2024.1109016.
E. Brynjolfsson, W. Jin, and K. McElheran, “The power of prediction: predictive analytics, workplace complements, and business performance,” Business Economics, vol. 56, no. 4, pp. 217–239, Oct. 2021, doi: 10.1057/S11369-021-00224-5/TABLES/5.
M. Holmlund et al., “Customer experience management in the age of big data analytics: A strategic framework,” J Bus Res, vol. 116, pp. 356–365, Aug. 2020, doi: 10.1016/J.JBUSRES.2020.01.022.
Ilori and O, “AI-driven audit analytics: A conceptual model for real-time risk detection and compliance monitoring,” (2023). AI-driven audit analytics: A conceptual model for real-time risk detection and compliance monitoring. Finance & Accounting Research Journal, vol. 2023), 2023.
O. Uchendu, K. O. Omomo, and A. E. Esiri, “Conceptual Framework for Data-driven Reservoir Characterization: Integrating Machine Learning in Petrophysical Analysis,” Comprehensive Research and Reviews in Multidisciplinary Studies, vol. 2, no. 2, pp. 1–13, 2024.
B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, “Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement,” Mach Learn, vol. 2, no. 1, p. 18, 2021.
L. Zhao, “Event Prediction in the Big Data Era: A Systematic Survey,” ACM Comput Surv, vol. 54, no. 5, Jun. 2021, doi: 10.1145/3450287/SUPPL_FILE/3450287-CORRIGENDUM.PDF.
Leesi Saturday Komi, Ashiata Yetunde Mustapha, Adelaide Yeboah Forkuo, and Damilola Osamika, “Reviewing Pharmacovigilance Strategies Using Real-World Data for Drug Safety Monitoring and Management,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11, no. 2, pp. 3771–3779, Apr. 2025, doi: 10.32628/CSEIT25112853.
E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, “Real-time data analytics for enhancing supply chain efficiency,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, no. 1, pp. 759–771, 2021, doi: 10.54660/.IJMRGE.2021.2.1.759-771.
Chianumba, I. E. C., M. N., F. A. Y., A. Y. Osamika, and D, “Developing a predictive model for healthcare compliance, risk management, and fraud detection using data analytics,” C., Ikhalea, N., Mustapha, A. Y., Forkuo, A. Y., & Osamika, D. (2022). Developing a predictive model for healthcare compliance, risk management, and fraud detection using data analytics. International Journal of Social Science Exceptional Research, vol. 2022), 2022.
O. Ogunbiyi-Badaru, O. B. Alao, O. F. Dudu, and E. O. Alonge, “Blockchain-enabled asset management: Opportunities, risks and global implications,” Comprehensive Research and Reviews in Multidisciplinary Studies, vol. 14, 2024.
Y. Demirel, “Energy Management and Economics,” Green Energy and Technology, pp. 531–617, 2021, doi: 10.1007/978-3-030-56164-2_13/TABLES/9.
P. K. Mally, “Cloud Data Warehousing and AI Analytics: A Comprehensive Review of Literature,” International Journal of Computer Trends and Technology, vol. 71, pp. 28–38, 2023, doi: 10.14445/22312803/IJCTT-V71I10P104.
E. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “Integrating AI, blockchain, and big data to strengthen healthcare data security, privacy, and patient outcomes,” researchgate.netEC Chianumba, N Ikhalea, AY Mustapha, AY Forkuo, D OsamikaJournal of Frontiers in Multidisciplinary Research, 2022•researchgate.net, vol. 3, no. 1, pp. 124–129, 2022, doi: 10.54660/.IJFMR.2022.3.1.124-129.
Osamika, A. D., K.-A. B. S., M. M. C., A. Y. Ikhalea, and N, “Artificial intelligence-based systems for cancer diagnosis: Trends and future prospects,” S., Kelvin-Agwu, M. C., Mustapha, A. Y., & Ikhalea, N. (2022). Artificial intelligence-based systems for cancer diagnosis: Trends and future prospects. IRE Journals, vol. 2022), 2022.
Esan, U. O. J., O. O. T., O. O., G. O. Etukudoh, and E. A, “Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework,” J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Etukudoh, E. A. (2022). Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework. Journal of Frontiers in Multidisciplinary Research, vol. 2022), 2022, [Online]. Available: https://doi.org/10.51594/estj.v6i2.1862
Chianumba, I. E. C., M. N., F. A. Y., A. Y. Osamika, and D, “Exploring the role of AI and machine learning in improving healthcare diagnostics and personalized medicine,” C., Ikhalea, N., Mustapha, A. Y., Forkuo, A. Y., & Osamika, D. (2023). Exploring the role of AI and machine learning in improving healthcare diagnostics and personalized medicine. Journal of Frontiers in Multidisciplinary Research, vol. 2023), 2023.
P. Russom, D. Stodder, and F. Halper, “TDWI BEST PRACTICES REPORT TDWI RESEARCh Real-Time Data, BI, and Analytics Accelerating Business to Leverage Customer Relations, Competitiveness, and Insights,” 2014.
N. Kumar, “IoT-Enabled Real-Time Data Integration in ERP Systems,” 2022, doi: 10.32628/IJSRSET2215479.
F. Halper, “Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processing BEST PRACTICES REPORT,” 2017.
Leesi Saturday Komi, Ashiata Yetunde Mustapha, Adelaide Yeboah Forkuo, and Damilola Osamika, “Reviewing Pharmacovigilance Strategies Using Real-World Data for Drug Safety Monitoring and Management,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11, no. 2, pp. 3771–3779, Apr. 2025, doi: 10.32628/CSEIT25112853.
“Advances in global services and retail management: Volume 2,” Advances in global services and retail management: Volume 2, Sep. 2021, doi: 10.5038/9781955833035.
“Ajiga: Assessing the role of HR analytics in transforming... - Google Scholar.” Accessed: May 12, 2025. [Online]. Available: https://scholar.google.com/scholar?cluster=324918711219914032&hl=en&oi=scholarr
A. Ajayi, “AI Integration in STEM Curriculum: A Conceptual Model for Deepening Student Engagement and Learning,” 2024. [Online]. Available: www.multiresearchjournal.com
Pietikäinen, Matti, Silvén, and Olli, “Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence,” Jan. 2022, Accessed: May 15, 2025. [Online]. Available: https://arxiv.org/pdf/2201.01466
M. A. Adewoyin, O. Adediwin, and A. J. Audu, “Artificial Intelligence and Sustainable Energy Development: A Review of Applications, Challenges, and Future Directions,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, 2025.
Y. G. Hassan, A. Collins, G. O. Babatunde, A. A. Alabi, and S. D. Mustapha, “AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 5, no. 1, pp. 1197–1202, 2024, doi: 10.54660/.IJMRGE.2024.5.1.1197-1202.
A. Hübner, J. Hense, and C. Dethlefs, “The revival of retail stores via omnichannel operations: A literature review and research framework,” Eur J Oper Res, vol. 302, no. 3, pp. 799–818, Nov. 2022, doi: 10.1016/J.EJOR.2021.12.021.
M. Khakifirooz, M. Fathi, A. Dolgui, and P. M. Pardalos, “Assessing resiliency in scale-free supply chain networks: a stress testing approach based on entropy measurements and value-at-risk analysis,” Int J Prod Res, Jun. 2024, doi: 10.1080/00207543.2024.2361850;WGROUP:STRING:PUBLICATION.
Ugbaja, A. U. S., E. O. S., L. F. S., C. I. Friday, and S. C, “Unknown Title,” vol. 2023), 2023.
A. Re-Thinking et al., “Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges,” Applied Sciences 2023, Vol. 13, Page 7082, vol. 13, no. 12, p. 7082, Jun. 2023, doi: 10.3390/APP13127082.
M. Tory, L. Bartram, B. Fiore-Gartland, and A. Crisan, “Finding Their Data Voice: Practices and Challenges of Dashboard Users,” IEEE Comput Graph Appl, vol. 43, no. 1, pp. 22–36, Jan. 2023, doi: 10.1109/MCG.2021.3136545.
“Data Quality: Dimensions, Measurement, Strategy, Management, and Governance - Rupa Mahanti - Google Books.” Accessed: May 12, 2025. [Online]. Available: https://books.google.co.za/books?hl=en&lr=&id=THeSDwAAQBAJ&oi=fnd&pg=PA1976&dq=%E2%80%A2%09Data+Quality:+Incomplete+or+inconsistent+data+can+undermine+reliability,+with+60%25+of+organizations+citing+data+quality+as+a+barrier+&ots=0wn1PDGine&sig=JqZLZWixFYSvwD72K2eiQrHxaeo&redir_esc=y#v=onepage&q&f=false
“Onukwulu: Mitigating market volatility: Advanced... - Google Scholar.” Accessed: May 12, 2025. [Online]. Available: https://scholar.google.com/scholar?hl=en&as_sdt=0,5&cluster=17054452682085348633
I. Herath, “Cross-Platform Development With Full-Stack Frameworks: Bridging the Gap for Seamless Integration,” 2024, Accessed: May 11, 2025. [Online]. Available: http://www.theseus.fi/handle/10024/867532
“Chukwurah: Frameworks for effective data governance:... - Google Scholar.” Accessed: May 11, 2025. [Online]. Available: https://scholar.google.com/scholar?cluster=17100741514767868648&hl=en&oi=scholarr
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
