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Framework for Automating Multi-Team Workflows to Maximize Operational Efficiency and Minimize Redundant Data Handling
Subject area: Science,Engineering and Technology · Area of research: Data Handling
Abstract
The increasing complexity of multi-team workflows in organizations necessitates efficient automation frameworks to maximize operational efficiency and minimize redundant data handling. This study proposes a comprehensive framework for automating multi-team workflows, building on existing research in workflow automation and integrating demonstrated methods for reducing operational burdens and increasing time savings. By leveraging advanced data synchronization, task prioritization, and real-time communication technologies, the framework ensures seamless collaboration across teams while eliminating inefficiencies caused by data redundancy and manual processes. The proposed framework incorporates machine learning algorithms for intelligent task allocation and adaptive process management, ensuring optimal utilization of resources. Key innovations include centralized data pipelines that prevent duplication, automated triggers for task execution, and real-time performance analytics to monitor and refine workflows dynamically. The framework was validated through implementation in diverse organizational contexts, demonstrating a measurable reduction in task completion time, improved data accuracy, and enhanced cross-team coordination. This work emphasizes the importance of stakeholder alignment during automation adoption, offering insights into strategies for managing resistance and ensuring user buy-in. Additionally, the study highlights the role of automation in fostering organizational agility by enabling teams to focus on high-value tasks rather than repetitive manual efforts. The integration of cybersecurity measures further ensures data integrity and compliance, addressing concerns over potential vulnerabilities in automated workflows. The findings contribute to the broader discourse on operational efficiency by providing actionable methodologies for organizations aiming to streamline processes and enhance productivity. Key benefits of the framework include improved decision-making speed, reduced operational costs, and a scalable model adaptable to various industries. Future directions include exploring integration with emerging technologies such as blockchain for enhanced data security and augmented reality for immersive team collaboration. The study concludes by advocating for a paradigm shift towards holistic workflow automation to address evolving organizational challenges.
Keywords
Workflow Automation, Operational Efficiency, Data Redundancy, Machine Learning, Real-Time Analytics, Cross-Team Collaboration, Cybersecurity, Stakeholder Alignment, Organizational Agility
References
[1] Akinsooto, O. (2013). Electrical Energy Savings Calculation in Single Phase Harmonic Distorted Systems. University of Johannesburg (South Africa).
[2] Akinsooto, O., De Canha, D., & Pretorius, J. H. C. (2014, September). Energy savings reporting and uncertainty in Measurement & Verification. In 2014 Australasian Universities Power Engineering Conference (AUPEC) (pp. 1-5). IEEE.
[3] Akinsooto, O., Pretorius, J. H., & van Rhyn, P. (2012). Energy savings calculation in a system with harmonics. In Fourth IASTED African Conference on Power and Energy Systems (AfricaPES.
[4] Al-Ali, R., Kathiresan, N., El Anbari, M., Schendel, E. R., & Zaid, T. A. (2016). Workflow optimization of performance and quality of service for bioinformatics application in high performance computing. Journal of Computational Science, 15, 3-10.
[5] Alexopoulos, P. (2020). Semantic modeling for data. O'Reilly Media.
[6] Austin-Gabriel, B., Hussain, N. Y., Ige, A. B., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2021). Advancing zero trust architecture with AI and data science for enterprise cybersecurity frameworks. Open Access Research Journal of Engineering and Technology. https://doi.org/10.53022/oarjet.2021.1.1.0107
[7] Austin-Gabriel, B., Hussain, N. Y., Ige, A. B., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2021). Advancing zero trust architecture with AI and data science for enterprise cybersecurity frameworks. Open Access Research Journal of Engineering and Technology. https://doi.org/10.53022/oarjet.2021.1.1.0107
[8] Bani-Hani, I., Tona, O., & Carlsson, S. (2020). Patterns of resource integration in the self-service approach to business analytics.
[9] Bergner, M. (2015). Integrating natural language processing with semantic-based modeling (Doctoral dissertation, Master’s thesis, University of Vienna).
[10] Bilal, K., Khalid, O., Erbad, A., & Khan, S. U. (2018). Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Computer Networks, 130, 94-120.
[11] Bitter, J. (2017). Improving multidisciplinary teamwork in preoperative scheduling (Doctoral dissertation, [Sl]:[Sn]).
[12] Bolton, A., Goosen, L., & Kritzinger, E. (2016, September). Enterprise digitization enablement through unified communication & collaboration. In Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists (pp. 1-10).
[13] Bratasanu, V. (2018). Leadership decision-making processes in the context of data driven tools. Quality-Access to Success, 19.
[14] Braun, T., Fung, B. C., Iqbal, F., & Shah, B. (2018). Security and privacy challenges in smart cities. Sustainable cities and society, 39, 499-507.
[15] Brinch, M. (2018). Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework. International Journal of Operations & Production Management, 38(7), 1589-1614.
[16] Brown, A., Fishenden, J., Thompson, M., & Venters, W. (2017). Appraising the impact and role of platform models and Government as a Platform (GaaP) in UK Government public service reform: Towards a Platform Assessment Framework (PAF). Government Information Quarterly, 34(2), 167-182.
[17] Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), 48-57.
[18] Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences, 275, 314-347.
[19] Chen, Q., Hall, D. M., Adey, B. T., & Haas, C. T. (2020). Identifying enablers for coordination across construction supply chain processes: a systematic literature review. Engineering, construction and architectural management, 28(4), 1083-1113.
[20] Chen, Y., Richter, J. I., & Patel, P. C. (2021). Decentralized governance of digital platforms. Journal of Management, 47(5), 1305-1337.
[21] Curuksu, J. D. (2018). Data driven. Management for Professionals.
[22] Davis, J. E. (2014). Temporal meta-model framework for Enterprise Information Systems (EIS) development (Doctoral dissertation, Curtin University).
[23] de Assuncao, M. D., da Silva Veith, A., & Buyya, R. (2018). Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. Journal of Network and Computer Applications, 103, 1-17.
[24] Dulam, N., Gosukonda, V., & Allam, K. (2021). Data Mesh in Action: Case Studies from Leading Enterprises. Journal of Artificial Intelligence Research and Applications, 1(2), 488-509.
[25] Dulam, N., Gosukonda, V., & Gade, K. R. (2020). Data As a Product: How Data Mesh Is Decentralizing Data Architectures. Distributed Learning and Broad Applications in Scientific Research, 6.
[26] Dulam, N., Katari, A., & Allam, K. (2020). Data Mesh in Practice: How Organizations Are Decentralizing Data Ownership. Distributed Learning and Broad Applications in Scientific Research, 6.
[27] Duo, X., Xu, P., Zhang, Z., Chai, S., Xia, R., & Zong, Z. (2022, October). KCL: A Declarative Language for Large-Scale Configuration and Policy Management. In International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (pp. 88-105). Cham: Springer Nature Switzerland.
[28] Dussart, P., van Oortmerssen, L. A., & Albronda, B. (2021). Perspectives on knowledge integration in cross-functional teams in information systems development. Team Performance Management: An International Journal, 27(3/4), 316-331.
[29] Dutta, D., & Bose, I. (2015). Managing a big data project: the case of ramco cements limited. International Journal of Production Economics, 165, 293-306.
[30] Escamilla-Ambrosio, P. J., Rodríguez-Mota, A., Aguirre-Anaya, E., Acosta-Bermejo, R., & Salinas-Rosales, M. (2018). Distributing computing in the internet of things: cloud, fog and edge computing overview. In NEO 2016: Results of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop held on September 20-24, 2016 in Tlalnepantla, Mexico (pp. 87-115). Springer International Publishing.
[31] Evans, P., Parker, G., Van Alstyne, M. W., & Finkhousen, D. (2021). Platform leadership: staffing and training the inverted firm. Available at SSRN 3871971.
[32] Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
[33] Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
[34] Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
[35] Gade, K. R. (2021). Data-Driven Decision Making in a Complex World. Journal of Computational Innovation, 1(1).
[36] Gade, K. R. (2022). Data Analytics: Data Fabric Architecture and Its Benefits for Data Management. MZ Computing Journal, 3(2).
[37] Gallino, S., & Rooderkerk, R. (2020). New product development in an omnichannel world. California Management Review, 63(1), 81-98.
[38] Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017). Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys & Tutorials, 19(4), 2456-2501.
[39] Govindarajan, N., Ferrer, B. R., Xu, X., Nieto, A., & Lastra, J. L. M. (2016, July). An approach for integrating legacy systems in the manufacturing industry. In 2016 IEEE 14th International Conference on Industrial Informatics (INDIN) (pp. 683-688). IEEE.
[40] Goyal, A. (2021). Enhancing Engineering Project Efficiency through Cross-Functional Collaboration and IoT Integration. Int. J. Res. Anal. Rev, 8(4), 396-402.
[41] Gudivada, V. N., Rao, D., & Raghavan, V. V. (2015). Big data driven natural language processing research and applications. In Handbook of statistics (Vol. 33, pp. 203-238). Elsevier.
[42] Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarci, B., & Soyata, T. (2019). A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustainable Cities and Society, 50, 101660.
[43] Halper, F., & Stodder, D. (2017). What it takes to be data-driven. TDWI Best Practices Report, December, 33-49.
[44] Hani, I. B. (2020). Self-Service Business Analytics and the Path to Insights: Integrating Resources for Generating Insights.
[45] Hassan, A., & Mhmood, A. H. (2021). Optimizing network performance, automation, and intelligent decision-making through real-time big data analytics. International Journal of Responsible Artificial Intelligence, 11(8), 12-22.
[46] Hayretci, H. E., & Aydemir, F. B. (2021, June). A multi case study on legacy system migration in the banking industry. In International Conference on Advanced Information Systems Engineering (pp. 536-550). Cham: Springer International Publishing.
[47] Henke, N., & Jacques Bughin, L. (2016). The age of analytics: Competing in a data-driven world.
[48] Hiidensalo, A. (2016). A framework for improving cost-effectiveness of product designs by cross-functional and inter-organizational collaboration (Master's thesis).
[49] Hlanga, M. F. (2022). Regulatory Compliance of Electric Hot Water Heaters: A Case Study. University of Johannesburg (South Africa).
[50] Hussain, N. Y., Austin-Gabriel, B., Ige, A. B., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2021). AI-driven predictive analytics for proactive security and optimization in critical infrastructure systems. Open Access Research Journal of Science and Technology. https://doi.org/10.53022/oarjst.2021.2.2.0059
[51] Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Press.
[52] Ike, C. C., Ige, A. B., Oladosu, S. A., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2021). Redefining zero trust architecture in cloud networks: A conceptual shift towards granular, dynamic access control and policy enforcement. Magna Scientia Advanced Research and Reviews, 2(1), 074–086. https://doi.org/10.30574/msarr.2021.2.1.0032
[53] Ike, C. C., Ige, A. B., Oladosu, S. A., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2021). Redefining zero trust architecture in cloud networks: A conceptual shift towards granular, dynamic access control and policy enforcement. Magna Scientia Advanced Research and Reviews, 2(1), 074–086. https://doi.org/10.30574/msarr.2021.2.1.0032
[54] Ilebode, T., & Mukherjee, A. (2019). From Chaos To Order: A study on how data-driven development can help improve decision-making.
[55] Jacobi, R., & Brenner, E. (2018). How large corporations survive digitalization. Digital marketplaces unleashed, 83-97.
[56] Jiang, C., Cheng, X., Gao, H., Zhou, X., & Wan, J. (2019). Toward computation offloading in edge computing: A survey. IEEE Access, 7, 131543-131558.
[57] Jones, C. L., Golanz, B., Draper, G. T., & Janusz, P. (2020). Practical Software and Systems Measurement Continuous Iterative Development Measurement Framework. Version, 1, 15.
[58] Li, Y., Thomas, M. A., & Liu, D. (2021). From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research. European Journal of Information Systems, 30(5), 569-590.
[59] Lin, L., Liao, X., Jin, H., & Li, P. (2019). Computation offloading toward edge computing. Proceedings of the IEEE, 107(8), 1584-1607.
[60] Lin, Y., Wang, Y., & Kung, L. (2015). Influences of cross-functional collaboration and knowledge creation on technology commercialization: Evidence from high-tech industries. Industrial marketing management, 49, 128-138.
[61] Lnenicka, M., & Komarkova, J. (2019). Developing a government enterprise architecture framework to support the requirements of big and open linked data with the use of cloud computing. International Journal of Information Management, 46, 124-141.
[62] Loukiala, A., Joutsenlahti, J. P., Raatikainen, M., Mikkonen, T., & Lehtonen, T. (2021, November). Migrating from a centralized data warehouse to a decentralized data platform architecture. In International Conference on Product-Focused Software Process Improvement (pp. 36-48). Cham: Springer International Publishing.
[63] Machireddy, J. R., Rachakatla, S. K., & Ravichandran, P. (2021). Leveraging AI and Machine Learning for Data-Driven Business Strategy: A Comprehensive Framework for Analytics Integration. African Journal of Artificial Intelligence and Sustainable Development, 1(2), 12-150.
[64] Mah, P. M., Skalna, I., & Muzam, J. (2022). Natural language processing and artificial intelligence for enterprise management in the era of industry 4.0. Applied Sciences, 12(18), 9207.
[65] Masuda, Y., & Viswanathan, M. (2019). Enterprise architecture for global companies in a digital it era: adaptive integrated digital architecture framework (AIDAF). Springer.
[66] Maynard, D., Bontcheva, K., & Augenstein, I. (2017). Natural language processing for the semantic web. San Rafael: Morgan & Claypool.
[67] Michalczyk, S., Nadj, M., Azarfar, D., Maedche, A., & Gröger, C. (2020). A state-of-the-art overview and future research avenues of self-service business intelligence and analytics.
[68] Mishra, S. (2020). Moving data warehousing and analytics to the cloud to improve scalability, performance and cost-efficiency. Distributed Learning and Broad Applications in Scientific Research, 6.
[69] Mishra, S., Komandla, V., & Bandi, S. (2021). A new pattern for managing massive datasets in the Enterprise through Data Fabric and Data Mesh. Journal of AI-Assisted Scientific Discovery, 1(2), 236-259.
[70] Nookala, G. (2022). Metadata-Driven Data Models for Self-Service BI Platforms. Journal of Big Data and Smart Systems, 3(1).
[71] Oladosu, S. A., Ike, C. C., Adepoju, P. A., Afolabi, A. I., Ige, A. B., & Amoo, O. O. (2021). The future of SD-WAN: A conceptual evolution from traditional WAN to autonomous, self-healing network systems. Magna Scientia Advanced Research and Reviews. https://doi.org/10.30574/msarr.2021.3.2.0086
[72] Oladosu, S. A., Ike, C. C., Adepoju, P. A., Afolabi, A. I., Ige, A. B., & Amoo, O. O. (2021). Advancing cloud networking security models: Conceptualizing a unified framework for hybrid cloud and on-premises integrations. Magna Scientia Advanced Research and Reviews. https://doi.org/10.30574/msarr.2021.3.1.0076
[73] Oliveira, E. A. D., Pimenta, M. L., Hilletofth, P., & Eriksson, D. (2016). Integration through cross-functional teams in a service company. European Business Review, 28(4), 405-430.
[74] Onoja, J. P., Ajala, O. A., & Ige, A. B. (2022). Harnessing artificial intelligence for transformative community development: A comprehensive framework for enhancing engagement and impact. GSC Advanced Research and Reviews, 11(03), 158–166. https://doi.org/10.30574/gscarr.2022.11.3.0154
[75] Paik, H. Y., Xu, X., Bandara, H. D., Lee, S. U., & Lo, S. K. (2019). Analysis of data management in blockchain-based systems: From architecture to governance. Ieee Access, 7, 186091-186107.
[76] Perkin, N., & Abraham, P. (2021). Building the agile business through digital transformation. Kogan Page Publishers.
[77] Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22(4), 2462-2488.
[78] Raj, P., Raman, A., Nagaraj, D., & Duggirala, S. (2015). High-performance big-data analytics. Computing Systems and Approaches (Springer, 2015), 1.
[79] Raj, P., Vanga, S., & Chaudhary, A. (2022). Cloud-Native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications. John Wiley & Sons.
[80] Raptis, T. P., Passarella, A., & Conti, M. (2019). Data management in industry 4.0: State of the art and open challenges. IEEE Access, 7, 97052-97093.
[81] Ratzesberger, O., & Sawhney, M. (2017). The sentient enterprise: The evolution of business decision making. John Wiley & Sons.
[82] Rico, R., Hinsz, V. B., Davison, R. B., & Salas, E. (2018). Structural influences upon coordination and performance in multiteam systems. Human Resource Management Review, 28(4), 332-346.
[83] Russo, D., Spreafico, M., & Precorvi, A. (2020). Discovering new business opportunities with dependent semantic parsers. Computers in Industry, 123, 103330.
[84] Saarikallio, M. (2022). Improving hybrid software business: quality culture, cycle-time and multi-team agile management. JYU dissertations.
[85] Salamkar, M. A. (2019). Next-Generation Data Warehousing: Innovations in cloud-native data warehouses and the rise of serverless architectures. Distributed Learning and Broad Applications in Scientific Research, 5.
[86] Sivagnana Ganesan, A. (2019). Framework for handling evolution of legacy systems (Doctoral dissertation, Department of Banking Technology, Pondicherry University).
[87] Stodder, D. (2015). Visual analytics for making smarter decisions faster. Best Practices Report, TDWI Research.
[88] Tang, P., Yilmaz, A., & Cooke, N. (2018). Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages (No. 15-8121). Arizona State Univ., Tempe, AZ (United States).
[89] Theodorou, V. (2017). Automating user-centered design of data-intensive processes.
[90] Venkatesan, D., & Sridhar, S. (2017). A novel programming framework for architecting next generation enterprise scale information systems. Information Systems and e-Business Management, 15, 489-534.
[91] Vlietland, J., Van Solingen, R., & Van Vliet, H. (2016). Aligning codependent Scrum teams to enable fast business value delivery: A governance framework and set of intervention actions. Journal of Systems and Software, 113, 418-429.
[92] Zhang, C., Tang, P., Cooke, N., Buchanan, V., Yilmaz, A., Germain, S. W. S., ... & Gupta, A. (2017). Human-centered automation for resilient nuclear power plant outage control. Automation in Construction, 82, 179-192.
[93] Zhou, R., Awasthi, A., & Stal-Le Cardinal, J. (2021). The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing. Computers in Industry, 125, 103369.
[94] Zong, Z. (2022, December). KCL: A Declarative Language for Large-Scale Configuration and Policy Management. In Dependable Software Engineering. Theories, Tools, and Applications: 8th International Symposium, SETTA 2022, Beijing, China, October 27-29, 2022, Proceedings (Vol. 13649, p. 88). Springer Nature.
[95] Zou, M., Vogel-Heuser, B., Sollfrank, M., & Fischer, J. (2020, December). A cross-disciplinary model-based systems engineering workflow of automated production systems leveraging socio-technical aspects. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 133-140). IEEE.
How to cite this paper
@article{1703282,
author = {Adebusayo Hassanat Adepoju, Blessing Austin-Gabriel, Adeoluwa Eweje, Anuoluwapo Collins},
title = {Framework for Automating Multi-Team Workflows to Maximize Operational Efficiency and Minimize Redundant Data Handling},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {9},
pages = {663-679},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703282.pdf},
abstract = {The increasing complexity of multi-team workflows in organizations necessitates efficient automation frameworks to maximize operational efficiency and minimize redundant data handling. This study proposes a comprehensive framework for automating multi-team workflows, building on existing research in workflow automation and integrating demonstrated methods for reducing operational burdens and increasing time savings. By leveraging advanced data synchronization, task prioritization, and real-time communication technologies, the framework ensures seamless collaboration across teams while eliminating inefficiencies caused by data redundancy and manual processes. The proposed framework incorporates machine learning algorithms for intelligent task allocation and adaptive process management, ensuring optimal utilization of resources. Key innovations include centralized data pipelines that prevent duplication, automated triggers for task execution, and real-time performance analytics to monitor and refine workflows dynamically. The framework was validated through implementation in diverse organizational contexts, demonstrating a measurable reduction in task completion time, improved data accuracy, and enhanced cross-team coordination. This work emphasizes the importance of stakeholder alignment during automation adoption, offering insights into strategies for managing resistance and ensuring user buy-in. Additionally, the study highlights the role of automation in fostering organizational agility by enabling teams to focus on high-value tasks rather than repetitive manual efforts. The integration of cybersecurity measures further ensures data integrity and compliance, addressing concerns over potential vulnerabilities in automated workflows. The findings contribute to the broader discourse on operational efficiency by providing actionable methodologies for organizations aiming to streamline processes and enhance productivity. Key benefits of the framework include improved decision-making speed, reduced operational costs, and a scalable model adaptable to various industries. Future directions include exploring integration with emerging technologies such as blockchain for enhanced data security and augmented reality for immersive team collaboration. The study concludes by advocating for a paradigm shift towards holistic workflow automation to address evolving organizational challenges.},
keywords = {Workflow Automation, Operational Efficiency, Data Redundancy, Machine Learning, Real-Time Analytics, Cross-Team Collaboration, Cybersecurity, Stakeholder Alignment, Organizational Agility},
month = {March},
}