Information and engineering sciences | Online ISSN 3068-0115
RESEARCH ARTICLE   (Open Access)

Advanced Analytics in IT Systems Unlocking Insights Enhancing Decision Making and Maximizing Business Value

Anik Biswas1*, Md Iqbal Hossain2, Ispita Jahan3, Niladry Chowdhury4, Md Sakib Mia4

+ Author Affiliations

Applied IT & Engineering 2 (1) 1-8 https://doi.org/10.25163/engineering.2110379

Submitted: 31 October 2023 Revised: 15 January 2024  Published: 17 January 2024 


Abstract

Background: The expanding digital transformation of businesses has revealed the need for sophisticated IT system analytics which deliver operational data and better decision-making capabilities. Traditional IT management experiences problems when dealing with extensive and complicated datasets which leads to both operational inefficiencies and limited value generation. Organizations use advanced analytics to solve this problem through big data and artificial intelligence and predictive modeling which allows them to improve IT-based business operations.

Methods: The expanding digital transformation of businesses has revealed the need for sophisticated IT system analytics which deliver operational data and better decision-making capabilities. Traditional IT management experiences problems when dealing with extensive and complicated datasets which leads to both operational inefficiencies and limited value generation. Organizations use advanced analytics to solve this problem through big data and artificial intelligence and predictive modeling which allows them to improve IT-based business operations.

Results: Organizations which adopted advanced analytics demonstrated a 31.7% boost in decision speed and a 26.9% improvement in accuracy when compared to organizations without these systems. The IT operations experienced an 18.4% decrease in costs together with a 21.6% increase in system performance. The company achieved a 15.2% boost in competitiveness through customer segmentation and predictive insights which resulted in a 19.8% average revenue increase. The survey participants showed that analytics-based IT systems deliver better scalability and efficiency and business value than conventional methods.

Conclusion: The study shows that advanced analytics improves IT systems through fast precise decision-making and cost reduction and business value optimization which leads to better organizational competitiveness.

Keywords: Advanced analytics, IT systems, Decision making, Business value, Data insights.

References

Abisoye, A., & Akerele, J. I. (2021). A High-Impact Data-Driven Decision-Making Model for Integrating Cutting-Edge Cybersecurity Strategies into Public Policy, Governance, and Organizational Frameworks. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 623–637. https://doi.org/10.54660/.ijmrge.2021.2.1.623-637

Akter, S., Bandara, R., Hani, U., Wamba, S. F., Foropon, C., & Papadopoulos, T. (2019). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48, 85–95. https://doi.org/10.1016/j.ijinfomgt.2019.01.020

Azvine, B., Cui, Z., & Nauck, D. D. (2005). Towards real-time business intelligence. BT Technology Journal, 23(3), 214–225. https://doi.org/10.1007/s10550-005-0043-0

Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2019). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources Conservation and Recycling, 153, 104559. https://doi.org/10.1016/j.resconrec.2019.104559

Brock, V., & Khan, H. U. (2017). Big data analytics: does organizational factor matters impact technology acceptance? Journal of Big Data, 4(1). https://doi.org/10.1186/s40537-017-0081-8

Bussa, S. (2023). Enhancing BI tools for improved data visualization and insights. International Journal of Computer Science and Mobile Computing, 12(2), 70–92. https://doi.org/10.47760/ijcsmc.2023.v12i02.005

Chae, B., Yang, C., Olson, D., & Sheu, C. (2013). The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems, 59, 119–126. https://doi.org/10.1016/j.dss.2013.10.012

Craig, T., & Campbell, D. (2012). Organisations and the business environment. In Routledge eBooks. https://doi.org/10.4324/9780080454603

Del Mar Alonso-Almeida, M., Bremser, K., & Llach, J. (2015). Proactive and reactive strategies deployed by restaurants in times of crisis. International Journal of Contemporary Hospitality Management, 27(7), 1641–1661. https://doi.org/10.1108/ijchm-03-2014-0117

Francis, R., & Bekera, B. (2013). A metric and frameworks for resilience analysis of engineered and infrastructure systems. Reliability Engineering & System Safety, 121, 90–103. https://doi.org/10.1016/j.ress.2013.07.004

Greenwood, M., & Van Buren, H. J., III. (2010). Trust and Stakeholder Theory: Trustworthiness in the Organisation–Stakeholder relationship. Journal of Business Ethics, 95(3), 425–438. https://doi.org/10.1007/s10551-010-0414-4

Hannila, H., Kuula, S., Harkonen, J., & Haapasalo, H. (2020). Digitalisation of a company decision-making system: a concept for data-driven and fact-based product portfolio management. Journal of Decision System, 31(3), 258–279. https://doi.org/10.1080/12460125.2020.1829386

Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00345-2

Kalaitzi, D., & Tsolakis, N. (2022). Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage. International Journal of Production Economics, 248, 108466. https://doi.org/10.1016/j.ijpe.2022.108466

Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022, February 23). Leveraging random forests and gradient boosting for enhanced predictive analytics in operational efficiency. https://cognitivecomputingjournal.com/index.php/IJAIML-V1/article/view/72

Kamble, S. S., & Gunasekaran, A. (2019). Big data-driven supply chain performance measurement system: a review and framework for implementation. International Journal of Production Research, 58(1), 65–86. https://doi.org/10.1080/00207543.2019.1630770

Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced Customer Analytics: Strategic value through integration of Relationship-Oriented Big Data. Journal of Management Information Systems, 35(2), 540–574. https://doi.org/10.1080/07421222.2018.1451957

Kumar, N. (2022). IoT-Enabled Real-Time Data integration in ERP systems. International Journal of Scientific Research in Science Engineering and Technology, 393–410. https://doi.org/10.32628/ijsrset2215479

Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11). https://doi.org/10.1115/1.4047856

Lyytinen, N., & Rose, N. (2003). The disruptive nature of information technology innovations: the case of internet computing in systems development organizations. MIS Quarterly, 27(4), 557. https://doi.org/10.2307/30036549

Meng, J., & Berger, B. K. (2012). Measuring return on investment (ROI) of organizations’ internal communication efforts. Journal of Communication Management, 16(4), 332–354. https://doi.org/10.1108/13632541211278987

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big Data Analytics Capabilities and Innovation: The mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272–298. https://doi.org/10.1111/1467-8551.12343

Müller, O., Junglas, I., Brocke, J. V., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines. European Journal of Information Systems, 25(4), 289–302. https://doi.org/10.1057/ejis.2016.2

Mutula, S. M., & Van Brakel, P. (2007). ICT skills readiness for the emerging global digital economy among small businesses in developing countries. Library Hi Tech, 25(2), 231–245. https://doi.org/10.1108/07378830710754992

Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725. https://doi.org/10.1016/j.ipm.2021.102725

Onwuegbuzie, A. J., Slate, J. R., Leech, N. L., & Collins, K. M. (2009). Mixed data analysis: Advanced integration techniques. International Journal of Multiple Research Approaches, 3(1), 13–33. https://doi.org/10.5172/mra.455.3.1.13

Peng, Y., Zhang, Y., Tang, Y., & Li, S. (2010). An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decision Support Systems, 51(2), 316–327. https://doi.org/10.1016/j.dss.2010.11.025

Poritskiy, N., Oliveira, F., & Almeida, F. (2019). The benefits and challenges of general data protection regulation for the information technology sector. Digital Policy Regulation and Governance, 21(5), 510–524. https://doi.org/10.1108/dprg-05-2019-0039

Psarommatis, F., Danishvar, M., Mousavi, A., & Kiritsis, D. (2022). Cost-Based Decision Support System: A dynamic cost estimation of key performance indicators in manufacturing. IEEE Transactions on Engineering Management, 71, 702–714. https://doi.org/10.1109/tem.2021.3133619

Raj, P., Raman, A., Nagaraj, D., & Duggirala, S. (2015). High-Performance Big-Data Analytics. In Computer communications and networks. https://doi.org/10.1007/978-3-319-20744-5

Ramakrishnan, T., Khuntia, J., Kathuria, A., & Saldanha, T. J. (2019). An Integrated Model of Business Intelligence & Analytics Capabilities and Organizational Performance. Communications of the Association for Information Systems, 46(1), 722–750. https://doi.org/10.17705/1cais.04631

Reijers, H. A., & Van Der Aalst, W. M. (2005). The effectiveness of workflow management systems: Predictions and lessons learned. International Journal of Information Management, 25(5), 458–472. https://doi.org/10.1016/j.ijinfomgt.2005.06.008

Sarker, I. H. (2021). Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Computer Science, 2(5). https://doi.org/10.1007/s42979-021-00765-8

Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2016). How does business analytics contribute to business value? Information Systems Journal, 27(3), 237–269. https://doi.org/10.1111/isj.12101

Seggie, S. H., Cavusgil, E., & Phelan, S. E. (2007). Measurement of return on marketing investment: A conceptual framework and the future of marketing metrics. Industrial Marketing Management, 36(6), 834–841. https://doi.org/10.1016/j.indmarman.2006.11.001

Sharma, A. K., Sharma, D. M., Purohit, N., Rout, S. K., & Sharma, S. A. (2021b). Analytics techniques: descriptive analytics, predictive analytics, and prescriptive analytics. In EAI/Springer Innovations in Communication and Computing (pp. 1–14). https://doi.org/10.1007/978-3-030-82763-2_1

Shen, Y., Chen, P., & Wang, C. (2015). A study of enterprise resource planning (ERP) system performance measurement using the quantitative balanced scorecard approach. Computers in Industry, 75, 127–139. https://doi.org/10.1016/j.compind.2015.05.006

Someh, I., Shanks, G., & Davern, M. (2019). Reconceptualizing synergy to explain the value of business analytics systems. Journal of Information Technology, 34(4), 371–391. https://doi.org/10.1177/0268396218816210

Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023). Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review. Energies, 16(10), 4025. https://doi.org/10.3390/en16104025

Van De Wetering, R., Mikalef, P., & Helms, R. (2017). Driving organizational sustainability-oriented innovation capabilities: a complex adaptive systems perspective. Current Opinion in Environmental Sustainability, 28, 71–79. https://doi.org/10.1016/j.cosust.2017.08.006

Vassakis, K., Petrakis, E., & Kopanakis, I. (2017). Big Data Analytics: applications, prospects and challenges. In Lecture notes on data engineering and communications technologies (pp. 3–20). https://doi.org/10.1007/978-3-319-67925-9_1

Vudugula, S., Chebrolu, S. K., Bhuiyan, M., & Rozony, F. Z. (2023). INTEGRATING ARTIFICIAL INTELLIGENCE IN STRATEGIC BUSINESS DECISION-MAKING: A SYSTEMATIC REVIEW OF PREDICTIVE MODELS. International Journal of Scientific Interdisciplinary Research, 04(01), 01–26. https://doi.org/10.63125/s5skge53

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014

Wang, Y., & Byrd, T. A. (2017). Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management, 21(3), 517–539. https://doi.org/10.1108/jkm-08-2015-0301

Yang, Y., Chen, N., & Chen, H. (2023). The digital platform, enterprise digital transformation, and enterprise performance of Cross-Border E-Commerce—From the perspective of digital transformation and data elements. Journal of Theoretical and Applied Electronic Commerce Research, 18(2), 777–794. https://doi.org/10.3390/jtaer18020040

Zhang, C., Wang, X., Cui, A. P., & Han, S. (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management, 91, 483–494. https://doi.org/10.1016/j.indmarman.2020.10.012


View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
37
View
0
Share