From Data to Decision Intelligence: Operationalizing Advanced Analytics for Strategic Decision-Making in Cloud-Based Contact Centers Centers

Arungopan Gopakumar

Abstract: Cloud-based contact centers have transitioned from simple call-handling systems into sophisticated digital engagement platforms that generate vast volumes of structured and unstructured data. These environments operate at high velocity and scale, making traditional decision-making approaches based on intuition, static dashboards, or retrospective reporting increasingly inadequate. Advanced data analytics offers organizations the capability to transform raw operational data into actionable intelligence that supports timely, consistent, and strategically aligned decisions. This paper examines how descriptive, predictive, and prescriptive analytics collectively enable decision intelligence in modern cloud-based contact centers. Using Amazon Connect as an applied industry context, the study integrates established analytics theory with cloud-native analytics architectures to propose a structured decision-intelligence framework. The paper analyzes current analytical capabilities, identifies systemic limitations and missed opportunities, and presents recommendations for advancing analytics maturity. The findings demonstrate that operationalizing advanced analytics enhances customer experience, improves operational efficiency, and strengthens long-term competitive advantage in cloud-native service ecosystems.

Keywords: Data Analytics, Business Analytics, Decision Intelligence, Business Intelligence, Cloud Computing, Cloud-Native Architectures, Contact Centers, Customer Experience Management, Big Data Analytics, Real-Time Analytics, Streaming Data Processing, Predictive Analytics, Prescriptive Analytics, Descriptive Analytics

How to Cite?: Arungopan Gopakumar, "From Data to Decision Intelligence: Operationalizing Advanced Analytics for Strategic Decision-Making in Cloud-Based Contact Centers Centers", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 772-775, https://www.ijsr.net/getabstract.php?paperid=SR26210051523, DOI: https://dx.dx.doi.org/10.21275/SR26210051523

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