Authors:
Sa Kaung Min Htet, Tun Zar Ni Aung, Hlaing Htake Khaung Tin
Addresses:
Faculty of Information Science, University of Information Technology, Yangon, Myanmar.
Abstract:
Organisations operating in the modern digital economy collect enormous amounts of data, both structured and unstructured, from user-generated content, activity on social media platforms, and interactions that take place online. When it comes to the latter, the objective is to extract meaning from unstructured data such as customer reviews, social media posts, and customer comments. On the other hand, the former emphasises structured behavioural indicators, such as page views, clickstreams, and conversion rates. Despite the merits of both lines of inquiry, there is a lack of understanding of how they interact to generate comprehensive business intelligence. A literature review, case-based examples, and a conceptual framework are all components of the comparative and integrative methodology developed for this study. The purpose of this technique is to investigate the various interactions that may occur between concurrent online and text analytics programs. By enabling consumer profiling, service customisation, predictive capabilities, and real-time responsiveness, the two approaches can be combined to improve decision-making. Quantitative behaviour and qualitative sentiment are integrated to achieve this. This paper proposes an organisational approach to turning data into actionable intelligence, improving competitive advantage, and influencing next-generation decision-support systems by connecting structured and unstructured analytics.
Keywords: Web Analytics; Text Analytics; Business Intelligence; Data Integration; Decision Support; Predictive Analytics; Online Platforms; Analytical Methods; Data Architectures; Unstructured Data.
Received: 04/09/2024, Revised: 20/10/2024, Accepted: 20/12/2024, Published: 05/09/2025
DOI: 10.64091/ATICL.2025.000229
AVE Trends in Intelligent Computer Letters, 2025 Vol. 1 No. 3 , Pages: 153-160