Authors : Ram Niwas; Yogesh Deshmukh; Sneha Vadavi; Khushi Chaudhary

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2b9ck95f

Scribd : https://tinyurl.com/khnchb7x

DOI : https://doi.org/10.38124/ijisrt/26mar1502

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The exponential growth of digital data has created what may be termed informational chaos — a condition in which the volume, velocity, and variety of incoming data exceed the processing and interpretive capacity of conventional analytics systems. Organisations operating in high-demand data environments, particularly in domains such as e-commerce, finance, and healthcare, face mounting pressure to derive real-time, actionable insights from massive and continuously evolving datasets. Despite the proliferation of visualisation tools, the existing literature offers limited structured guidance on how to select and deploy these tools specifically within high-demand contexts, defined in this paper as environments characterised simultaneously by high data volume (millions or billions of records), high velocity (near real-time ingestion), high variety (heterogeneous data modalities), and elevated decision pressure. This study addresses that gap through three primary contributions. First, a taskoriented taxonomy is proposed, classifying high-demand visualisation activities into four operational categories: Monitoring, Alerting, Exploration, and Explanation. Second, a six-criterion scoring framework is introduced, evaluating tools across Scalability (C1), Latency (C2), Cognitive Load (C3), Literacy Support (C4), Domain-Adaptivity (C5), and Big-Data Integration (C6). Third, the framework is empirically grounded through a primary survey of thirty working IT and analytics professionals, supplemented by an illustrative case study comparing four leading tools — Tableau, Power BI, D3.js, and Plotly — across the e-commerce domain. Survey findings indicate that Power BI is the most widely adopted tool (50% most-frequent usage), with an overall mean effectiveness rating of 8.37 out of 10, while Tableau is preferred for exploratory and narrative visualisation tasks. The framework proposed herein offers a reproducible methodology for practitioners and researchers seeking toolselection guidance in data-intensive operational settings.

Keywords : Data Visualisation, High-Demand Data, Tool Selection Framework, Dashboard Design, Big Data Analytics, Visualisation Literacy, Cognitive Load, E-Commerce Analytics.

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