DATA STORYTELLING IN INTELLIGENT ANALYTICS SYSTEMS: FROM VISUALIZATION TO DECISION INTELLIGENCE

Abstract

Abstract

The widespread organizational adoption of business intelligence and analytics systems has not resolved a persistent structural problem: the gap between the availability of analytical information and its effective use in decision-making. Research documents that information overload, insufficient narrative context, and poor communicative design in conventional dashboards systematically prevent decision-makers from deriving actionable insights from available data, with consequences measurable in decision quality, cognitive load, and organizational analytics effectiveness. This article examines data storytelling — the structured integration of data analysis, visual representation, and narrative architecture to convey analytical insights in a form that supports comprehension, interpretation, and organizational action — as a theoretical and practical response to this problem. The study is positioned as an analytical-conceptual inquiry adopting an interpretivist-pragmatist epistemological orientation. It draws on an analytically structured five-stage pipeline applied to literature identified across ACM Digital Library, IEEE Xplore, Web of Science, and Scopus, covering the period from 2000 to early 2026. The theoretical framework integrates Cognitive Load Theory, Dual Process Theory, Sensemaking Theory, and Situated Cognition Theory to explain the cognitive and organizational mechanisms through which narrative structures enhance analytical communication — and to identify the theoretical tensions between these frameworks that define the boundary conditions of data storytelling's effectiveness. The article presents four paradigmatic case studies — covering industrial analytics, public health dashboard failures during the COVID-19 pandemic, marketing analytics composite performance measurement, and documented instances of narrative-induced misinterpretation — that ground the theoretical propositions in empirically documented outcomes. A five-layer Decision-Oriented Data Storytelling Framework is proposed and formally evaluated, comprising Data Foundation, Analytical Processing, Visual Abstraction, Narrative Structuring, and Decision Activation layers, each grounded in peer-reviewed empirical literature and associated with explicit evaluation criteria, criterion-evidence mappings, and boundary conditions. The framework is explicitly distinguished from existing models in the field — including the Segel and Heer narrative visualization spectrum, the S-DIKW framework, and the three-stage authoring model — and its contributions are positioned in relation to those frameworks. The article further examines the organizational, ethical, and governance implications of emerging technologies — including automated natural language generation, conversational business intelligence systems, AI copilots for analytics, and large language model-based narrative generation — and identifies the principal unresolved challenges these technologies pose for narrative integrity, decision accountability, and responsible organizational deployment. The findings support the conclusion that the transformation of dashboards from passive monitoring tools into active decision-support systems requires deliberate narrative design grounded in cognitive theory, empirical evidence, and explicit governance frameworks — and that the effectiveness of data storytelling is situationally contingent rather than universally guaranteed, varying systematically with organizational context, task complexity, audience literacy, and the integrity of the narrative structures employed.

Keywords: data storytelling; narrative visualization; decision support systems; business intelligence; cognitive load theory; dashboard design; analytical communication; sensemaking; information overload; AI-assisted analytics; natural language generation; decision intelligence; visualization rhetoric; organizational analytics; data literacy