A Conceptual Model for Leveraging Business Intelligence Tools to Improve Financial Forecasting Accuracy
Abstract
Financial forecasting accuracy is fundamental to organisational sustainability, competitive strategy, and effective resource allocation. Despite advancements in analytics, many organisations continue to face forecasting challenges driven by volatile markets, fragmented data environments, and lack of integrated decision-support systems. Business Intelligence (BI) tools such as dashboards, data warehousing, OLAP systems, and predictive analytics offer opportunities to transform data into actionable insights, yet the link between BI capability and improved forecasting accuracy remains conceptually underdeveloped. This paper develops a pre-2019 conceptual model that explains how BI tools enhance financial forecasting accuracy by integrating data quality, analytical capability, organisational decision processes, and technological infrastructure. Drawing from literature published up to 2018, the model positions BI-enabled forecasting as an iterative, data-intensive process supported by information integration, analytical sophistication, and organisational learning. The paper provides theoretical grounding for future empirical validation and insights for practitioners seeking to leverage BI tools for more reliable financial forecasting.
Keywords
Business Intelligence; Financial Forecasting; Predictive Analytics; Data Warehousing; Organizational Decision Support; Forecast Accuracy
Citations
IRE Journals:
Michael Uzoma Agu, Olawole Akomolafe "A Conceptual Model for Leveraging Business Intelligence Tools to Improve Financial Forecasting Accuracy" Iconic Research And Engineering Journals Volume 3 Issue 1 2019 Page 596-613
IEEE:
Michael Uzoma Agu, Olawole Akomolafe
"A Conceptual Model for Leveraging Business Intelligence Tools to Improve Financial Forecasting Accuracy" Iconic Research And Engineering Journals, 3(1)
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