Advancing Business Intelligence Adoption for Macroeconomic and Productivity Growth: An Econometric Assessment of Data Driven Decision Making in the U.S. Financial Sector
Abstract
It is an empirical study of the macroeconomic and operational effects of Business Intelligence (BI) implementation in the banking industry of the United States, in terms of productivity and GDP. The study connects a multi source quantitative model that integrates information from the US Bureau of Economic Analysis (BEA), Federal Reserve Economic Data (FRED), and 10-K filings with the Securities and Exchange Commission (SEC) to construct a longitudinal data set covering 2023 to 2025. The paper examines the relationship between the intensity of BI, as measured in actual terms by a new text analytics based BI Index, and economic consequences using log-linear econometric models, and finds that the two variables are related. The findings show that the relationship between the adoption of BI and the growth in sectoral GDP is strong (R2 = 0.999), and that increasing the strength of BI by 50% points results in a 1.6% increase in GDP, or a 16.6 billion dollar increase in economic activity. Nonetheless, the relationship between business intelligence and productivity has been slow and nonlinear, with short term inefficiencies caused by transitional changes as well as costs associated with technology integration. The findings support the Resource Based View (RBV) and Technology Performance Chain (TPC) models, demonstrating that Business Intelligence (BI) systems are advanced resources capable of transforming data resources into economic value through alignment with organizational strategy and the promotion of organizational learning. According to the research as a manager, competences such as analytics expertise, data management skills, and organizational readiness are key to realizing the benefits of business intelligence over time. The policy implications are that national digital policies and goals, such as the United States' AI and Data Strategy 2030, must incorporate the development of BI adoption indicators in order to support long term productivity gains and boost innovation competitiveness. This study addresses a gap in existing empirical data on the disconnect between a country's digital transformation efforts and its operational results in terms of overall performance by conducting the first multi source econometric analysis of BI in relation to the final performance criteria. It recognizes business intelligence as a vital economic driver that will put the United States at the forefront of the world's data driven economy.
