DATA SCIENCE CAPABILITIES AND BUSINESS INTELLIGENCE PERFORMANCE AMONG MANUFACTURING FIRMS IN SOUTH-SOUTH NIGERIA

The global manufacturing landscape is undergoing a profound transformation driven by the accelerating wave of digitalisation and the exponential growth of organisational data. Manufacturing firms now generate vast volumes of structured and unstructured data through production lines, sensors, supply chains, enterprise systems, and customer interactions, and the capacity to harness this data has become a defining feature of competitive firms (Ghobakhloo et al., 2024; Dataversity, 2024). The worldwide big data analytics market, valued at an estimated US$320.7 billion in 2025 and projected to reach US$842.6 billion by 2033 at a compound annual growth rate of about 12.9 percent, illustrates the scale of investment now directed at turning data into value (SNS Insider, 2025). Yet the accumulation of data confers no advantage in itself; value emerges only when firms possess the capabilities to collect, govern, analyse, interpret, and act upon data in ways that improve decisions and outcomes (Gupta & George, 2016; Mikalef et al., 2019). Manufacturing firms in emerging economies increasingly operate in data-rich environments, yet many struggle to convert their data into actionable intelligence that improves decision quality and competitiveness. This study examines the relationship between data science capabilities and business intelligence performance among manufacturing firms in the South-South geopolitical zone of Nigeria. Anchored on the Resource-Based View and the Dynamic Capabilities perspective, the study conceptualises data science capability through two focal dimensions, analytical talent capability and data-driven decision culture, and investigates their influence on business intelligence performance. A quantitative, cross-sectional survey design was adopted. Structured questionnaires were administered to 384 managers, analysts, and information technology personnel drawn from 96 registered manufacturing firms across the six South-South states; 351 valid responses were retained, representing a usable response rate of 91.4 percent. Data were analysed using performance percentage analysis for the research questions, and descriptive statistics, Pearson product-moment correlation, and multiple regression analysis for the hypotheses. Results indicate that respondents rated analytical talent capability lowest (mean = 3.41) while perceiving moderately strong business intelligence performance (mean = 3.62). Both analytical talent capability (r = 0.704; β = 0.342, p < 0.001) and data-driven decision culture (r = 0.689; β = 0.298, p < 0.001) were positively and significantly related to business intelligence performance, jointly explaining about 58.9 percent of its variance. The study concludes that the human and cultural dimensions of data science capability are decisive for business intelligence outcomes in resource-constrained manufacturing contexts, and recommends prioritised investment in analytical talent and the deliberate cultivation of evidence-based decision norms.