Assessment of Business Intelligence Systems and Decision-Making Quality in Nigerian Manufacturing Conglomerates

Business intelligence systems are increasingly deployed by Nigerian manufacturing conglomerates to improve decision quality and competitive responsiveness, yet their professional assessment in this context remains limited. This study assessed the deployment and impact of business intelligence systems on decision-making quality in five major Nigerian manufacturing conglomerates: Dangote Industries, Flour Mills of Nigeria, BUA Group, UAC of Nigeria, and Nigerian Breweries, with operations spanning multiple geopolitical zones. A mixed-methods design combined structured interviews with 45 senior managers and data analysts with review of BI system implementation reports and financial performance data for the period 2020 to 2023. Decision-making quality was assessed by decision timeliness, forecast accuracy improvement, and strategic alignment of operational decisions. Results showed that companies with real-time BI dashboards integrated with enterprise resource planning systems demonstrated 27% higher forecast accuracy and 31% faster strategic decision cycles than those with batch-reporting BI systems. Data quality was identified as the primary BI effectiveness constraint by 72% of respondents, attributed to fragmented legacy data systems. Analytics literacy among senior management was rated as adequate in only 4 of 5 companies. BI adoption showed the strongest ROI in supply chain and production planning applications. The study concludes that BI system effectiveness in Nigerian manufacturing conglomerates depends more on data quality and management analytics literacy than on system sophistication alone, and recommends data governance frameworks as foundational investments.

Keywords: business intelligence, decision-making, manufacturing conglomerates, data quality, Nigeria