Business Intelligence and Operational Performance in the Kenyan Banking Sector

Abstract

This study paper covered business intelligence and operational performance of banks in Kenya. Some of the objectives included finding out business intelligence tools being utilized in the sector, assessing the relationship9between business intelligence and operational9performance, and identifying the challenges facing the successful integration of business intelligence in the banks’ processes. A descriptive survey design was utilized and targeted a population of 38 banks, and utilizing primary and secondary data sources. Primary data was obtained from questionnaire responses while secondary data was gotten from bank sector reports on the topic then analyzed using both descriptive statistics and regression analysis. The research outcomes demonstrated that business intelligence had a significant and moderate impact on the operational performance of banks in Kenya. It was shown by R2 value of 0.473 and an F test p-value of 0.001 which fall below 5% significance level. Commonly utilized business intelligence tools identified as data mining, data warehousing, ETL and OLAP positively affected operational performance aspects enlisted as quality of services, flexibility of operations, timeliness, and cost savings. Challenges enlisted in the study included rigidity of organizational management in switching to newer technologies, lack of required finances to acquire needed tools, lack of internal expertise of handling the technologies, and incompatibility of internal structures with some of the newer technologies. Given that technologies changed so fast in the current era, some commercial banks with limited financial resources found it hard to set aside budgets to advance their technologies. Other challenges included strict regulations on operations that limited usage of some business intelligence tools and the lack of desired business intelligence tools within the local banking space. This study concluded that business intelligence was an important part of operations that significantly impacted on operational performance of commercial banks through improving access to wide volumes of data, enabling storage of the data safely within businesses, appropriate classification of data, and extraction of needed data any time for key decision making that would improve quality of service provision, flexibility in operations, timeliness, and cost savings