Inter-organizations Technology Systems’ Data Analytics, Corridor Performance Monitoring Using the Northern Transport Corridor Observatory

Abstract

Over the past recent years, institutions have adopted various mechanisms for measuring operational performance in their areas of operations with the key objective of improving efficiency and gaining competitive advantage. The important role Information technology data analytics play in measuring the efficiencies is unquestionable. This has made organizations to adopt tools and techniques in Innovation technology for measuring efficiencies. The evaluation of the effectiveness of Information technology systems’ data analytics in measuring corridor performance was the key objective of this study with the case study being the Transport Observatory project of the NCTTCA. The research also looked into the effectiveness of the adopted transport observatory of northern corridor in measuring corridor performance. The research design adopted was descriptive analysis using both primary and secondary data. Data collected through the structured questionnaires were analyzed using SPSS, Microsoft Excel and SQL. The study established that use of data analytics in measuring corridor performance is most effective and provide reliable information to decision makers and stakeholders. The findings give approval ratings of 79% of the respondents who believes that use of data analytics for measuring corridor performance is the most effective. The findings from secondary data indicated that the adopted transport observatory tool is quite effective in measuring corridor performance with the measure of corridor efficiency increasing from 92.6 (least efficient) in 2014 to 49.5 (more efficient) in 2017 indicating improvement in the corridor performance. However, the findings noted some of the challenges associated with use of data analytics and the adopted transport observatory as corridor monitoring tool. Notable challenges from the findings include; unreliability of data providers in sharing data, complexity of consolidating different data, availability of data gaps in the shared data, complexity of processes of data analysis. Inefficiency of dissemination tools and lack of awareness are other notable challenges identified. The research contributes more to the use of data analytics in measuring performance. With the ever changing and innovation of technology, the research finds that there is more valuable information generated through automation which can be used for measuring performance. The study recommends that more focus should be put on integrating related automated processes to assist in generating data for use in measuring corridor performance.