Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process

This study aims to fill a gap in the literature by identifying, defining, and evaluating the critical success factors that impact the implementation of data intelligence in the public sector. Fourteen factors were identified, and then divided into three categories: organization, process, and technology. We used the analytical hierarchy process, a quantitative method of decision-making, to evaluate the importance of the factors presented in the study using data collected from nine experts. The results showed that technology, as a category, is the most important. The analysis also indicated that project management, information systems & data, and data quality are the most important factors among all fourteen critical success factors. We discuss the implications of the analysis for practitioners and researchers in the paper.

Automated summary

This study identified and evaluated fourteen critical success factors for implementing data intelligence in the public sector, categorizing them into organization, process, and technology. Using the analytical hierarchy process, the study found that technology is the most important category, with project management, information systems & data, and data quality being the most crucial factors among all fourteen. The implications of the analysis for practitioners and researchers are discussed in the paper.