Complex IT project management information system

Abstract

The article is devoted to the development of the concept and architecture of an information system for managing complex IT projects, which provides automated detection, analysis and reduction of uncertainty in conditions of high complexity of project implementation. It is shown that modern IT projects are characterized by a high level of dynamics, technological novelty, multidisciplinary nature and a large number of interconnected elements. In such projects, complexity is manifested not only in the scale or number of components, but primarily in the interdependence of technical, organizational and behavioral factors. This complicates the prediction of results and reduces the effectiveness of classical management methods. The problem is formulated – the lack of a systematic approach to managing uncertainty, which arises as a result of the complexity of implementing IT projects and directly affects their success. It is shown that complexity should be considered as the complexity of implementation, which covers technological, organizational, communication, time and behavioral aspects of the project. They form a source of uncertainty, which makes it impossible to predict results stably and creates risks of missing deadlines or not meeting customer expectations. To systematically overcome this problem, a method for managing complex IT projects has been proposed, based on the idea of sequentially reducing uncertainty. The method is implemented in the form of stages: identifying areas of uncertainty, forming hypotheses, testing their effectiveness through Spike, PoC or R&D experiments, further refactoring of management processes and updating the project plan. The structure of an information system for managing complex IT projects has been developed, which includes nine modules and provides full automation of the management cycle. The system uses natural language processing tools, multi-criteria analysis, machine learning and predictive analytics to form management decisions based on data. A logical architecture with a closed adaptive loop has been implemented, which ensures continuous improvement of management actions and stabilization of the project state. The implementation of the proposed system will minimize the impact of the human factor, increase predictability and ensure a controlled reduction in uncertainty, which is critically important for the successful completion of complex IT projects.