APPLICATION OF THE KPI-ANALYTICS METHOD WITH ELEMENTS OF COMPARATIVE ANALYSIS AS A TOOL FOR ASSESSING THE IMPROVEMENT OF THE EFFICIENCY OF IT PROJECT MANAGEMENT
DOI:
https://doi.org/10.31891/2219-9365-2025-81-59Keywords:
IT projects, KPI analytics, benchmarking, model, implementation, testing, development, challengesAbstract
The article investigates the methodological foundations of using KPI analytics in combination with benchmarking as a tool for assessing the improvement of IT project management efficiency. The expediency of using key performance indicators (KPIs) to measure results at different stages of the project life cycle - from planning to completion and support - is substantiated.
A phased model for the implementation of the KPI methodology is proposed, which includes: the formation of a baseline based on historical data; identification and implementation of optimization measures (transition to Agile/Scrum, CI/CD automation, etc.); re-measurement of key metrics; comparative analysis with internal (internal benchmarking) and external industry benchmarks (external benchmarking).
An example of the practical application of the methodology in an IT company specializing in business process automation - European Regional Agency LLC - is presented. Implementation of KPI-analytics allowed to: reduce the average time of tasks execution by 42.6%; reduce the number of defects in the code by 52.2%; increase customer satisfaction by 16.7 percentage points; improve compliance with deadlines within sprints by 25 percentage points; increase release stability to 96%.
The analytical dashboards created on the basis of the collected KPIs provided visualization of processes, which allows to quickly identify deviations and make informed management decisions.
The article concludes that an integrated approach to performance evaluation based on measurable, comparable, and strategically oriented indicators is beneficial. Such an approach contributes to increasing management transparency, creating a culture of continuous improvement, and creating a sustainable analytical basis for data-driven management in IT projects.
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