What is it about?
Imagine you start a tech project and there's a 50% chance it will fail — that's like flipping a coin and hoping for the best. This is a reality many in the IT world face. Our study used computers (machine learning) to analyze massive amounts of data to see if there are telltale signs that predict a project's success or failure. We found seven clues from this data that can predict with nearly 80% accuracy if a project will fail or succeed. This is a big leap from the 50-50 coin flip! Our findings provide a toolkit for IT professionals to hopefully increase their odds of success.
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Why is it important?
Many studies have tried to pinpoint why IT projects fail, but their findings often had little practical impact. By using advanced computer techniques to comb through vast data, our research offers a much more accurate prediction model than previously available. In today's tech-driven world, where millions can be spent on a single project, having a tool that can provide a clearer picture of potential success is invaluable.
Perspectives
Working on this publication was both challenging and rewarding. We ventured into uncharted territories, blending machine learning with traditional research methods. It was a reminder that while technology is advancing rapidly, it can, and should, be used to enhance time-tested methodologies. The fact that we were able to provide a more accurate toolkit for IT professionals is a testament to the exciting crossroads where technology and research meet. This work has the potential to revolutionize how IT projects are approached and evaluated in the future.
Assoc. Prof. Narasimha Rao Vajjhala
University of New York at Tirana
Read the Original
This page is a summary of: Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods, International Journal of Information Technology Project Management, February 2023, IGI Global,
DOI: 10.4018/ijitpm.317221.
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