IT projects notoriously exceed initial time and budget estimates. Accurately estimating these large, complex projects is a challenging task with many unknowns and assumptions. Nevertheless, big-dollar business decisions are made based on IT project estimations, so it is critical to get it right.
In fact, here are some stats showing exactly how much of a problem it is.
Typical large projects have cost overruns between 560% and 1,900% (Satista).
The Harvard Business Review performed an in-depth analysis of an SAP system migration project for Levi Strauss. Initial estimates put costs at $5 million, but the project ultimately topped $192.5 million. That is 3,750% in the wrong direction.
“When we broke down the projects’ cost overruns, what we found surprised us. The average overrun was 27%—but that figure masks a far more alarming one. Graphing the projects’ budget overruns reveals a “fat tail”—a large number of gigantic overages. Fully one in six of the projects we studied was a black swan, with a cost overrun of 200%, on average, and a schedule overrun of almost 70%.” Harvard Business Review
Is machine learning the technology that can finally provide the solution?
Absolutely, and we are doing it now.
Here, you can download the “Machine Learning to Accurately Estimate IT Project Time and Budget” white paper. It will walk you through the concepts involved in implementing machine learning into your standard project processes with a goal of achieving more than a 500% improvement in time and cost estimation accuracy.
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