8% of companies experienced ML project failures in 2023, with poor data cleansing and lackluster cost-performance the primary causes
published on 2024/11/05
The top contributing factor to ML project failures in 2023 was insufficient budget (29%), which is consistent with many other findings throughout the report (Figure 6). But aside from the cost concerns, the other top contributing factors to project failures were poor data preparation (19%) and poor data cleansing (19%) – both of which are crucial to the success of ML projects, because they have a direct impact on the number of successful ML iterations that can be achieved within the available project budget. The more inefficient the data preparation and cleansing, the less opportunity there is to maximize the volume of iterations required to achieve optimal results.
This is an eye opening report.