Efficient Use of KPIs in Data Science Projects
Project controlling is essential in data science projects, as the time required and costs are often difficult to estimate.
Clients want to implement their use cases efficiently and successfully with machine learning methods. You can positively influence consistent project control with meaningful key performance indicators (KPIs). According to some studies, 85% of all data science projects fail, which requires early identification of obstacles.
In classical IT projects, many KPIs exist for project controlling, but these are not sufficient for data science projects. For this reason, in this article we present basic KPIs for IT projects and analyse them with regard to data science projects. As a result, we will see that specific KPIs can make the project controlling of data science projects more transparent.
The article first deals with the basics of project controlling and presents some KPIs from the software industry. Next, we use the presented KPIs and analyse them regarding data science projects using the process model CRISP-DM (CRoss-Industry Standard Process for Data Mining). The analysis also includes personal project experiences from data science projects.
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