The life cycle activities of industrial software systems are often complex, and encompass a variety of tasks. Such tasks are supported by integrated
environments (IDEs) that allow for project data to be collected and analyzed. To date, most such analytics techniques are based on quantitative models to assess project features such as effort, cost and quality. In this paper, we propose a project data analytics framework where first, analytics objectives are represented as goal models with conditional contributions; second, goal models are transformed to rules that yield a Markov Logic Network (MLN) and third, goal models are assessed by an MLN probabilistic reasoner. This approach has been applied with promising results to a sizeable collection of software project data obtained by ISBSG repository, and can yield results even with incomplete or partial data.