Missing data is quite common in the industrial field, resulting in problems in downstream applications such as soft sensing and anomaly detection, as most data driven methods used in these applications rely on complete and high-quality dataset to build a high-quality model. Current data imputation methods hardly take its following applications like soft sensing into consideration. A considerable challenge is how to refine missing data repair according to its downstream application. In this paper, we propose an imputation generative adversarial network with soft sensor(SSIGAN) which considers the loss of soft sensors as data. Compared with the imputation generative adversarial network(GAIN), the proposed SSIGAN model introduces the influence of data imputation on the soft sensor. The temporary soft sensor model gives guidance for better repair of quality-related variables. Thus, “customized” data imputation can be achieved for building a more accurate industrial soft sensor. An experiment of soft sensing of end-point composition in a steel-making process is conducted and verifies the improvement of data imputation of quality-related variables and that of the soft sensor with the proposed data imputation model.