What piece of knowledge am I missing here to use this dataset?
By anonymising the data and altering the attribute names, the providers of this dataset have made it into an abstract machine learning exercise, where data has similar qualities to real-world credit approval data, so that approaches to train models that work well on it are likely to work well in a real-world scenario. However, those models could not be used in a production system. You are not in a position to train a model based on it and then input real world data you have collected elsewhere to make a prediction.
You can still train a model on this data to predict A16 (the class attribute). You can measure the accuracy - or any other metric - by holding out a test set. You can experiment with feature engineering, feature selection etc, in an abstract way without being able to apply much domain knowledge. You can try out different model classes, different hyper-parameters, different approaches to imputing missing values or cross-validation etc.
Your project can draw conclusions about the approach you have taken, based on test results. What you cannot do is explore real-world scenarios with simulated customer data. This might make it less compelling as a demonstration - for instance users could not explore exactly what factors might lead to an application being approved or not, instead you would just have to show some graphs that demonstrate you have selected a model with good performance.