Being promoted by major search engines such as Google, Yahoo!, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, deployed Microdata is most often not free from errors, which limits its practical use. In this paper, we use the WebDataCommons corpus of Microdata extracted from more than 250 million web pages for a quantitative analysis of common mistakes in Microdata provision. Since it is unrealistic that data providers will provide clean and correct data, we discuss a set of heuristics that can be applied on the data consumer side to fix many of those mistakes in a post-processing step. We apply those heuristics to provide an improved knowledge base constructed from the raw Microdata extraction.