XBRL is the established standard for business reporting used in 184 regulatory programs worldwide. Understanding XBRL is crucial for every data scientist, accountant, and financial analyst, offering significant career benefits to students as they prepare for future endeavors.
XBRL data is volumous, which is both a benefit, but also a hurdle for students who are newer to data extraction. You can overcome this hurdle by providing students with a highly structured case, like the two spotlight cases below.
Spotlight Case: XBRL Case Study: Are Big Companies Paying their Fair Share?
Authors: Christine Cheng (University of Mississippi) and Campbell Pryde (President and CEO, XBRL US)
In this case, students are asked to conduct an ETR analysis using both commercially available data and as-reported XBRL data. This case illustrates that the “normalization” process of commercial datasets – where data is structured according to standardized forms by data aggregators to enhance consistency – can lead to a loss of information, impacting their utility in certain types of data modeling. Extracting as-reported data directly from company financial reports would be more advantageous for ETR analysis, but historically has been labor-intensive and time-consuming. However, XBRL data allows for the extraction of as-reported financial statement data in machine-readable format at scale.This case serves as an excellent introduction for students to XBRL data and its value. Students will discover that as-reported data enables them to address more questions compared to commercial data. Moreover, students will learn about the consistency of underlying XBRL tags, which remain uniform across companies despite variations in company-assigned line item labels, ensuring ease of comparison. Furthermore, through inline XBRL (iXBRL) documents, students will gain insight into how to view and interact with metadata associated with each reported fact. Ultimately, this case equips students with knowledge of XBRL.
