Datasets especially financial datasets can be hard to understand. Among many complexities, understanding the data schema is particularly important to be able to extract the data required correctly.This conference presentation calls to advocate and emphasise coding as a means to understand datasets, to be able to correctly interpret table structure and relevant fields to extract the correct data, to ensure accuracy and relevance.An example or two of how to work with WRDS data will be used to illustrate the importance of this premise in understanding dataset contents and relationships, to support data extraction and uphold data integrity to support learning and research.Using a case study on retrieving historical S&P 500 membership data from the WRDS database, this presentation illustrates how coding enhances comprehension of dataset intricacies and improves research or data extraction outcomes.The article "Notes and Thoughts on Retrieving Historical Members of S&P 500 from WRDS" provides a clear data learning path and a walkthrough for querying historical members of the S&P 500 using WRDS with Python code. The incremental approach taken helps learners understand both the coding process and dataset structure.Data extraction is only as true as its data comprehension, particularly in the domain of financial datasets.In the context librarians are increasingly seen as collaborators in research than just custodians of information and data, the role of librarians as active data interpreters through coding is highly valued.This presentation aims to inspire librarians to adopt coding as a tool not just for extraction but for critical engagement with datasets, underscoring the importance of computational literacy in bridging the gaps in dataset discovery, ensuring accurate, meaningful data retrieval for academic and professional endeavours.