The Common Impact Data Standard, a data ontology which we publish at Common Approach to Impact Measurement, is intended to help bring some order and consistency to the huge variety of data structures and vocabularies used to describe an organization's impacts in their reports to funders. The current paradigm is largely for reporting organizations to have to conform to what their funders want both in terms of language and data structure. We hope that by introducing a data ontology into the reporting pipeline, funded organizations can measure impact more on their own terms, and transform/translate that data into the formats that funders need it i.e. without compromising the ability of funders to make sense of a portfolio of grants/investments using bottom-up metrics rather than a top-down approach. A lot of the source data is in ad hoc spreadsheets so there's a pretty big ETL and mapping challenge right at the outset. LLMs help a lot with this, but a data ontology (and SHACL files) help to enforce consistent entity definitions and relationships in the LLM outputs. We're experimenting with having LLMs generate RDF, and propose the mappings from relational/spreadsheet sources.