Data Mapper ™

AI-powered EDC to SDTM and ADaM mapping uses machine learning over protocol, CRF, SAP, and study metadata to propose how each raw EDC field maps into CDISC SDTM domains, and how approved SDTM maps into ADaM analysis datasets. Clymb Clinical's Data Mapper, part of Clymbr Hub, generates those mapping specifications, keeps them in a governed Metadata Repository, and auto-generates the SAS or R code that implements them, with a statistical programmer reviewing and approving every mapping.

Data Mapper converts raw clinical data from any EDC system into SDTM domains, then maps SDTM into ADaM analysis datasets. Mappings are stored as metadata in a central repository, and the tool auto-generates the SAS or R code, define.xml, and annotated CRF that implement them.

Data Mapper is EDC-agnostic. It ingests raw extracts from Medidata Rave, Veeva CDMS, Oracle Clinical/InForm, OpenClinica, and other systems, along with central lab and external vendor feeds, in formats such as SAS datasets, CSV, and Excel.

Teams using Data Mapper report 50-60% less manual mapping time and 30-50% faster SDTM and ADaM dataset delivery, because AI drafts the mapping specifications and generates production-ready code instead of programmers writing both by hand.

Yes. Data Mapper is CDISC-aligned across CDASH, SDTM, ADaM, Controlled Terminology, and define.xml. Sponsor standards are versioned centrally, can be pinned per study, and reused across a compound or program, with CDISC conformance checks built in.

Yes. Every AI suggestion is human-in-the-loop: a programmer reviews and approves each mapping. Data Mapper runs in a 21 CFR Part 11 aligned statistical computing environment with version control, audit trails, and role-based access, and every ADaM variable traces back through SDTM to the source CRF field.

Yes. Generated SAS and R programs, mapping specifications (Excel, CSV, JSON), and define.xml can be exported, versioned in the SCE, and executed inside Clymbr Hub or in your existing environment.