dataarchaeology

Dataarchaeology is the interdisciplinary study and reconstruction of historical data ecosystems. It combines archival science, digital preservation, data governance, and information forensics to recover, interpret, and contextualize data that survives in obsolete formats, undocumented datasets, or legacy computing environments. Practitioners aim to illuminate how data were created, stored, transformed, and used, enabling long-term accessibility and understanding of past technologies and decision-making.

Methods include data recovery from obsolete media, format migration, schema inference, and reverse engineering of databases and file formats. Analysts extract provenance, metadata, and lineage information, often building provenance graphs and ontologies. They may use digital forensics tools, pattern analysis, and manual reconstruction to approximate original data models when documentation is missing. They also engage with archivists, historians, and software engineers to validate interpretations.

Applications span digital preservation, historical research, compliance audits, and forensic investigations into software or system behavior. Dataarchaeology can reveal how datasets evolved, reveal biases or errors introduced over time, and inform strategies for preserving data integrity in future systems.

Challenges include data loss, degradation, undocumented schema changes, proprietary formats, privacy concerns, and the scale of heterogeneous data. Ethical considerations address consent, confidentiality, and the potential misinterpretation of historical data.

As a field, it complements digital archaeology, archival science, and data curation. Advances in automated format identification, provenance capture (for example PROV), and AI-assisted reconstruction are expected to expand scope and efficiency.