Post-Custodialism, Distributed Custody, and Big Data

James Doig

doi:10.37683/asa.v52.10985

Post-Custodialism, Distributed Custody, and Big Data

James Doig Digital Preservation Manager, National Archives of Australia, Canberra, Australian Capital Territory

DOI: https://doi.org/10.37683/asa.v52.10985

Keywords: Distributed custody, Post-custodialism, Digital preservation, Big data

Abstract

This reflection piece describes the outcomes of a research project undertaken by the National Archives of Australia that aimed to gather information from other government archives and selected Australian government agencies about their approach to archiving and preserving large-big datasets in the government sector. Big data collections pose a challenge for government archives around the world. Many of these archives have a role in information management in their government domains and provide guidance and advice to their government agency clients on ensuring the integrity and trustworthiness of data over time. The article examines the nexus between theory and practice, exploring issues related to the post-custodial ideas developed by Terry Cook and others in the 1990s and their practical implementation.

Author Biography

James Doig, Digital Preservation Manager, National Archives of Australia, Canberra, Australian Capital Territory

Dr. James Doig has worked at the NAA for more than 20 years. In that time, he has worked in many roles in collection management, including digital preservation, transfer, description, and collection review. He has presented regularly at conferences such as Australian Society of Archivists (ASA) and Records and Information Management Practitioners Alliance (RIMPA) and has published articles in Archives & Manuscripts, American Archivist, and Script & Print. He is on the Research and Practice subcommittee of the Digital Preservation Coalition. He has a PhD in medieval history from Swansea University.

Published

2024-11-29

How to Cite

Doig J. (2024) “Post-Custodialism, Distributed Custody, and Big Data”, Archives & Manuscripts, 52(1), pp. 79-85. doi: 10.37683/asa.v52.10985.

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Issue

Vol 52 (2024)

Section

Reflection articles

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