Introducing the MAST Methodology - a new framework for developing metadata management capability

As more academic fields and government sectors experience the value of data, the need for increased data literacy and data librarianship has grown dramatically. However, the necessary skills for data and metadata management remain concentrated within specialised data management areas. Additionally, some novice data users from underrepresented communities may not have the access to tools, resources and networks to develop capability for data documentation. To meet the needs of the growing diversity of data users, new methods are required to support the democratisation of knowledge and capability to support data librarianship.

Based on research and experience with data governance across 15 Government departments and Academic organisations this talk introduces the MAST Methodology - an operational framework for building sustainable data culture.

Building on existing data governance frameworks, the MAST Methodology provides practitioners with 4 major principles - Metadata, Analysis, Support & Teamwork - to build skills and organisational support for data management. This is extended by examining how change management for data can deliver IDEAL metadata through the steps of Inventory, Documentation, Endorsement, Auditing and Leadership to develop a pragmatic approach to build a sustainable data culture.

In this poster, we will cover:

* Existing challenges in change management and education in data governance * The four principles of the MAST Methodology and how they improve data governance capability especially among novice practitioners and underrepresented fields * The role of specialist teams in developing a culture of peer review to support knowledge development and retention for operationalised data governance * A breakdown of how the IDEAL metadata framework rapidly improves data quality, along with low- or no-cost practical steps to support under-resourced communities Lastly, we discuss how the Aristotle Metadata Registry implements the MAST Methodology with examples demonstrating how these tools improve data quality and increase engagement with metadata.