About the Project
How can we improve information architectures to support more effective data discovery and the responsible sharing of data? What methodologies can best support the effective exchange of information inherent in enterprise information architecture? Sharing mission critical, trusted data between organisations is a significant business challenge.
This project will tackle the challenge by investigating how organisations can leverage human & AI/ML approaches to improve the forensic discovery and analysis of organisational data across multiple and complex information architectures. Working with partner Astral Consulting, the benefits of this research will include improved resilience through improved data curation and discovery with a focus beyond search, towards provenance, trust, custody, traceability and the governance of information.
About the Candidate
PhD candidates should have a background in Information Systems, or Enterprise Information Management with a computing science or similar technology background preferred, but not essential. A strong interest in technology and the business value of advanced data analytics, AI and machine learning is an advantage. Previous experience in business analysis, enterprise architecture and/or working with metadata, taxonomies and Information Architecture is beneficial, but not essential.