Research on data governance for AI adoption, including data readiness, data quality frameworks and privacy-compliant AI data practices.
Data governance for AI encompasses the policies, standards, processes and accountability structures that ensure the data used to train, run and evaluate AI systems is fit for purpose, ethically sourced, legally compliant and of sufficient quality to support AI decisions. It is a prerequisite for responsible AI — systems trained on poor-quality, biased or improperly handled data will produce unreliable, unfair or legally non-compliant outcomes regardless of model sophistication. AI-specific data governance must address the full data lifecycle from a model training perspective: data collection and consent, cleaning and labelling, feature engineering, training set composition, validation set integrity and ongoing monitoring of data drift.
Data readiness is one of the most common AI adoption barriers in Australian organisations. Many organisations discover, partway through an AI initiative, that their data is insufficiently structured, insufficiently labelled, held in incompatible systems or subject to privacy constraints that prevent the use required for the intended application. Strategen AI's data governance research examines the conditions for data readiness — what organisations must have in place before AI adoption can succeed — and the investment required to address data gaps in practice. Australia's Privacy Act 1988 and the Australian Privacy Principles create specific obligations that affect training data composition, consent requirements and explainability obligations for AI-driven decisions.
In the APIG framework, data governance sits primarily within the Infrastructure dimension — the systems, architecture and data assets that AI depends on. However, data governance also has critical Governance implications: privacy compliance, consent management and data access controls all require policy and accountability structures. Research in this hub provides the empirical foundation for assessing and improving Infrastructure readiness, and for building the data governance policies that satisfy both technical and regulatory requirements.
Data governance succeeds when treated as a strategic organisational challenge built on culture, processes, and accountability.
Category: Data Governance & Organisational Leadership | Year: