While data driven operations have become more and more accessible to small and medium enterprise over the past years, that same ease of access has left a substantial number of organisations with stored personal data, serious privacy obligations and limited resources to keep up with the changeable privacy landscape.

High-quality data is vital for training any machine learning system. Data readiness is an ongoing process. Let us explain.

When we interact with other people and organisations in the world, we reveal information like our names, what we look like, where we’re from, or other, more specific information. This is our personally identifiable information. The idea of privacy concerns all aspects of the collection, storage, dissemination and use of that information.

An AI agent is only as good as the data behind it. Data is the lifeblood that flows through all forms of automation, and that includes AI.

Australia’s financial system needs strong data governance to buttress against cybersecurity threats. It has escaped nobody’s notice that banking — for individuals as well as businesses — is increasingly digital.

Data cleansing, or data cleaning, is the process of finding and removing problems with the data in a database. Those problems include incorrect, corrupt, duplicated, incomplete or outdated data.