Insights
Product, design, AI, and engineering perspectives from our team.
Do Growing Australian Companies Need a Chief AI Officer?
As AI initiatives scale beyond a single pilot team, growing Australian companies face a real organisational design question: create a dedicated Chief AI Officer role, or extend an existing CTO or Head of Data mandate? This piece looks at the trade-offs against the backdrop of Australia's active regulatory environment for AI and data.
Change Data Capture for AI-Ready Data Pipelines
Change data capture (CDC) keeps analytics, search, and AI features in sync with source systems in near real time — but it's not free. This explainer covers how CDC works, where it fits in an AI-ready data stack, and when the operational overhead is actually worth it for an Australian engineering team.
How to Build a Data Science Function From Scratch
A practical guide to standing up a data science function from zero — who your first hire should report to, whether to start with a data scientist or analytics engineer, and how to sequence early wins before scaling the team.
Feature Stores for Machine Learning at Scale
Growing data teams often outgrow spreadsheets and ad hoc pipelines long before they need a full feature store platform. Here's how to tell the difference — and how Google Cloud's Vertex AI implements feature management as a concrete example.
AI Services & Data Sovereignty for Regulated Australian Industries
A practical guide for fintech, healthtech, and insurance leaders on where AI training and inference data can legally live under the Privacy Act 1988 and APRA CPS 234 — plus architecture patterns for keeping sensitive data onshore while using cloud AI services.
Engineering Leadership Succession Planning During a CTO Exit
A CTO exit puts delivery velocity, architectural continuity, and team retention at risk if handled as a recruitment problem alone. Here's how to structure interim leadership, preserve institutional knowledge, and protect momentum during the transition.
CLV Modelling for SaaS and E-commerce Growth Teams
A practical guide for SaaS and e-commerce data leaders on building customer lifetime value models that inform retention and acquisition spend. Covers data requirements, modelling approaches, and common pitfalls for teams scaling past product-market fit.
Causal Inference for Business: Beyond Correlation in Analytics
Correlation-based dashboards can tell you what happened, but not what will happen if you change something. This article explains when that gap misleads resourcing and pricing decisions, and how A/B testing, quasi-experiments and uplift modelling give growing Australian companies more reliable answers.
Data Lineage and Cataloging: Trustworthy, Discoverable Data
Data lineage and cataloging are the unglamorous foundations that make AI and self-serve analytics trustworthy. This guide covers what to build, in what order, and how the Australian Privacy Principles shape the work.