Insights
Product, design, AI, and engineering perspectives from our team.
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.
Quantifying Technical Debt: A Business Case Your Board Approves
Boards fund reduced risk, protected revenue, and faster time to market — not "code quality." This article shows how to translate technical debt into cost of delay, risk exposure, and opportunity cost, with a board-ready register template.
SRE for Scale-Ups: Reducing Incidents Without a Team
Reliability incidents rising as you scale don't require a dedicated SRE team to fix — they require the right practices. Here's how CTOs can adopt error budgets, incident response, and on-call rotations with existing engineers.
Board-Level AI Literacy: A Director's Guide
Board-level AI literacy doesn't require directors to understand machine learning — it requires knowing what questions to ask. This guide sets out a practical framework for AI oversight grounded in existing directors' duties.
Data Mesh vs Centralised Data Platforms for Mid-Market Growth
Choosing between data mesh and a centralised data platform is an organisational decision as much as a technical one. This guide walks Australian scale-up leaders through team structure, governance overhead, and when each pattern fits.
AI for Insurance and Insurtech: A Practical Guide
A practical look at where AI genuinely fits in insurance and insurtech — underwriting support, claims triage, and fraud detection — and the data quality and explainability obligations Australian insurers need to get right first.
Building an Experimentation Platform for Product Teams
Moving from ad hoc A/B tests to a real experimentation platform means getting event tracking, statistical rigour, and feature-flagging right — together. Here's what that infrastructure actually requires.